From 0de875ce362d7758453264e17f37fcd6e20d37ab Mon Sep 17 00:00:00 2001 From: Tang Piseth Date: Mon, 12 Oct 2026 16:22:54 +1030 Subject: [PATCH 1/2] Add day-ahead price forecasting walk-forward backtest - forecasting/: AEMO NEMWeb SA1 history (2019-2026, 30-min AEST) cached to data/sa1_prices.parquet; naive, LightGBM and PyTorch LSTM day-ahead models; validation-then-frozen walk-forward test (365 days, retrain every 30 days) scored by feeding forecasts into the unchanged LP and settling at actual prices - results/: backtest.json, REPORT.md, typical-day chart, per-day JSON (LSTM 61.8%, LightGBM 49.5%, naive 27.2% of perfect-foresight revenue) - tests: leakage tests (features and fit+predict) and realised-profit tests - API: GET /api/v1/backtest/days and /api/v1/backtest/day/{date} - React: Backtest page (#/backtest) with synced price/SoC/profit charts, capture-rate bars and best/worst/spike day pickers; live page unchanged apart from a link - forecasting deps in an optional "forecast" extra (CPU torch); CI installs it Co-Authored-By: Claude Opus 5.5 --- .github/workflows/backend-tests.yml | 2 +- .gitignore | 4 +- README.md | 24 + data/sa1_prices.parquet | Bin 0 -> 2771602 bytes forecasting/__init__.py | 0 forecasting/backtest.py | 278 +++ forecasting/config.py | 30 + forecasting/data.py | 133 ++ forecasting/economics.py | 30 + forecasting/features.py | 86 + forecasting/models.py | 196 ++ forecasting/report.py | 161 ++ frontend/src/App.tsx | 1 + frontend/src/Backtest.tsx | 286 +++ frontend/src/Router.tsx | 14 + frontend/src/main.tsx | 4 +- frontend/src/types.ts | 58 +- main.py | 35 +- pyproject.toml | 20 +- results/REPORT.md | 87 + results/backtest.json | 328 ++++ results/daily/2025-10-12.json | 1 + results/daily/2025-10-13.json | 1 + results/daily/2025-10-14.json | 1 + results/daily/2025-10-15.json | 1 + results/daily/2025-10-16.json | 1 + results/daily/2025-10-17.json | 1 + results/daily/2025-10-18.json | 1 + results/daily/2025-10-19.json | 1 + 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results/daily/2026-10-06.json create mode 100644 results/daily/2026-10-07.json create mode 100644 results/daily/2026-10-08.json create mode 100644 results/daily/2026-10-09.json create mode 100644 results/daily/2026-10-10.json create mode 100644 results/daily/2026-10-11.json create mode 100644 results/daily/index.json create mode 100644 results/typical_day.png create mode 100644 results/validation.json create mode 100644 tests/test_forecasting.py diff --git a/.github/workflows/backend-tests.yml b/.github/workflows/backend-tests.yml index e7be352..ac1d5f4 100644 --- a/.github/workflows/backend-tests.yml +++ b/.github/workflows/backend-tests.yml @@ -32,4 +32,4 @@ jobs: env: # This securely pulls the encrypted key from GitHub Settings OPENELECTRICITY_API_KEY: ${{ secrets.OPENELECTRICITY_API_KEY }} - run: uv run pytest \ No newline at end of file + run: uv run --extra forecast pytest \ No newline at end of file diff --git a/.gitignore b/.gitignore index 789ba23..5972878 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,6 @@ /**/.pytest_cache .venv/ .env -*.svg \ No newline at end of file +*.svg +# forecasting: raw NEMWeb downloads are re-fetchable; the processed parquet is committed +data/raw/ diff --git a/README.md b/README.md index 6631199..bf5e36d 100644 --- a/README.md +++ b/README.md @@ -119,6 +119,30 @@ Live spot prices are fetched from AEMO via the Open Electricity API: Implementation: [main.py](main.py) - `run_live_simulation()` +## Day-Ahead Forecasting Backtest + +The live optimizer schedules against prices that have already happened (perfect foresight). The [forecasting/](forecasting/) package measures how much of that revenue a real operator could capture by scheduling on **forecast** prices instead. + +- **Data:** SA1 prices from AEMO NEMWeb from 2019 onwards, averaged to 30 minutes in AEST and cached in `data/sa1_prices.parquet`. +- **Models:** a naive baseline (same half-hour yesterday), LightGBM (lagged prices and calendar features) and a PyTorch LSTM (the past 7 days of prices plus calendar features). Each model forecasts all 48 prices of day D using only data up to the end of D-1. A pytest test checks for leakage. +- **Evaluation:** walk-forward. Hyperparameters are chosen on a 3-month validation window. The final 12 months are the test period, with models retrained every 30 days. Each day, the unchanged LP is run on the forecast, and the resulting schedule is scored at actual prices. + +| Model | Share of perfect-foresight revenue (365 test days) | +|---|---:| +| Naive (yesterday) | 27% | +| LightGBM | 49% | +| LSTM | 62% | + +To reproduce (results go to `results/`, see [results/REPORT.md](results/REPORT.md)): + +```bash +uv sync --extra forecast +uv run python -m forecasting.data # optional: refresh the AEMO price cache +uv run python -m forecasting.backtest # validation -> freeze -> walk-forward test -> report +``` + +The API serves the results at `GET /api/v1/backtest/days` and `GET /api/v1/backtest/day/{date}`. The React app shows them on the **Backtest** page (`#/backtest`). + ## Current Limitations ### Single Market Region diff --git a/data/sa1_prices.parquet b/data/sa1_prices.parquet new file mode 100644 index 0000000000000000000000000000000000000000..9b93116bdf285d9db5c57e93bb2acfbb0661dfe4 GIT binary patch literal 2771602 zcmX7Q3z$>Y_5G&Z>b;q{GnpAC6Alotp;l})LK((ZPc~~H8$2-Q)}&i9shnG2t#u3x%=$B z_S$RjbG@tjyF0nFe%D}U^sU94I^RF7>!fy~+)ZtT$ns#n^ML&CUwx{v`A=TwMdQZtEpAnradLz6y}q33x*xez zmw8Q(v-mvz+ob9FYa3Oceq4|9p`P>hM4i{Yix16oJO5?D<%W8kr$53UzOcc0$L;v< z-yY|?xNAt&D`$tC<9@8a_<4EG#9PKA0An&h1u;K_dli2u@P4|;!%A(y%)cY^QvC1_u(eSAvMYW-J&}Am917U zt3K7w3%23QA8fJO`NJ(%7mKZGnJ*m1zkcS^e6$rS*u7JAaNBk@pw#y}*SYu;pS8mC zx8YK6?XZTqG>m^fwN0i*?6>u+J+)Solu|R6Tve7U63=YJm>l?4S*5f}dEA_4lmC5#uO)Nj}|#@3d^TmiU1!s-F&c$~?Tq>ZIeI0>80Ub1gJ~c+0LIs{Z=(h0YE!I5MwJI;(VkLfZ$XXzMNKZF;>U?^O)yLl)#)kZR zi`&Jg`r%rq`|t!`92zBldI)ZLuut=aeEi>jEfR9jFif-sAw@3~9Xx3W3+&zMcJfC% zVf`h$)jIvI&rZ9rv@h+1#lzdJdET=JF0{~Q7kt=TDLO;CnvpzSL+z00x?Zfx( 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/dev/null +++ b/forecasting/backtest.py @@ -0,0 +1,278 @@ +"""Walk-forward backtest of day-ahead price forecasts fed into the battery LP. + + python -m forecasting.backtest # full run: validation -> freeze -> test -> report + python -m forecasting.backtest --reuse-validation # skip tuning, reuse results/validation.json + +Stages +1. Split by time: train = start .. val_start-1, validation = 3 months before the test period, + test = final TEST_DAYS days. During tuning the price matrix is truncated at the test start, + so the test period is not merely unused but physically absent. +2. Validation: every candidate config runs the same walk-forward procedure as the test + (expanding window, retrain every RETRAIN_EVERY_DAYS) over the validation months; the config + with the best validation capture rate is frozen and written to results/validation.json. +3. Test: frozen configs, expanding-window retrain every RETRAIN_EVERY_DAYS days on all data before + the retrain date, then forecast each day of that block one day ahead. +4. Each day: LP on actual prices (perfect foresight) and on each forecast; forecast schedules + are scored at actual prices. Capture rate = sum(realised) / sum(perfect foresight). +""" + +import argparse +import json +import time + +import numpy as np +import pandas as pd + +from forecasting import config as C +from forecasting.data import daily_matrix, gap_report, load_prices +from forecasting.economics import capture_rate, realised_profit, schedule +from forecasting.models import build + +LGBM_GRID = [ + {"transform": t, "objective": o, "num_leaves": nl, "train_start": ts} + for (t, o) in [("identity", "l2"), ("identity", "huber"), ("asinh", "l2")] + for nl in (15, 63) + for ts in ("2019-01-01", "2021-10-01") # all history vs. five-minute-settlement era only +] +LSTM_GRID = [ + {"transform": t, "loss": l, "hidden": h} + for (t, l) in [("identity", "mse"), ("identity", "huber"), ("asinh", "mse")] + for h in (32, 64) +] +GRIDS = {"naive": [{}], "lightgbm": LGBM_GRID, "lstm": LSTM_GRID} +ML_MODELS = ["lightgbm", "lstm"] +FORECAST_MODELS = ["naive", *ML_MODELS] + + +def split(P: pd.DataFrame) -> dict[str, pd.Timestamp]: + last = P.index[-1] + test_start = last - pd.Timedelta(days=C.TEST_DAYS - 1) + val_start = test_start - pd.DateOffset(months=C.VALIDATION_MONTHS) + return {"data_start": P.index[0], "val_start": val_start, "test_start": test_start, "test_end": last} + + +def complete_days(P: pd.DataFrame, start, end) -> pd.DatetimeIndex: + days = P.index[(P.index >= start) & (P.index <= end)] + return days[P.loc[days].notna().all(axis=1).to_numpy()] + + +def walk_forward(name: str, cfg: dict, P: pd.DataFrame, days: pd.DatetimeIndex) -> tuple[dict, list]: + """Expanding-window retrain every RETRAIN_EVERY_DAYS; forecast each day one day ahead.""" + forecasts, fits = {}, [] + block_start = days[0] + while block_start <= days[-1]: + block_end = block_start + pd.Timedelta(days=C.RETRAIN_EVERY_DAYS) + block = days[(days >= block_start) & (days < block_end)] + model = build(name, cfg).fit(P, cutoff=block_start) + trained_through = getattr(model, "trained_through", None) + assert trained_through is None or trained_through < block_start, "training saw a forecast day" + preds = model.predict(P, block) + forecasts.update(zip(block, preds)) + fits.append({ + "retrain_date": block_start.strftime("%Y-%m-%d"), + "trained_through": trained_through.strftime("%Y-%m-%d") if trained_through is not None else None, + "rounds_or_epochs": getattr(model, "num_rounds", getattr(model, "num_epochs", None)), + }) + block_start = block_end + return forecasts, fits + + +def score(P: pd.DataFrame, days, forecasts: dict, pf_cache: dict) -> dict: + realised, perfect, errors = [], [], [] + for d in days: + actual = P.loc[d].to_numpy() + if d not in pf_cache: + s = schedule(actual) + pf_cache[d] = (s, realised_profit(s["charge_mw"], s["discharge_mw"], actual).sum()) + s = schedule(forecasts[d]) + realised.append(realised_profit(s["charge_mw"], s["discharge_mw"], actual).sum()) + perfect.append(pf_cache[d][1]) + errors.append(forecasts[d] - actual) + e = np.concatenate(errors) + return { + "capture_rate": capture_rate(sum(realised), sum(perfect)), + "realised_aud": float(sum(realised)), + "perfect_foresight_aud": float(sum(perfect)), + "mae": float(np.abs(e).mean()), + "rmse": float(np.sqrt((e ** 2).mean())), + } + + +def validate(P: pd.DataFrame, s: dict) -> dict: + P_tune = P[P.index < s["test_start"]] # the test period does not exist during tuning + val_days = complete_days(P_tune, s["val_start"], s["test_start"] - pd.Timedelta(days=1)) + pf_cache, out = {}, {} + for name in FORECAST_MODELS: + candidates = [] + for cfg in GRIDS[name]: + t0 = time.time() + forecasts, _ = walk_forward(name, cfg, P_tune, val_days) + m = score(P_tune, val_days, forecasts, pf_cache) + candidates.append({"config": cfg, **m}) + print(f" [val] {name:8s} {json.dumps(cfg):95s} capture={m['capture_rate']:.3f} " + f"mae={m['mae']:.1f} rmse={m['rmse']:.1f} ({time.time() - t0:.0f}s)") + best = max(candidates, key=lambda c: c["capture_rate"]) + out[name] = {"chosen": best["config"], "candidates": candidates} + return { + "selection_metric": "validation capture rate (aggregate realised / perfect foresight)", + "validation_days": len(val_days), + "validation_period": [val_days[0].strftime("%Y-%m-%d"), val_days[-1].strftime("%Y-%m-%d")], + "models": out, + } + + +def run_test(P: pd.DataFrame, s: dict, chosen: dict) -> tuple[dict, dict, dict]: + test_days = complete_days(P, s["test_start"], s["test_end"]) + forecasts, fits = {}, {} + for name in FORECAST_MODELS: + t0 = time.time() + forecasts[name], fits[name] = walk_forward(name, chosen[name], P, test_days) + print(f" [test] {name} forecasts done ({time.time() - t0:.0f}s)") + + C.DAILY_DIR.mkdir(parents=True, exist_ok=True) + rows = [] + slots = [f"{h:02d}:{m:02d}" for h in range(24) for m in (0, 30)] + for i, d in enumerate(test_days): + actual = P.loc[d].to_numpy() + price_inputs = {"perfect_foresight": actual, **{n: forecasts[n][d] for n in FORECAST_MODELS}} + methods, row = {}, {"date": d.strftime("%Y-%m-%d"), "price_max": float(actual.max()), "price_min": float(actual.min())} + for method, prices in price_inputs.items(): + sch = schedule(prices) + profit = realised_profit(sch["charge_mw"], sch["discharge_mw"], actual) + methods[method] = { + "charge_mw": sch["charge_mw"].tolist(), + "discharge_mw": sch["discharge_mw"].tolist(), + "soc_mwh": sch["soc_mwh"].tolist(), + "cumulative_profit_aud": np.round(np.cumsum(profit), 2).tolist(), + "profit_aud": round(float(profit.sum()), 2), + } + row[f"{method}_aud"] = float(profit.sum()) + if method != "perfect_foresight": + row[f"{method}_abs_err"] = float(np.abs(prices - actual).sum()) + row[f"{method}_sq_err"] = float(((prices - actual) ** 2).sum()) + day_json = { + "date": row["date"], + "intervals": slots, + "actual_price": np.round(actual, 2).tolist(), + "forecast_price": {n: np.round(forecasts[n][d], 2).tolist() for n in FORECAST_MODELS}, + "methods": methods, + } + (C.DAILY_DIR / f"{row['date']}.json").write_text(json.dumps(day_json)) + rows.append(row) + if (i + 1) % 50 == 0: + print(f" [test] scored {i + 1}/{len(test_days)} days") + return pd.DataFrame(rows), forecasts, fits + + +def summarise(daily: pd.DataFrame) -> dict: + n_intervals = len(daily) * C.INTERVALS_PER_DAY + pf = daily["perfect_foresight_aud"].sum() + models = {} + for n in FORECAST_MODELS: + models[n] = { + "capture_rate": float(daily[f"{n}_aud"].sum() / pf), + "realised_aud": float(daily[f"{n}_aud"].sum()), + "mae": float(daily[f"{n}_abs_err"].sum() / n_intervals), + "rmse": float(np.sqrt(daily[f"{n}_sq_err"].sum() / n_intervals)), + "days_with_loss": int((daily[f"{n}_aud"] < 0).sum()), + } + q = pd.PeriodIndex(pd.to_datetime(daily["date"]), freq="Q").astype(str) + quarterly = [] + for quarter, g in daily.groupby(q): + entry = {"quarter": quarter, "days": len(g), "perfect_foresight_aud": float(g["perfect_foresight_aud"].sum())} + for n in FORECAST_MODELS: + entry[f"{n}_capture_rate"] = float(g[f"{n}_aud"].sum() / g["perfect_foresight_aud"].sum()) + quarterly.append(entry) + best_ml = max(ML_MODELS, key=lambda n: models[n]["capture_rate"]) + return {"perfect_foresight_aud": float(pf), "models": models, "quarterly": quarterly, "best_ml_model": best_ml} + + +def day_index(daily: pd.DataFrame, best: str) -> dict: + """Small index for the API/React picker; 'best'/'worst' use the best ML model's daily capture + on days where perfect foresight earned at least $100 (ratios on near-zero days are meaningless).""" + d = daily.copy() + d["best_model_capture"] = d[f"{best}_aud"] / d["perfect_foresight_aud"] + eligible = d[d["perfect_foresight_aud"] >= 100] + cols = ["date", "price_max", "price_min", "perfect_foresight_aud", *[f"{n}_aud" for n in FORECAST_MODELS]] + return { + "best_model": best, + "best_day": eligible.loc[eligible["best_model_capture"].idxmax(), "date"], + "worst_day": eligible.loc[eligible["best_model_capture"].idxmin(), "date"], + "biggest_spike_day": d.loc[d["price_max"].idxmax(), "date"], + "definitions": { + "best_day": f"highest {best} capture rate among days where perfect foresight earned >= $100", + "worst_day": f"lowest {best} capture rate among days where perfect foresight earned >= $100", + "biggest_spike_day": "highest actual 30-minute price", + }, + "days": d[cols].round(2).to_dict(orient="records"), + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--reuse-validation", action="store_true", help="reuse results/validation.json instead of re-tuning") + args = parser.parse_args() + np.random.seed(C.SEED) + t_start = time.time() + + df = load_prices() + P = daily_matrix(df) + s = split(P) + print("split:", {k: v.strftime("%Y-%m-%d") for k, v in s.items()}) + + C.RESULTS_DIR.mkdir(parents=True, exist_ok=True) + val_path = C.RESULTS_DIR / "validation.json" + if args.reuse_validation and val_path.exists(): + validation = json.loads(val_path.read_text()) + print("reusing", val_path) + else: + print("validation (test period excluded from the data)...") + validation = validate(P, s) + val_path.write_text(json.dumps(validation, indent=2)) + chosen = {n: validation["models"][n]["chosen"] for n in FORECAST_MODELS} + print("frozen configs:", chosen) + + print("test...") + daily, forecasts, fits = run_test(P, s, chosen) + summary = summarise(daily) + index = day_index(daily, summary["best_ml_model"]) + (C.DAILY_DIR / "index.json").write_text(json.dumps(index)) + + gaps = gap_report(df) + result = { + "generated_at": pd.Timestamp.now().strftime("%Y-%m-%d %H:%M"), + "data": { + "source": "AEMO NEMWeb aggregated price and demand, SA1 (5-min RRP averaged to 30-min; pre-Oct-2021 native 30-min)", + "timezone": "NEM market time (AEST, UTC+10, no daylight saving); intervals labelled by start", + **{k: v for k, v in gaps.items() if k != "days_with_missing_intervals"}, + "n_days_with_missing_intervals": len(gaps["days_with_missing_intervals"]), + }, + "battery": {"capacity_mwh": C.BATTERY_CAPACITY_MWH, "max_mw": C.BATTERY_MAX_MW, + "efficiency": C.BATTERY_EFFICIENCY, "efficiency_applied_to": "charging only (unchanged app LP)", + "start_soc_mwh": 0.0, "interval_hours": C.DT_HOURS}, + "split": { + "train": [s["data_start"].strftime("%Y-%m-%d"), (s["val_start"] - pd.Timedelta(days=1)).strftime("%Y-%m-%d")], + "validation": validation["validation_period"], + "test": [daily["date"].iloc[0], daily["date"].iloc[-1]], + "retrain_every_days": C.RETRAIN_EVERY_DAYS, + }, + "Z_test_days": int(len(daily)), + "chosen_configs": chosen, + "retrains": fits, + **summary, + "X_best_model_capture_pct": round(100 * summary["models"][summary["best_ml_model"]]["capture_rate"]), + "Y_naive_capture_pct": round(100 * summary["models"]["naive"]["capture_rate"]), + "runtime_seconds": round(time.time() - t_start), + } + (C.RESULTS_DIR / "backtest.json").write_text(json.dumps(result, indent=2)) + + from forecasting.report import write_report + write_report(result, validation, daily, forecasts, P) + + print(json.dumps({k: result[k] for k in ("Z_test_days", "best_ml_model", "X_best_model_capture_pct", "Y_naive_capture_pct")})) + for n, m in summary["models"].items(): + print(f" {n:8s} capture={m['capture_rate']:.3f} mae={m['mae']:.1f} rmse={m['rmse']:.1f}") + + +if __name__ == "__main__": + main() diff --git a/forecasting/config.py b/forecasting/config.py new file mode 100644 index 0000000..16e2b44 --- /dev/null +++ b/forecasting/config.py @@ -0,0 +1,30 @@ +from pathlib import Path + +ROOT = Path(__file__).resolve().parent.parent +DATA_DIR = ROOT / "data" +RAW_DIR = DATA_DIR / "raw" +PRICES_PATH = DATA_DIR / "sa1_prices.parquet" +RESULTS_DIR = ROOT / "results" +DAILY_DIR = RESULTS_DIR / "daily" +MODELS_DIR = RESULTS_DIR / "models" + +REGION = "SA1" +# first month of history to download from AEMO NEMWeb +HISTORY_START = "2019-01" + +INTERVALS_PER_DAY = 48 +DT_HOURS = 0.5 + +# battery used for every backtest day: the React app's default sliders + the API's default efficiency +BATTERY_CAPACITY_MWH = 5.0 +BATTERY_MAX_MW = 2.5 +BATTERY_EFFICIENCY = 0.9 + +# walk-forward split (all in NEM market time, AEST) +TEST_DAYS = 365 +VALIDATION_MONTHS = 3 +RETRAIN_EVERY_DAYS = 30 + +SEED = 42 + +METHODS = ["perfect_foresight", "naive", "lightgbm", "lstm"] diff --git a/forecasting/data.py b/forecasting/data.py new file mode 100644 index 0000000..75223ce --- /dev/null +++ b/forecasting/data.py @@ -0,0 +1,133 @@ +"""Historical SA1 spot prices from AEMO NEMWeb, resampled to 30-minute means. + +Why NEMWeb and not Open Electricity: the OE free plan only serves the last 730 days and caps +each 5-minute request to a few days, while the AEMO "Aggregated price and demand" monthly CSVs +go back decades. Both carry the same AEMO dispatch prices (spot-checked: 5-minute values match). + +Conventions +- AEMO SETTLEMENTDATE is the END of the interval, in NEM market time (AEST, UTC+10, no DST). + We relabel every row to its interval START so a day D covers 00:00..23:30 AEST of D. +- 5-minute rows (from 1 Oct 2021, five-minute settlement) are averaged into 30-minute buckets, + the same mean-resample the live pipeline uses. Older files are already 30-minute. + +Run: python -m forecasting.data (downloads missing months, writes data/sa1_prices.parquet) +""" + +import io +import json +import time +from datetime import date + +import pandas as pd +import requests + +from forecasting.config import HISTORY_START, PRICES_PATH, RAW_DIR, REGION + +URL = "https://www.aemo.com.au/aemo/data/nem/priceanddemand/PRICE_AND_DEMAND_{ym}_{region}.csv" + + +def _months(start: str, end: date) -> list[str]: + return [p.strftime("%Y%m") for p in pd.period_range(start, end.strftime("%Y-%m"), freq="M")] + + +def download_month(ym: str, force: bool = False) -> pd.DataFrame | None: + RAW_DIR.mkdir(parents=True, exist_ok=True) + path = RAW_DIR / f"PRICE_AND_DEMAND_{ym}_{REGION}.csv" + if force or not path.exists(): + r = requests.get(URL.format(ym=ym, region=REGION), headers={"User-Agent": "Mozilla/5.0"}, timeout=120) + if r.status_code != 200 or not r.text.startswith("REGION"): + print(f" {ym}: not available (HTTP {r.status_code})") + return None + path.write_text(r.text) + time.sleep(0.5) # be polite to NEMWeb + return pd.read_csv(path) + + +def to_30min(raw: pd.DataFrame) -> pd.DataFrame: + df = raw.rename(columns={"SETTLEMENTDATE": "end", "RRP": "price", "TOTALDEMAND": "demand"}) + df["end"] = pd.to_datetime(df["end"], format="%Y/%m/%d %H:%M:%S") + df = df.sort_values("end").drop_duplicates("end") + + # interval length from the gap to the previous row: 5 min after 5MS, 30 min before it + length = df["end"].diff().dt.total_seconds().div(60) + length = length.where(length.isin([5, 30])).bfill().fillna(5) + df["start"] = df["end"] - pd.to_timedelta(length, unit="min") + + out = ( + df.set_index("start")[["price", "demand"]] + .resample("30min") + .agg(["mean", "count"]) + ) + return pd.DataFrame({ + "price": out[("price", "mean")], + "demand": out[("demand", "mean")], + "n_obs": out[("price", "count")], + }) + + +def build(force_current_month: bool = True) -> pd.DataFrame: + today = date.today() + frames = [] + for ym in _months(HISTORY_START, today): + is_current = ym == today.strftime("%Y%m") + raw = download_month(ym, force=force_current_month and is_current) + if raw is not None: + frames.append(raw) + + df = to_30min(pd.concat(frames, ignore_index=True)) + + # keep whole AEST days only, and drop today's (still incomplete) day + first_day = df.index.min().ceil("D") + last_day = min((df.index.max() + pd.Timedelta(minutes=30)).floor("D") - pd.Timedelta(days=1), + pd.Timestamp(today) - pd.Timedelta(days=1)) + full_index = pd.date_range(first_day, last_day + pd.Timedelta(hours=23, minutes=30), freq="30min", name="interval_start") + df = df.reindex(full_index) + df["n_obs"] = df["n_obs"].fillna(0).astype(int) + # buckets with no observations at all stay NaN (mean of nothing); they are reported, never invented + df.loc[df["n_obs"] == 0, ["price", "demand"]] = float("nan") + return df + + +def gap_report(df: pd.DataFrame) -> dict: + missing = df["price"].isna() + expected_obs = pd.Series(6, index=df.index).where(df.index >= pd.Timestamp("2021-10-01 04:00"), 1) + partial = (df["n_obs"] > 0) & (df["n_obs"] < expected_obs) + days_with_missing = sorted({d.strftime("%Y-%m-%d") for d in df.index[missing].normalize()}) + return { + "start": df.index.min().strftime("%Y-%m-%d %H:%M"), + "end": df.index.max().strftime("%Y-%m-%d %H:%M"), + "rows": int(len(df)), + "days": int(len(df) // 48), + "missing_30min_intervals": int(missing.sum()), + "partial_30min_intervals": int(partial.sum()), + "days_with_missing_intervals": days_with_missing, + "price_min": float(df["price"].min()), + "price_max": float(df["price"].max()), + "gap_handling": ( + "30-min buckets are the mean of whatever 5-min observations exist (partial buckets counted above). " + "Buckets with no observations stay NaN and are never filled in the stored data. Days with any NaN " + "price are excluded as backtest target days; as model inputs, LightGBM takes NaN natively and the " + "LSTM forward-fills." + ), + } + + +def load_prices() -> pd.DataFrame: + return pd.read_parquet(PRICES_PATH) + + +def daily_matrix(df: pd.DataFrame, column: str = "price") -> pd.DataFrame: + """Rows = AEST calendar days, columns = 48 half-hour intervals (0 = 00:00-00:30).""" + s = df[column] + out = pd.DataFrame({"day": s.index.normalize(), "slot": s.index.hour * 2 + s.index.minute // 30, "v": s.values}) + return out.pivot(index="day", columns="slot", values="v") + + +if __name__ == "__main__": + df = build() + PRICES_PATH.parent.mkdir(parents=True, exist_ok=True) + df.to_parquet(PRICES_PATH) + report = gap_report(df) + shown = {k: (v if k != "days_with_missing_intervals" else f"{len(v)} days: {v[:10]}{' ...' if len(v) > 10 else ''}") for k, v in report.items()} + print(json.dumps(shown, indent=2)) + print(f"saved {PRICES_PATH}") diff --git a/forecasting/economics.py b/forecasting/economics.py new file mode 100644 index 0000000..8ddfc60 --- /dev/null +++ b/forecasting/economics.py @@ -0,0 +1,30 @@ +"""Turn a price vector into a battery schedule with the app's own LP, then score it on actual prices.""" + +from datetime import datetime, timedelta + +import numpy as np + +from forecasting.config import BATTERY_CAPACITY_MWH, BATTERY_EFFICIENCY, BATTERY_MAX_MW, DT_HOURS +from main import BatterySpecs, MarketInterval, calculate_optimal_dispatch + +BATTERY = BatterySpecs(capacity_mwh=BATTERY_CAPACITY_MWH, max_mw=BATTERY_MAX_MW, efficiency=BATTERY_EFFICIENCY) + + +def schedule(prices, battery: BatterySpecs = BATTERY) -> dict[str, np.ndarray]: + """Run the unchanged LP from main.py on `prices` (forecast or actual).""" + t0 = datetime(2000, 1, 1) + market = [MarketInterval(timestamp=t0 + timedelta(minutes=30 * i), price_aud_mwh=float(p)) for i, p in enumerate(prices)] + result = calculate_optimal_dispatch(battery, market) + if result["optimization_status"] != "Optimal": + raise RuntimeError(f"LP status {result['optimization_status']}") + rows = result["schedule"] + return {k: np.array([r[k] for r in rows]) for k in ("charge_mw", "discharge_mw", "soc_mwh")} + + +def realised_profit(charge, discharge, actual_prices) -> np.ndarray: + """Per-interval AUD earned by executing (charge, discharge) at the ACTUAL prices.""" + return (np.asarray(discharge) - np.asarray(charge)) * np.asarray(actual_prices) * DT_HOURS + + +def capture_rate(realised_total: float, perfect_total: float) -> float: + return realised_total / perfect_total diff --git a/forecasting/features.py b/forecasting/features.py new file mode 100644 index 0000000..8956885 --- /dev/null +++ b/forecasting/features.py @@ -0,0 +1,86 @@ +"""Day-ahead features. Decision time is the end of day D-1: every feature for target day D +uses only rows of the daily price matrix strictly before D, plus calendar facts about D itself +(which are known in advance). tests/test_forecasting.py enforces this.""" + +from functools import lru_cache + +import holidays +import numpy as np +import pandas as pd + +from forecasting.config import INTERVALS_PER_DAY + +LAG_DAYS = (1, 2, 7) +LSTM_LOOKBACK_DAYS = 7 + + +@lru_cache(maxsize=None) +def _sa_holidays(year: int) -> frozenset: + return frozenset(holidays.Australia(subdiv="SA", years=year).keys()) + + +def is_sa_holiday(day: pd.Timestamp) -> int: + return int(day.date() in _sa_holidays(day.year)) + + +def check_contiguous(P: pd.DataFrame) -> None: + expected = pd.date_range(P.index[0], P.index[-1], freq="D") + if len(expected) != len(P) or not (P.index == expected).all(): + raise ValueError("daily price matrix must have one row per calendar day") + + +def lgbm_features(P: pd.DataFrame, days: pd.DatetimeIndex) -> pd.DataFrame: + """One row per (target day, half-hour slot). P = daily price matrix (days x 48).""" + check_contiguous(P) + pos = P.index.get_indexer(days) + if (pos < max(LAG_DAYS)).any(): + raise ValueError(f"need {max(LAG_DAYS)} days of history before each target day") + values = P.to_numpy() + + n = len(days) + slot = np.tile(np.arange(INTERVALS_PER_DAY), n) + rep = lambda a: np.repeat(np.asarray(a), INTERVALS_PER_DAY) + + feats = {"slot": slot} + for lag in LAG_DAYS: + feats[f"lag_{lag}d"] = values[pos - lag].ravel() + + prev = values[pos - 1] # day D-1, the last complete day at decision time + feats["d1_mean"] = rep(np.nanmean(prev, axis=1)) + feats["d1_std"] = rep(np.nanstd(prev, axis=1)) + feats["d1_min"] = rep(np.nanmin(prev, axis=1)) + feats["d1_max"] = rep(np.nanmax(prev, axis=1)) + + feats["dow"] = rep(days.dayofweek) + feats["month"] = rep(days.month) + feats["holiday"] = rep([is_sa_holiday(d) for d in days]) + + index = pd.MultiIndex.from_arrays([np.repeat(days, INTERVALS_PER_DAY), slot], names=["day", "slot"]) + return pd.DataFrame(feats, index=index) + + +def calendar_vector(day: pd.Timestamp) -> np.ndarray: + """Calendar features of the target day (known in advance): day-of-week one-hot, month sin/cos, holiday.""" + dow = np.zeros(7) + dow[day.dayofweek] = 1.0 + angle = 2 * np.pi * (day.month - 1) / 12 + return np.concatenate([dow, [np.sin(angle), np.cos(angle), is_sa_holiday(day)]]) + + +def lstm_inputs(P: pd.DataFrame, days: pd.DatetimeIndex) -> tuple[np.ndarray, np.ndarray]: + """Sequence input (n, 7, 48 + 2): the 7 days before D, each step = that day's 48 prices plus its + day-of-week sin/cos. Calendar input (n, 10) for day D. Prices are raw here; the model transforms them.""" + check_contiguous(P) + pos = P.index.get_indexer(days) + if (pos < LSTM_LOOKBACK_DAYS).any(): + raise ValueError(f"need {LSTM_LOOKBACK_DAYS} days of history before each target day") + values = P.ffill().to_numpy() # gaps (none in the current cache) are forward-filled for the LSTM only + + seq, cal = [], [] + for p, day in zip(pos, days): + window = values[p - LSTM_LOOKBACK_DAYS:p] + window_days = P.index[p - LSTM_LOOKBACK_DAYS:p] + angle = 2 * np.pi * window_days.dayofweek.to_numpy() / 7 + seq.append(np.column_stack([window, np.sin(angle), np.cos(angle)])) + cal.append(calendar_vector(day)) + return np.asarray(seq, dtype=np.float32), np.asarray(cal, dtype=np.float32) diff --git a/forecasting/models.py b/forecasting/models.py new file mode 100644 index 0000000..2358b11 --- /dev/null +++ b/forecasting/models.py @@ -0,0 +1,196 @@ +"""Day-ahead price models. Each model is fitted on target days strictly before a cutoff and then +forecasts any later day D from the daily price matrix, reading only rows before D. + +fit(P, cutoff): every training target day is < cutoff. The last ES_DAYS of that window are held out +for early stopping, then the model is refitted on the whole window with the chosen number of +boosting rounds / epochs. The test period is never visible to fit().""" + +import copy +import os + +import lightgbm as lgb +import numpy as np +import pandas as pd +import torch +from torch import nn + +from forecasting.config import SEED +from forecasting.features import LAG_DAYS, LSTM_LOOKBACK_DAYS, lgbm_features, lstm_inputs + +ES_DAYS = 60 # early-stopping split, taken from the end of each training window +ASINH_SCALE = 50.0 # $/MWh; asinh is ~linear below this and ~log above it + + +def forward(y: np.ndarray, transform: str) -> np.ndarray: + return np.arcsinh(y / ASINH_SCALE) if transform == "asinh" else y + + +def inverse(y: np.ndarray, transform: str) -> np.ndarray: + return np.sinh(y) * ASINH_SCALE if transform == "asinh" else y + + +def training_days(P: pd.DataFrame, cutoff: pd.Timestamp, lookback: int, start: pd.Timestamp | None = None) -> pd.DatetimeIndex: + """Target days usable for training: complete, enough history, and strictly before cutoff.""" + first = P.index[0] + pd.Timedelta(days=lookback) + if start is not None: + first = max(first, start) + days = P.index[(P.index >= first) & (P.index < cutoff)] + return days[P.loc[days].notna().all(axis=1).to_numpy()] + + +class NaiveModel: + """price(D, t) = price(D-1, t).""" + + name = "naive" + + def fit(self, P, cutoff): + return self + + def predict(self, P: pd.DataFrame, days: pd.DatetimeIndex) -> np.ndarray: + pos = P.index.get_indexer(days) + return P.to_numpy()[pos - 1] + + +class LightGBMModel: + name = "lightgbm" + + def __init__(self, transform="identity", objective="l2", num_leaves=31, train_start=None, learning_rate=0.05): + self.transform = transform + self.objective = objective + self.num_leaves = num_leaves + self.train_start = pd.Timestamp(train_start) if train_start else None + self.params = { + "objective": "huber" if objective == "huber" else "regression", + "learning_rate": learning_rate, + "num_leaves": num_leaves, + "min_data_in_leaf": 100, + "feature_fraction": 0.9, + "bagging_fraction": 0.8, + "bagging_freq": 1, + "lambda_l2": 1.0, + "seed": SEED, + "deterministic": True, + "force_row_wise": True, + "num_threads": min(8, os.cpu_count() or 1), + "verbose": -1, + } + if objective == "huber": + # huber delta in target units: $/MWh for raw prices, ~1 for asinh + self.params["alpha"] = 50.0 if transform == "identity" else 1.0 + + def _xy(self, P, days): + X = lgbm_features(P, days) + y = forward(P.loc[days].to_numpy().ravel(), self.transform) + return X, y + + def fit(self, P: pd.DataFrame, cutoff: pd.Timestamp): + days = training_days(P, cutoff, max(LAG_DAYS), self.train_start) + fit_days, es_days = days[:-ES_DAYS], days[-ES_DAYS:] + X_fit, y_fit = self._xy(P, fit_days) + X_es, y_es = self._xy(P, es_days) + probe = lgb.train( + self.params, lgb.Dataset(X_fit, y_fit), num_boost_round=3000, + valid_sets=[lgb.Dataset(X_es, y_es)], + callbacks=[lgb.early_stopping(100, verbose=False)], + ) + self.num_rounds = max(probe.best_iteration, 1) + X, y = self._xy(P, days) + self.booster = lgb.train(self.params, lgb.Dataset(X, y), num_boost_round=self.num_rounds) + self.trained_through = days[-1] + return self + + def predict(self, P: pd.DataFrame, days: pd.DatetimeIndex) -> np.ndarray: + pred = self.booster.predict(lgbm_features(P, days)) + return inverse(pred, self.transform).reshape(len(days), -1) + + +class _Net(nn.Module): + def __init__(self, n_seq_features: int, n_cal: int, hidden: int, n_out: int = 48): + super().__init__() + self.lstm = nn.LSTM(n_seq_features, hidden, batch_first=True) + self.head = nn.Sequential(nn.Linear(hidden + n_cal, 128), nn.ReLU(), nn.Linear(128, n_out)) + + def forward(self, seq, cal): + _, (h, _) = self.lstm(seq) + return self.head(torch.cat([h[-1], cal], dim=1)) + + +class LSTMModel: + name = "lstm" + + def __init__(self, transform="asinh", loss="mse", hidden=32, max_epochs=300, patience=25, batch_size=64, lr=1e-3): + self.transform = transform + self.loss = loss + self.hidden = hidden + self.max_epochs = max_epochs + self.patience = patience + self.batch_size = batch_size + self.lr = lr + + def _prep(self, P, days): + seq, cal = lstm_inputs(P, days) + seq[:, :, :48] = (forward(seq[:, :, :48], self.transform) - self.mu) / self.sigma + return torch.from_numpy(seq), torch.from_numpy(cal) + + def _target(self, P, days): + y = (forward(P.loc[days].to_numpy(), self.transform) - self.mu) / self.sigma + return torch.from_numpy(y.astype(np.float32)) + + def _train(self, seq, cal, y, epochs, val=None): + torch.manual_seed(SEED) + net = _Net(seq.shape[2], cal.shape[1], self.hidden) + opt = torch.optim.Adam(net.parameters(), lr=self.lr) + loss_fn = nn.HuberLoss(delta=1.0) if self.loss == "huber" else nn.MSELoss() + gen = torch.Generator().manual_seed(SEED) + best, best_epoch, best_state = float("inf"), 0, None + for epoch in range(1, epochs + 1): + net.train() + for idx in torch.randperm(len(y), generator=gen).split(self.batch_size): + opt.zero_grad() + loss = loss_fn(net(seq[idx], cal[idx]), y[idx]) + loss.backward() + nn.utils.clip_grad_norm_(net.parameters(), 1.0) + opt.step() + if val is not None: + net.eval() + with torch.no_grad(): + v = loss_fn(net(val[0], val[1]), val[2]).item() + if v < best: + best, best_epoch, best_state = v, epoch, copy.deepcopy(net.state_dict()) + elif epoch - best_epoch >= self.patience: + break + if val is not None: + net.load_state_dict(best_state) + return net, best_epoch + + def fit(self, P: pd.DataFrame, cutoff: pd.Timestamp): + torch.set_num_threads(min(8, os.cpu_count() or 1)) + days = training_days(P, cutoff, LSTM_LOOKBACK_DAYS) + fit_days, es_days = days[:-ES_DAYS], days[-ES_DAYS:] + # scaling statistics come from the training targets only + t = forward(P.loc[fit_days].to_numpy(), self.transform) + self.mu, self.sigma = float(t.mean()), float(t.std()) + + seq, cal = self._prep(P, fit_days) + es_seq, es_cal = self._prep(P, es_days) + _, self.num_epochs = self._train(seq, cal, self._target(P, fit_days), self.max_epochs, + val=(es_seq, es_cal, self._target(P, es_days))) + self.num_epochs = max(self.num_epochs, 1) + + t = forward(P.loc[days].to_numpy(), self.transform) + self.mu, self.sigma = float(t.mean()), float(t.std()) + seq, cal = self._prep(P, days) + self.net, _ = self._train(seq, cal, self._target(P, days), self.num_epochs) + self.trained_through = days[-1] + return self + + def predict(self, P: pd.DataFrame, days: pd.DatetimeIndex) -> np.ndarray: + self.net.eval() + seq, cal = self._prep(P, days) + with torch.no_grad(): + out = self.net(seq, cal).numpy().astype(np.float64) + return inverse(out * self.sigma + self.mu, self.transform) + + +def build(name: str, config: dict): + return {"naive": NaiveModel, "lightgbm": LightGBMModel, "lstm": LSTMModel}[name](**config) diff --git a/forecasting/report.py b/forecasting/report.py new file mode 100644 index 0000000..3777ef7 --- /dev/null +++ b/forecasting/report.py @@ -0,0 +1,161 @@ +"""Writes results/REPORT.md and the typical-day chart from the backtest outputs.""" + +import matplotlib + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +from forecasting import config as C + +COLOURS = {"perfect_foresight": "#111827", "naive": "#f59e0b", "lightgbm": "#2563eb", "lstm": "#db2777"} +LABELS = {"perfect_foresight": "Actual (perfect foresight)", "naive": "Naive (yesterday)", "lightgbm": "LightGBM", "lstm": "LSTM"} + + +def typical_day(daily: pd.DataFrame) -> str: + """The test day whose perfect-foresight profit is closest to the median.""" + pf = daily["perfect_foresight_aud"] + return daily.loc[(pf - pf.median()).abs().idxmin(), "date"] + + +def plot_day(day: str, forecasts: dict, P: pd.DataFrame, path): + d = pd.Timestamp(day) + x = np.arange(C.INTERVALS_PER_DAY) / 2 + fig, ax = plt.subplots(figsize=(10, 4.5)) + ax.plot(x, P.loc[d].to_numpy(), color=COLOURS["perfect_foresight"], lw=2.2, label=LABELS["perfect_foresight"]) + for n in ("naive", "lightgbm", "lstm"): + ax.plot(x, forecasts[n][d], color=COLOURS[n], lw=1.6, ls="--" if n == "naive" else "-", label=LABELS[n]) + ax.axhline(0, color="#9ca3af", lw=0.8) + ax.set_xlim(0, 23.5) + ax.set_xticks(range(0, 24, 3), [f"{h:02d}:00" for h in range(0, 24, 3)]) + ax.set_xlabel("Interval start (AEST)") + ax.set_ylabel("SA1 spot price (AUD/MWh)") + ax.set_title(f"Typical test day {day}: actual vs day-ahead forecasts") + ax.grid(alpha=0.25) + ax.legend(frameon=False, ncol=2) + fig.tight_layout() + fig.savefig(path, dpi=130) + plt.close(fig) + + +def pct(x: float) -> str: + return f"{100 * x:.1f}%" + + +def write_report(result: dict, validation: dict, daily: pd.DataFrame, forecasts: dict, P: pd.DataFrame): + day = typical_day(daily) + plot_day(day, forecasts, P, C.RESULTS_DIR / "typical_day.png") + row = daily.set_index("date").loc[day] + + m = result["models"] + best = result["best_ml_model"] + data, bat, sp = result["data"], result["battery"], result["split"] + ml_ranked = sorted(["lightgbm", "lstm"], key=lambda n: -m[n]["capture_rate"]) + + lines = [ + "# Day-ahead price forecasting backtest (SA1)", + "", + f"Generated {result['generated_at']} by `python -m forecasting.backtest` from `data/sa1_prices.parquet`.", + "", + "## Headline", + "", + f"Over **{result['Z_test_days']} test days** ({sp['test'][0]} to {sp['test'][1]}), scheduling the battery on " + f"**{LABELS[best]}** forecasts captured **{pct(m[best]['capture_rate'])}** of perfect-foresight arbitrage revenue, " + f"vs **{pct(m['naive']['capture_rate'])}** for the naive same-time-yesterday baseline. " + f"{LABELS[ml_ranked[0]]} beat {LABELS[ml_ranked[1]]} " + f"({pct(m[ml_ranked[0]]['capture_rate'])} vs {pct(m[ml_ranked[1]]['capture_rate'])}).", + "", + "## Data", + "", + f"- Source: {data['source']}", + f"- Range: {data['start']} to {data['end']} ({data['days']} days, {data['rows']:,} half-hour rows), {data['timezone']}", + f"- Gaps: {data['missing_30min_intervals']} missing and {data['partial_30min_intervals']} partially observed half-hours. {data['gap_handling']}", + f"- Price range: {data['price_min']:,.2f} to {data['price_max']:,.2f} AUD/MWh", + "- Open Electricity (used by the live app) only serves the last 730 days on the current plan, so history comes " + "from AEMO NEMWeb; the same 5-minute prices were spot-checked against Open Electricity.", + "", + "## Battery (fixed for every day)", + "", + f"- Capacity {bat['capacity_mwh']} MWh, max charge/discharge {bat['max_mw']} MW, efficiency {bat['efficiency']} " + f"({bat['efficiency_applied_to']}), starts each day empty, {bat['interval_hours']} h intervals.", + "- These are the React app's default sliders and the API's default efficiency. The LP is `calculate_optimal_dispatch()` " + "from `main.py`, unchanged.", + "", + "## Method", + "", + f"- Train: {sp['train'][0]} to {sp['train'][1]}. Validation: {sp['validation'][0]} to {sp['validation'][1]} " + f"({validation['validation_days']} days). Test: {sp['test'][0]} to {sp['test'][1]}.", + "- Decision at the end of day D-1; all 48 half-hour prices of day D are forecast from data up to D-1 only " + "(enforced by `tests/test_forecasting.py`).", + f"- Expanding window: each model is retrained every {sp['retrain_every_days']} days on all data before the retrain " + "date (early stopping uses the last 60 days of that training window), then forecasts each day of the block one day ahead.", + "- Hyperparameters, target transform / loss and training start were chosen on the validation period by capture " + "rate, with the test period removed from the data, then frozen.", + "- Each test day: LP on actual prices gives perfect foresight PF_D; LP on each forecast gives a schedule which is " + "scored at actual prices, `sum((d - c) * actual * 0.5)`. Capture rate = sum(realised) / sum(PF).", + "", + "Frozen configurations:", + "", + ] + for n in ("lightgbm", "lstm"): + lines.append(f"- {LABELS[n]}: `{result['chosen_configs'][n]}`") + lines += [ + "", + "## Results (test period)", + "", + f"Z = {result['Z_test_days']} days. Perfect-foresight revenue over the period: ${result['perfect_foresight_aud']:,.0f}.", + "", + "| Model | Capture rate | Realised revenue (AUD) | MAE (AUD/MWh) | RMSE (AUD/MWh) | Days with a loss |", + "|---|---:|---:|---:|---:|---:|", + ] + for n in ("naive", "lightgbm", "lstm"): + lines.append(f"| {LABELS[n]} | {pct(m[n]['capture_rate'])} | {m[n]['realised_aud']:,.0f} | {m[n]['mae']:.1f} | " + f"{m[n]['rmse']:.1f} | {m[n]['days_with_loss']} |") + lines += [ + "", + "Capture rate by quarter:", + "", + "| Quarter | Days | Perfect foresight (AUD) | Naive | LightGBM | LSTM |", + "|---|---:|---:|---:|---:|---:|", + ] + for q in result["quarterly"]: + lines.append(f"| {q['quarter']} | {q['days']} | {q['perfect_foresight_aud']:,.0f} | {pct(q['naive_capture_rate'])} | " + f"{pct(q['lightgbm_capture_rate'])} | {pct(q['lstm_capture_rate'])} |") + lines += [ + "", + "Validation results (used for selection; test period not involved):", + "", + "| Model | Config | Val capture | Val MAE |", + "|---|---|---:|---:|", + ] + for n in ("naive", "lightgbm", "lstm"): + for c in sorted(validation["models"][n]["candidates"], key=lambda c: -c["capture_rate"]): + chosen = " (chosen)" if c["config"] == validation["models"][n]["chosen"] else "" + lines.append(f"| {LABELS[n]}{chosen} | `{c['config']}` | {pct(c['capture_rate'])} | {c['mae']:.1f} |") + lines += [ + "", + "## Typical test day", + "", + f"![Typical test day](typical_day.png)", + "", + f"{day} is the test day whose perfect-foresight profit (${row['perfect_foresight_aud']:,.0f}) is closest to the " + f"median. Realised that day: naive ${row['naive_aud']:,.0f}, LightGBM ${row['lightgbm_aud']:,.0f}, " + f"LSTM ${row['lstm_aud']:,.0f}.", + "", + "## Limitations", + "", + "This is a single small battery acting as a price taker in one region (SA1) on energy arbitrage only: no FCAS, " + "no network losses, no degradation cost and no cycling limit. The LP is the app's unchanged formulation, so efficiency " + "is applied on charge only, the battery starts every day empty with no end-of-day state-of-charge target, and it may " + "charge and discharge in the same interval (which burns energy when prices are negative). Prices are 30-minute " + "averages of the 5-minute settlement price, so intra-half-hour spikes are smoothed out and the true opportunity " + "is larger than measured. The schedule is fixed once per day at the end of D-1 and never re-optimised intraday, " + "whereas a real operator rebids through the day using AEMO pre-dispatch forecasts, demand, weather and " + "interconnector data, none of which are used here (features are lagged prices and calendar only). " + "Forecasts are point forecasts with no uncertainty, so the schedule cannot hedge against spikes. Results come from " + f"one {result['Z_test_days']}-day test window. SA1 revenue is dominated by a few extreme-price days, so the capture " + "rate moves a lot from quarter to quarter (table above) and should be read as indicative rather than precise.", + "", + ] + (C.RESULTS_DIR / "REPORT.md").write_text("\n".join(lines)) diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index 25dd2bd..ad6f5fc 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -91,6 +91,7 @@ export default function App() { Battery Dispatch

    SA1 Region Optimization

    +     Forecast backtest →
    diff --git a/frontend/src/Backtest.tsx b/frontend/src/Backtest.tsx new file mode 100644 index 0000000..6f4dfed --- /dev/null +++ b/frontend/src/Backtest.tsx @@ -0,0 +1,286 @@ +import { Loader2 } from 'lucide-react'; +import { useEffect, useMemo, useState } from 'react'; +import { + Bar, + BarChart, + CartesianGrid, + Cell, + LabelList, + Line, + LineChart, + ReferenceLine, + ResponsiveContainer, + Tooltip, + XAxis, + YAxis +} from 'recharts'; +import type { BacktestDay, BacktestIndex, BacktestMethod, ForecastMethod } from './types'; + +const BASE_URL = import.meta.env.VITE_API_BASE_URL || "http://127.0.0.1:8000/api/v1"; + +// one colour per method, used in every chart; perfect foresight is always the dashed line +const METHODS: BacktestMethod[] = ["perfect_foresight", "naive", "lightgbm", "lstm"]; +const FORECASTS: ForecastMethod[] = ["naive", "lightgbm", "lstm"]; +const COLOUR: Record = { + perfect_foresight: "#e4e4e7", + naive: "#f59e0b", + lightgbm: "#3b82f6", + lstm: "#ec4899", +}; +const LABEL: Record = { + perfect_foresight: "Perfect foresight", + naive: "Naive", + lightgbm: "LightGBM", + lstm: "LSTM", +}; +const DASH: Partial> = { perfect_foresight: "6 4" }; + +const aud = (v: number) => `${v < 0 ? '-' : ''}$${Math.abs(v).toLocaleString('en-AU', { maximumFractionDigits: 0 })}`; +const price = (v: number) => `${v < 0 ? '-' : ''}$${Math.abs(Math.round(v))}`; +const tooltipStyle = { + contentStyle: { backgroundColor: '#18181b', borderColor: '#27272a', borderRadius: '8px', fontSize: '12px' }, + itemStyle: { color: '#fafafa' }, +}; + +function Legend() { + return ( +
    + {METHODS.map((m) => ( + + + + + {m === "perfect_foresight" ? "Perfect foresight / actual price" : LABEL[m]} + + ))} +
    + ); +} + +function StackedChart({ title, data, keys, unit, height, showXAxis, domain }: { + title: string; + data: Record[]; + keys: Record; + unit: (v: number) => string; + height: number; + showXAxis: boolean; + domain?: [number, number]; +}) { + return ( +
    +

    {title}

    + + + + + + unit(Number(v))} /> + {METHODS.map((m) => ( + + ))} + + +
    + ); +} + +export default function Backtest() { + const [index, setIndex] = useState(null); + const [selected, setSelected] = useState(""); + const [day, setDay] = useState(null); + const [error, setError] = useState(""); + const [zoomPrice, setZoomPrice] = useState(false); + + useEffect(() => { + fetch(`${BASE_URL}/backtest/days`) + .then((r) => (r.ok ? r.json() : Promise.reject(new Error(`HTTP ${r.status}`)))) + .then((idx: BacktestIndex) => { + setIndex(idx); + setSelected(idx.best_day); + }) + .catch((e) => setError(`Could not load backtest results (${e.message}).`)); + }, []); + + useEffect(() => { + if (!selected) return; + fetch(`${BASE_URL}/backtest/day/${selected}`) + .then((r) => (r.ok ? r.json() : Promise.reject(new Error(`HTTP ${r.status}`)))) + .then(setDay) + .catch((e) => setError(`Could not load ${selected} (${e.message}).`)); + }, [selected]); + + const rows = useMemo(() => { + if (!day) return []; + return day.intervals.map((time, i) => { + const row: Record = { time, price_perfect_foresight: day.actual_price[i] }; + for (const f of FORECASTS) row[`price_${f}`] = day.forecast_price[f][i]; + for (const m of METHODS) { + row[`soc_${m}`] = day.methods[m].soc_mwh[i]; + row[`cum_${m}`] = day.methods[m].cumulative_profit_aud[i]; + } + return row; + }); + }, [day]); + + const captureBars = useMemo(() => { + if (!index) return []; + return FORECASTS.map((m) => ({ + method: m, + label: LABEL[m], + capture: Math.round(index.summary.models[m].capture_rate * 1000) / 10, + })); + }, [index]); + + // zoomed price axis: fit the forecasts and the bulk of actual prices, letting rare spikes run off the top + const zoomDomain = useMemo((): [number, number] | undefined => { + if (!day) return undefined; + const sorted = [...day.actual_price].sort((a, b) => a - b); + const p90 = sorted[Math.floor(0.9 * (sorted.length - 1))]; + const all = [...day.actual_price, ...FORECASTS.flatMap((f) => day.forecast_price[f])]; + const hi = Math.max(p90, ...FORECASTS.flatMap((f) => day.forecast_price[f])); + return [Math.floor(Math.min(0, ...all) / 50) * 50, Math.ceil((hi * 1.15) / 50) * 50]; + }, [day]); + + const dates = index?.days.map((d) => d.date) ?? []; + const keysFor = (prefix: string) => + Object.fromEntries(METHODS.map((m) => [m, `${prefix}_${m}`])) as Record; + + const pickDate = (value: string) => { + if (dates.includes(value)) { + setError(""); + setSelected(value); + } + }; + + return ( +
    + {/* LEFT SIDEBAR */} +
    +
    + ← Live simulation +

    Forecast Backtest

    +

    + SA1 day-ahead forecasts scheduled by the same LP, scored on actual prices +

    +
    + + {index && ( + <> +
    +

    Test days: {index.summary.Z_test_days}

    +

    Period: {index.summary.split.test[0]} → {index.summary.split.test[1]}

    +

    Battery: {index.summary.battery.capacity_mwh} MWh / {index.summary.battery.max_mw} MW / {index.summary.battery.efficiency * 100}%

    +
    + +
    + + pickDate(e.target.value)} + className="w-full bg-zinc-900 border border-zinc-800 rounded-lg px-3 py-2 text-sm text-zinc-100 [color-scheme:dark]" + /> +
    + {([ + ["best_day", "Best day"], + ["worst_day", "Worst day"], + ["biggest_spike_day", "Biggest spike day"], + ] as const).map(([key, label]) => ( + + ))} +
    +
    + + )} +
    + + {/* MAIN CONTENT AREA */} +
    + {error &&

    {error}

    } + {!index && !error && ( +
    + Loading backtest results... +
    + )} + + {day && ( + <> + {/* per-day KPI cards */} +
    + {METHODS.map((m) => { + const profit = day.methods[m].profit_aud; + const pf = day.methods.perfect_foresight.profit_aud; + return ( +
    +

    {LABEL[m]}

    +

    {aud(profit)}

    +

    + {m === "perfect_foresight" ? "upper bound for this day" : pf > 0 ? `${Math.round((100 * profit) / pf)}% of perfect foresight` : "—"} +

    +
    + ); + })} +
    + +
    +
    +

    {day.date} (AEST, 30-minute intervals)

    + +
    + + + `${v}`} height={170} showXAxis={false} domain={index ? [0, index.summary.battery.capacity_mwh] : undefined} /> + +
    + + )} + + {index && ( +
    +

    Capture rate across all {index.summary.Z_test_days} test days

    +

    + Sum of realised revenue ÷ sum of perfect-foresight revenue ({aud(index.summary.perfect_foresight_aud)}). Dashed line = perfect foresight (100%). +

    + + + + Math.min(0, Math.floor(dataMin)), 100]} stroke="#52525b" fontSize={11} tickFormatter={(v) => `${v}%`} /> + + + + {captureBars.map((b) => )} + `${v}%`} /> + + + +
    + )} +
    +
    + ); +} diff --git a/frontend/src/Router.tsx b/frontend/src/Router.tsx new file mode 100644 index 0000000..95504df --- /dev/null +++ b/frontend/src/Router.tsx @@ -0,0 +1,14 @@ +import { useEffect, useState } from 'react' +import App from './App.tsx' +import Backtest from './Backtest.tsx' + +// hash routing keeps the S3/CloudFront static hosting unchanged: #/backtest -> Backtest, anything else -> live App +export default function Router() { + const [hash, setHash] = useState(window.location.hash) + useEffect(() => { + const onChange = () => setHash(window.location.hash) + window.addEventListener('hashchange', onChange) + return () => window.removeEventListener('hashchange', onChange) + }, []) + return hash.startsWith('#/backtest') ? : +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx index bef5202..b98bedd 100644 --- a/frontend/src/main.tsx +++ b/frontend/src/main.tsx @@ -1,10 +1,10 @@ import { StrictMode } from 'react' import { createRoot } from 'react-dom/client' import './index.css' -import App from './App.tsx' +import Router from './Router.tsx' createRoot(document.getElementById('root')!).render( - + , ) diff --git a/frontend/src/types.ts b/frontend/src/types.ts index 58d60ec..524eb6a 100644 --- a/frontend/src/types.ts +++ b/frontend/src/types.ts @@ -43,4 +43,60 @@ export interface OptimizationData { export interface ApiResponse { status: string; data: OptimizationData; -} \ No newline at end of file +} + +// --- BACKTEST TYPES (GET /backtest/days, /backtest/day/{date}) --- + +export type ForecastMethod = "naive" | "lightgbm" | "lstm"; +export type BacktestMethod = "perfect_foresight" | ForecastMethod; + +export interface MethodDay { + charge_mw: number[]; + discharge_mw: number[]; + soc_mwh: number[]; + cumulative_profit_aud: number[]; + profit_aud: number; +} + +export interface BacktestDay { + date: string; + intervals: string[]; + actual_price: number[]; + forecast_price: Record; + methods: Record; +} + +export interface ModelSummary { + capture_rate: number; + realised_aud: number; + mae: number; + rmse: number; + days_with_loss: number; +} + +export interface BacktestDayRow { + date: string; + price_max: number; + price_min: number; + perfect_foresight_aud: number; + naive_aud: number; + lightgbm_aud: number; + lstm_aud: number; +} + +export interface BacktestIndex { + summary: { + Z_test_days: number; + split: { train: string[]; validation: string[]; test: string[]; retrain_every_days: number }; + battery: { capacity_mwh: number; max_mw: number; efficiency: number }; + models: Record; + best_ml_model: ForecastMethod; + perfect_foresight_aud: number; + }; + best_model: ForecastMethod; + best_day: string; + worst_day: string; + biggest_spike_day: string; + definitions: Record<"best_day" | "worst_day" | "biggest_spike_day", string>; + days: BacktestDayRow[]; +} diff --git a/main.py b/main.py index 091c5fb..72311da 100644 --- a/main.py +++ b/main.py @@ -4,8 +4,10 @@ from pydantic import BaseModel, Field -from typing import List, cast -from datetime import datetime +from typing import List, cast +from datetime import date, datetime +from pathlib import Path +import json import pandas as pd # import numpy as np @@ -198,5 +200,32 @@ def run_live_simulation(battery: BatterySpecs): -# include router in the app +# backtest results (written by `python -m forecasting.backtest`) +RESULTS_DIR = Path(__file__).resolve().parent / "results" + + +def _read_json(path: Path): + if not path.exists(): + raise HTTPException(status_code=404, detail=f"{path.name} not found - run `python -m forecasting.backtest`") + return json.loads(path.read_text()) + + +@router.get("/backtest/days") +def backtest_days(): + summary = _read_json(RESULTS_DIR / "backtest.json") + index = _read_json(RESULTS_DIR / "daily" / "index.json") + return { + "summary": {k: summary[k] for k in ("Z_test_days", "split", "battery", "models", "quarterly", + "best_ml_model", "perfect_foresight_aud", "chosen_configs")}, + **index, + } + + +@router.get("/backtest/day/{day}") +def backtest_day(day: date): + # `day` is parsed as a date, so the filename can only ever be YYYY-MM-DD.json + return _read_json(RESULTS_DIR / "daily" / f"{day.isoformat()}.json") + + +# include router in the app app.include_router(router) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index d534dbc..9396ab2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,4 +23,22 @@ dependencies = [ [tool.pytest.ini_options] pythonpath = [ "." -] \ No newline at end of file +] +# forecasting/backtest extras (not needed by the deployed API): uv sync --extra forecast +[project.optional-dependencies] +forecast = [ + "holidays>=0.60", + "lightgbm>=4.5", + "matplotlib>=3.9", + "pyarrow>=18.0", + "torch>=2.5", +] + +# CPU-only torch wheels (the CUDA build is several GB) +[tool.uv.sources] +torch = [{ index = "pytorch-cpu" }] + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true diff --git a/results/REPORT.md b/results/REPORT.md new file mode 100644 index 0000000..b93b0b5 --- /dev/null +++ b/results/REPORT.md @@ -0,0 +1,87 @@ +# Day-ahead price forecasting backtest (SA1) + +Generated 2026-10-12 16:18 by `python -m forecasting.backtest` from `data/sa1_prices.parquet`. + +## Headline + +Over **365 test days** (2025-10-12 to 2026-10-11), scheduling the battery on **LSTM** forecasts captured **61.8%** of perfect-foresight arbitrage revenue, vs **27.2%** for the naive same-time-yesterday baseline. LSTM beat LightGBM (61.8% vs 49.5%). + +## Data + +- Source: AEMO NEMWeb aggregated price and demand, SA1 (5-min RRP averaged to 30-min; pre-Oct-2021 native 30-min) +- Range: 2019-01-01 00:00 to 2026-10-11 23:30 (2841 days, 136,368 half-hour rows), NEM market time (AEST, UTC+10, no daylight saving); intervals labelled by start +- Gaps: 0 missing and 0 partially observed half-hours. 30-min buckets are the mean of whatever 5-min observations exist (partial buckets counted above). Buckets with no observations stay NaN and are never filled in the stored data. Days with any NaN price are excluded as backtest target days; as model inputs, LightGBM takes NaN natively and the LSTM forward-fills. +- Price range: -962.20 to 20,300.00 AUD/MWh +- Open Electricity (used by the live app) only serves the last 730 days on the current plan, so history comes from AEMO NEMWeb; the same 5-minute prices were spot-checked against Open Electricity. + +## Battery (fixed for every day) + +- Capacity 5.0 MWh, max charge/discharge 2.5 MW, efficiency 0.9 (charging only (unchanged app LP)), starts each day empty, 0.5 h intervals. +- These are the React app's default sliders and the API's default efficiency. The LP is `calculate_optimal_dispatch()` from `main.py`, unchanged. + +## Method + +- Train: 2019-01-01 to 2025-07-11. Validation: 2025-07-12 to 2025-10-11 (92 days). Test: 2025-10-12 to 2026-10-11. +- Decision at the end of day D-1; all 48 half-hour prices of day D are forecast from data up to D-1 only (enforced by `tests/test_forecasting.py`). +- Expanding window: each model is retrained every 30 days on all data before the retrain date (early stopping uses the last 60 days of that training window), then forecasts each day of the block one day ahead. +- Hyperparameters, target transform / loss and training start were chosen on the validation period by capture rate, with the test period removed from the data, then frozen. +- Each test day: LP on actual prices gives perfect foresight PF_D; LP on each forecast gives a schedule which is scored at actual prices, `sum((d - c) * actual * 0.5)`. Capture rate = sum(realised) / sum(PF). + +Frozen configurations: + +- LightGBM: `{'transform': 'asinh', 'objective': 'l2', 'num_leaves': 63, 'train_start': '2019-01-01'}` +- LSTM: `{'transform': 'asinh', 'loss': 'mse', 'hidden': 64}` + +## Results (test period) + +Z = 365 days. Perfect-foresight revenue over the period: $560,314. + +| Model | Capture rate | Realised revenue (AUD) | MAE (AUD/MWh) | RMSE (AUD/MWh) | Days with a loss | +|---|---:|---:|---:|---:|---:| +| Naive (yesterday) | 27.2% | 152,353 | 80.0 | 584.1 | 85 | +| LightGBM | 49.5% | 277,312 | 57.8 | 417.2 | 32 | +| LSTM | 61.8% | 346,394 | 51.2 | 413.9 | 28 | + +Capture rate by quarter: + +| Quarter | Days | Perfect foresight (AUD) | Naive | LightGBM | LSTM | +|---|---:|---:|---:|---:|---:| +| 2025Q4 | 81 | 100,628 | 34.9% | 54.0% | 59.3% | +| 2026Q1 | 90 | 250,285 | 24.4% | 45.5% | 67.6% | +| 2026Q2 | 91 | 123,472 | 29.8% | 58.8% | 62.4% | +| 2026Q3 | 92 | 73,280 | 23.5% | 43.5% | 49.1% | +| 2026Q4 | 11 | 12,649 | 16.2% | 35.8% | 35.2% | + +Validation results (used for selection; test period not involved): + +| Model | Config | Val capture | Val MAE | +|---|---|---:|---:| +| Naive (yesterday) (chosen) | `{}` | 40.3% | 64.9 | +| LightGBM (chosen) | `{'transform': 'asinh', 'objective': 'l2', 'num_leaves': 63, 'train_start': '2019-01-01'}` | 61.2% | 52.7 | +| LightGBM | `{'transform': 'asinh', 'objective': 'l2', 'num_leaves': 15, 'train_start': '2019-01-01'}` | 60.7% | 52.7 | +| LightGBM | `{'transform': 'identity', 'objective': 'huber', 'num_leaves': 63, 'train_start': '2019-01-01'}` | 60.1% | 53.7 | +| LightGBM | `{'transform': 'asinh', 'objective': 'l2', 'num_leaves': 63, 'train_start': '2021-10-01'}` | 59.8% | 53.1 | +| LightGBM | `{'transform': 'identity', 'objective': 'huber', 'num_leaves': 63, 'train_start': '2021-10-01'}` | 58.5% | 52.7 | +| LightGBM | `{'transform': 'asinh', 'objective': 'l2', 'num_leaves': 15, 'train_start': '2021-10-01'}` | 57.2% | 53.9 | +| LightGBM | `{'transform': 'identity', 'objective': 'l2', 'num_leaves': 15, 'train_start': '2021-10-01'}` | 56.1% | 64.1 | +| LightGBM | `{'transform': 'identity', 'objective': 'l2', 'num_leaves': 15, 'train_start': '2019-01-01'}` | 56.1% | 65.4 | +| LightGBM | `{'transform': 'identity', 'objective': 'huber', 'num_leaves': 15, 'train_start': '2019-01-01'}` | 55.7% | 54.0 | +| LightGBM | `{'transform': 'identity', 'objective': 'huber', 'num_leaves': 15, 'train_start': '2021-10-01'}` | 54.3% | 53.6 | +| LightGBM | `{'transform': 'identity', 'objective': 'l2', 'num_leaves': 63, 'train_start': '2019-01-01'}` | 51.2% | 67.5 | +| LightGBM | `{'transform': 'identity', 'objective': 'l2', 'num_leaves': 63, 'train_start': '2021-10-01'}` | 43.3% | 78.7 | +| LSTM (chosen) | `{'transform': 'asinh', 'loss': 'mse', 'hidden': 64}` | 66.4% | 45.0 | +| LSTM | `{'transform': 'asinh', 'loss': 'mse', 'hidden': 32}` | 65.8% | 46.2 | +| LSTM | `{'transform': 'identity', 'loss': 'huber', 'hidden': 64}` | 65.5% | 47.4 | +| LSTM | `{'transform': 'identity', 'loss': 'huber', 'hidden': 32}` | 64.2% | 48.6 | +| LSTM | `{'transform': 'identity', 'loss': 'mse', 'hidden': 64}` | 53.2% | 59.7 | +| LSTM | `{'transform': 'identity', 'loss': 'mse', 'hidden': 32}` | 49.3% | 67.4 | + +## Typical test day + +![Typical test day](typical_day.png) + +2025-12-19 is the test day whose perfect-foresight profit ($786) is closest to the median. Realised that day: naive $354, LightGBM $477, LSTM $539. + +## Limitations + +This is a single small battery acting as a price taker in one region (SA1) on energy arbitrage only: no FCAS, no network losses, no degradation cost and no cycling limit. The LP is the app's unchanged formulation, so efficiency is applied on charge only, the battery starts every day empty with no end-of-day state-of-charge target, and it may charge and discharge in the same interval (which burns energy when prices are negative). Prices are 30-minute averages of the 5-minute settlement price, so intra-half-hour spikes are smoothed out and the true opportunity is larger than measured. The schedule is fixed once per day at the end of D-1 and never re-optimised intraday, whereas a real operator rebids through the day using AEMO pre-dispatch forecasts, demand, weather and interconnector data, none of which are used here (features are lagged prices and calendar only). Forecasts are point forecasts with no uncertainty, so the schedule cannot hedge against spikes. Results come from one 365-day test window. SA1 revenue is dominated by a few extreme-price days, so the capture rate moves a lot from quarter to quarter (table above) and should be read as indicative rather than precise. diff --git a/results/backtest.json b/results/backtest.json new file mode 100644 index 0000000..06c7a8c --- /dev/null +++ b/results/backtest.json @@ -0,0 +1,328 @@ +{ + "generated_at": "2026-10-12 16:18", + "data": { + "source": "AEMO NEMWeb aggregated price and demand, SA1 (5-min RRP averaged to 30-min; pre-Oct-2021 native 30-min)", + "timezone": "NEM market time (AEST, UTC+10, no daylight saving); intervals labelled by start", + "start": "2019-01-01 00:00", + "end": "2026-10-11 23:30", + "rows": 136368, + "days": 2841, + "missing_30min_intervals": 0, + "partial_30min_intervals": 0, + "price_min": -962.1966666666667, + "price_max": 20300.0, + "gap_handling": "30-min buckets are the mean of whatever 5-min observations exist (partial buckets counted above). Buckets with no observations stay NaN and are never filled in the stored data. Days with any NaN price are excluded as backtest target days; as model inputs, LightGBM takes NaN natively and the LSTM forward-fills.", + "n_days_with_missing_intervals": 0 + }, + "battery": { + "capacity_mwh": 5.0, + "max_mw": 2.5, + "efficiency": 0.9, + "efficiency_applied_to": "charging only (unchanged app LP)", + "start_soc_mwh": 0.0, + "interval_hours": 0.5 + }, + "split": { + "train": [ + "2019-01-01", + "2025-07-11" + ], + "validation": [ + "2025-07-12", + "2025-10-11" + ], + "test": [ + "2025-10-12", + "2026-10-11" + ], + "retrain_every_days": 30 + }, + "Z_test_days": 365, + "chosen_configs": { + "naive": {}, + "lightgbm": { + "transform": "asinh", + "objective": "l2", + "num_leaves": 63, + "train_start": "2019-01-01" + }, + "lstm": { + "transform": "asinh", + "loss": "mse", + "hidden": 64 + } + }, + "retrains": { + "naive": [ + { + "retrain_date": "2025-10-12", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2025-11-11", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2025-12-11", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-01-10", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-02-09", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-03-11", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-04-10", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-05-10", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-06-09", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-07-09", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-08-08", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-09-07", + "trained_through": null, + "rounds_or_epochs": null + }, + { + "retrain_date": "2026-10-07", + "trained_through": null, + "rounds_or_epochs": null + } + ], + "lightgbm": [ + { + "retrain_date": "2025-10-12", + "trained_through": "2025-10-11", + "rounds_or_epochs": 185 + }, + { + "retrain_date": "2025-11-11", + "trained_through": "2025-11-10", + "rounds_or_epochs": 80 + }, + { + "retrain_date": "2025-12-11", + "trained_through": "2025-12-10", + "rounds_or_epochs": 72 + }, + { + "retrain_date": "2026-01-10", + "trained_through": "2026-01-09", + "rounds_or_epochs": 150 + }, + { + "retrain_date": "2026-02-09", + "trained_through": "2026-02-08", + "rounds_or_epochs": 176 + }, + { + "retrain_date": "2026-03-11", + "trained_through": "2026-03-10", + "rounds_or_epochs": 68 + }, + { + "retrain_date": "2026-04-10", + "trained_through": "2026-04-09", + "rounds_or_epochs": 108 + }, + { + "retrain_date": "2026-05-10", + "trained_through": "2026-05-09", + "rounds_or_epochs": 256 + }, + { + "retrain_date": "2026-06-09", + "trained_through": "2026-06-08", + "rounds_or_epochs": 99 + }, + { + "retrain_date": "2026-07-09", + "trained_through": "2026-07-08", + "rounds_or_epochs": 96 + }, + { + "retrain_date": "2026-08-08", + "trained_through": "2026-08-07", + "rounds_or_epochs": 96 + }, + { + "retrain_date": "2026-09-07", + "trained_through": "2026-09-06", + "rounds_or_epochs": 42 + }, + { + "retrain_date": "2026-10-07", + "trained_through": "2026-10-06", + "rounds_or_epochs": 46 + } + ], + "lstm": [ + { + "retrain_date": "2025-10-12", + "trained_through": "2025-10-11", + "rounds_or_epochs": 6 + }, + { + "retrain_date": "2025-11-11", + "trained_through": "2025-11-10", + "rounds_or_epochs": 20 + }, + { + "retrain_date": "2025-12-11", + "trained_through": "2025-12-10", + "rounds_or_epochs": 18 + }, + { + "retrain_date": "2026-01-10", + "trained_through": "2026-01-09", + "rounds_or_epochs": 14 + }, + { + "retrain_date": "2026-02-09", + "trained_through": "2026-02-08", + "rounds_or_epochs": 9 + }, + { + "retrain_date": "2026-03-11", + "trained_through": "2026-03-10", + "rounds_or_epochs": 5 + }, + { + "retrain_date": "2026-04-10", + "trained_through": "2026-04-09", + "rounds_or_epochs": 14 + }, + { + "retrain_date": "2026-05-10", + "trained_through": "2026-05-09", + "rounds_or_epochs": 8 + }, + { + "retrain_date": "2026-06-09", + "trained_through": "2026-06-08", + "rounds_or_epochs": 11 + }, + { + "retrain_date": "2026-07-09", + "trained_through": "2026-07-08", + "rounds_or_epochs": 16 + }, + { + "retrain_date": "2026-08-08", + "trained_through": "2026-08-07", + "rounds_or_epochs": 19 + }, + { + "retrain_date": "2026-09-07", + "trained_through": "2026-09-06", + "rounds_or_epochs": 18 + }, + { + "retrain_date": "2026-10-07", + "trained_through": "2026-10-06", + "rounds_or_epochs": 8 + } + ] + }, + "perfect_foresight_aud": 560314.3511249999, + "models": { + "naive": { + "capture_rate": 0.2719056986239471, + "realised_aud": 152352.66509166668, + "mae": 80.04433837519025, + "rmse": 584.1204242470058, + "days_with_loss": 85 + }, + "lightgbm": { + "capture_rate": 0.4949221742102672, + "realised_aud": 277311.9969, + "mae": 57.75984683798546, + "rmse": 417.2313257763026, + "days_with_loss": 32 + }, + "lstm": { + "capture_rate": 0.6182133339160598, + "realised_aud": 346393.80305, + "mae": 51.194683588702205, + "rmse": 413.8813834823171, + "days_with_loss": 28 + } + }, + "quarterly": [ + { + "quarter": "2025Q4", + "days": 81, + "perfect_foresight_aud": 100628.05439166666, + "naive_capture_rate": 0.3494813534284034, + "lightgbm_capture_rate": 0.5403161474570122, + "lstm_capture_rate": 0.5931140610089076 + }, + { + "quarter": "2026Q1", + "days": 90, + "perfect_foresight_aud": 250285.0319166667, + "naive_capture_rate": 0.2443953712756034, + "lightgbm_capture_rate": 0.4551541104061288, + "lstm_capture_rate": 0.6763545250268749 + }, + { + "quarter": "2026Q2", + "days": 91, + "perfect_foresight_aud": 123472.04622500001, + "naive_capture_rate": 0.2977050231111937, + "lightgbm_capture_rate": 0.5882949215698073, + 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newline at end of file diff --git a/results/daily/2025-11-23.json b/results/daily/2025-11-23.json new file mode 100644 index 0000000..b2dcd4e --- /dev/null +++ b/results/daily/2025-11-23.json @@ -0,0 +1 @@ +{"date": "2025-11-23", "intervals": ["00:00", "00:30", "01:00", "01:30", "02:00", "02:30", "03:00", "03:30", "04:00", "04:30", "05:00", "05:30", "06:00", "06:30", "07:00", "07:30", "08:00", "08:30", "09:00", "09:30", "10:00", "10:30", "11:00", "11:30", "12:00", "12:30", "13:00", "13:30", "14:00", "14:30", "15:00", "15:30", "16:00", "16:30", "17:00", "17:30", "18:00", "18:30", "19:00", "19:30", "20:00", "20:30", "21:00", "21:30", "22:00", "22:30", "23:00", "23:30"], "actual_price": [116.64, 160.92, 150.93, 178.64, 178.11, 158.44, 135.26, 155.57, 207.48, 232.38, 176.56, 148.92, 87.75, 31.47, -12.04, -19.94, -22.38, -13.62, -10.83, -24.64, -24.86, -9.1, -7.32, -8.58, -5.19, -11.63, -10.04, -10.56, -10.18, -16.36, -38.92, -14.07, -19.0, -9.68, -3.1, 60.18, 96.23, 101.71, 130.48, 132.19, 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newline at end of file diff --git a/results/daily/2026-04-24.json b/results/daily/2026-04-24.json new file mode 100644 index 0000000..f12fb6e --- /dev/null +++ b/results/daily/2026-04-24.json @@ -0,0 +1 @@ +{"date": "2026-04-24", "intervals": ["00:00", "00:30", "01:00", "01:30", "02:00", "02:30", "03:00", "03:30", "04:00", "04:30", "05:00", "05:30", "06:00", "06:30", "07:00", "07:30", "08:00", "08:30", "09:00", "09:30", "10:00", "10:30", "11:00", "11:30", "12:00", "12:30", "13:00", "13:30", "14:00", "14:30", "15:00", "15:30", "16:00", "16:30", "17:00", "17:30", "18:00", "18:30", "19:00", "19:30", "20:00", "20:30", "21:00", "21:30", "22:00", "22:30", "23:00", "23:30"], "actual_price": [7.28, 6.73, 4.49, 4.1, 3.33, 3.58, 13.94, 8.41, 8.79, 5.86, 11.96, 16.47, 20.09, 26.44, 13.38, 6.01, -2.08, -4.68, -13.12, -7.6, -4.16, -4.28, -5.0, -6.83, -4.96, -3.35, -4.16, -3.07, -2.62, -2.01, -0.82, -1.3, 28.64, 59.91, 64.77, 68.28, 72.73, 67.91, 63.32, 64.9, 62.72, 50.82, 30.13, 18.88, 1.58, -0.25, 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--git a/results/daily/2026-06-28.json b/results/daily/2026-06-28.json new file mode 100644 index 0000000..a9b65e3 --- /dev/null +++ b/results/daily/2026-06-28.json @@ -0,0 +1 @@ +{"date": "2026-06-28", "intervals": ["00:00", "00:30", "01:00", "01:30", "02:00", "02:30", "03:00", "03:30", "04:00", "04:30", "05:00", "05:30", "06:00", "06:30", "07:00", "07:30", "08:00", "08:30", "09:00", "09:30", "10:00", "10:30", "11:00", "11:30", "12:00", "12:30", "13:00", "13:30", "14:00", "14:30", "15:00", "15:30", "16:00", "16:30", "17:00", "17:30", "18:00", "18:30", "19:00", "19:30", "20:00", "20:30", "21:00", "21:30", "22:00", "22:30", "23:00", "23:30"], "actual_price": [73.75, 77.59, 74.73, 78.42, 75.35, 70.26, 69.92, 68.42, 63.97, 63.52, 64.26, 71.36, 69.29, 73.15, 80.4, 77.64, 79.05, 73.66, 67.9, 61.64, 60.72, 58.28, 57.93, 56.46, 56.13, 53.76, 55.03, 53.81, 54.36, 60.87, 62.64, 94.71, 100.45, 101.91, 100.72, 93.66, 85.11, 80.5, 78.96, 78.2, 78.1, 74.24, 68.93, 66.7, 64.4, 53.15, 37.44, 39.86], 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newline at end of file diff --git a/results/daily/2026-08-04.json b/results/daily/2026-08-04.json new file mode 100644 index 0000000..75566a3 --- /dev/null +++ b/results/daily/2026-08-04.json @@ -0,0 +1 @@ +{"date": "2026-08-04", "intervals": ["00:00", "00:30", "01:00", "01:30", "02:00", "02:30", "03:00", "03:30", "04:00", "04:30", "05:00", "05:30", "06:00", "06:30", "07:00", "07:30", "08:00", "08:30", "09:00", "09:30", "10:00", "10:30", "11:00", "11:30", "12:00", "12:30", "13:00", "13:30", "14:00", "14:30", "15:00", "15:30", "16:00", "16:30", "17:00", "17:30", "18:00", "18:30", "19:00", "19:30", "20:00", "20:30", "21:00", "21:30", "22:00", "22:30", "23:00", "23:30"], "actual_price": [96.58, 101.32, 96.81, 92.65, 99.63, 91.46, 93.46, 93.44, 88.66, 88.43, 80.76, 88.69, 91.73, 95.85, 95.96, 83.8, 79.08, 78.96, 84.8, 76.95, 84.68, 82.2, 86.1, 90.02, 88.62, 101.91, 101.18, 109.04, 107.79, 102.97, 112.49, 134.46, 135.07, 139.64, 129.41, 140.98, 132.46, 132.3, 132.25, 133.45, 139.88, 138.84, 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or later --- + +def test_lgbm_features_ignore_target_day_and_future(): + P = synthetic_prices() + D = P.index[120] + days = pd.DatetimeIndex([D]) + pd.testing.assert_frame_equal(lgbm_features(P, days), lgbm_features(tamper_from(P, D), days)) + + +def test_lstm_inputs_ignore_target_day_and_future(): + P = synthetic_prices() + D = P.index[120] + days = pd.DatetimeIndex([D]) + for a, b in zip(lstm_inputs(P, days), lstm_inputs(tamper_from(P, D), days)): + np.testing.assert_array_equal(a, b) + + +def test_features_do_change_when_history_changes(): + # guard against a vacuous leakage test: D-1 must actually matter + P = synthetic_prices() + D = P.index[120] + days = pd.DatetimeIndex([D]) + assert not lgbm_features(P, days).equals(lgbm_features(tamper_from(P, P.index[119]), days)) + + +@pytest.mark.parametrize("model_factory", [ + lambda: NaiveModel(), + lambda: LightGBMModel(transform="asinh", num_leaves=7), + lambda: LSTMModel(hidden=8, max_epochs=3, patience=2), +], ids=["naive", "lightgbm", "lstm"]) +def test_fit_and_forecast_ignore_target_day_and_future(model_factory): + """End to end: fit with cutoff D and forecast D; tampering with D and later changes nothing.""" + P = synthetic_prices() + D = P.index[150] + days = pd.DatetimeIndex([D]) + clean = model_factory().fit(P, cutoff=D).predict(P, days) + Q = tamper_from(P, D) + tampered = model_factory().fit(Q, cutoff=D).predict(Q, days) + np.testing.assert_allclose(clean, tampered) + + +def test_split_order(): + P = synthetic_prices(n_days=800) + s = split(P) + assert s["data_start"] < s["val_start"] < s["test_start"] <= s["test_end"] + assert (s["test_end"] - s["test_start"]).days == 364 + + +# --- realised profit --- + +def test_realised_profit_hand_calculation(): + charge = [2.0, 0.0, 0.0] + discharge = [0.0, 0.0, 1.8] + actual = [10.0, 50.0, 200.0] + # (0 - 2) * 10 * 0.5 + 0 + (1.8 - 0) * 200 * 0.5 = -10 + 180 + np.testing.assert_allclose(realised_profit(charge, discharge, actual), [-10.0, 0.0, 180.0]) + + +def test_realised_profit_uses_actual_not_forecast_prices(): + actual = np.array([10.0] * 24 + [200.0] * 24) + # forecast puts the peak in the morning, when actual prices are flat and cheap + wrong_forecast = np.array([10.0] * 12 + [200.0] * 12 + [10.0] * 24) + s = schedule(wrong_forecast) + realised = realised_profit(s["charge_mw"], s["discharge_mw"], actual).sum() + pf = schedule(actual) + perfect = realised_profit(pf["charge_mw"], pf["discharge_mw"], actual).sum() + assert perfect > 0 + # it cycled inside the flat $10 period, so it lost the 10% charging loss + assert realised < 0 + assert realised <= perfect + + +def test_perfect_forecast_captures_everything(): + actual = np.array([30.0, -20.0, 5.0, 300.0] * 12) + s = schedule(actual) + pf = realised_profit(s["charge_mw"], s["discharge_mw"], actual).sum() + s2 = schedule(actual.copy()) + assert realised_profit(s2["charge_mw"], s2["discharge_mw"], actual).sum() == pytest.approx(pf) diff --git a/uv.lock b/uv.lock index 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++++++++++++++++++++++------------ frontend/src/NavTabs.tsx | 27 ++++++ images/backtest_worst_day.png | Bin 0 -> 168892 bytes 6 files changed, 313 insertions(+), 63 deletions(-) create mode 100644 demo_script.txt create mode 100644 frontend/src/NavTabs.tsx create mode 100644 images/backtest_worst_day.png diff --git a/README.md b/README.md index bf5e36d..ce87291 100644 --- a/README.md +++ b/README.md @@ -127,11 +127,37 @@ The live optimizer schedules against prices that have already happened (perfect - **Models:** a naive baseline (same half-hour yesterday), LightGBM (lagged prices and calendar features) and a PyTorch LSTM (the past 7 days of prices plus calendar features). Each model forecasts all 48 prices of day D using only data up to the end of D-1. A pytest test checks for leakage. - **Evaluation:** walk-forward. Hyperparameters are chosen on a 3-month validation window. The final 12 months are the test period, with models retrained every 30 days. Each day, the unchanged LP is run on the forecast, and the resulting schedule is scored at actual prices. -| Model | Share of perfect-foresight revenue (365 test days) | -|---|---:| -| Naive (yesterday) | 27% | -| LightGBM | 49% | -| LSTM | 62% | +### Results + +The test period ran from 12 Oct 2025 to 11 Oct 2026 (365 days). The battery was 5 MWh / 2.5 MW / 90% efficiency. With perfect foresight it would have earned **$560,314**. + +| Model | Capture rate | Realised revenue (AUD) | MAE (AUD/MWh) | RMSE (AUD/MWh) | Days with a loss | +|---|---:|---:|---:|---:|---:| +| Naive (yesterday) | 27.2% | 152,353 | 80.0 | 584.1 | 85 | +| LightGBM | 49.5% | 277,312 | 57.8 | 417.2 | 32 | +| **LSTM** | **61.8%** | **346,394** | **51.2** | **413.9** | **28** | + +*Capture rate = total realised revenue ÷ total perfect-foresight revenue over all test days.* + +- **The LSTM won.** It captured 62% of perfect-foresight revenue, against 49% for LightGBM and 27% for the naive baseline. It also had the lowest forecast error. It won every full quarter of the test year (49–68%) and only tied LightGBM in the 11-day partial quarter at the end. +- **Both ML models more than doubled the naive baseline's revenue** and had about a third as many losing days (28–32 vs 85). +- **Revenue depends on timing, not average accuracy.** South Australian revenue is dominated by a few extreme price spikes, so capture rates vary a lot from quarter to quarter. Getting the evening peak right matters far more than being accurate overnight. + +### When forecasts go wrong + +![Backtest page on the worst test day](images/backtest_worst_day.png) + +The Backtest page on the **worst test day, 18 May 2026**: + +- **What happened:** prices were unusually high from morning to early afternoon (averaging $214/MWh from 06:00 to 14:00), then fell to about $105/MWh over the evening. +- **What the models expected:** all three forecasts assumed a normal day, with a cheaper middle of the day and the usual 17:30 evening peak. +- **Result:** every model charged during the expensive daytime and discharged into the cheap evening. + +| | Perfect foresight | Naive | LightGBM | LSTM | +|---|---:|---:|---:|---:| +| Profit on 18 May 2026 | +$391 | −$649 | −$490 | −$360 | + +The LSTM lost the least, but it still lost money. A schedule fixed at the end of the previous day can't react when the day's pattern flips. The fix would be intraday re-optimisation using AEMO pre-dispatch forecasts (see [results/REPORT.md](results/REPORT.md) for the full limitations). To reproduce (results go to `results/`, see [results/REPORT.md](results/REPORT.md)): diff --git a/demo_script.txt b/demo_script.txt new file mode 100644 index 0000000..ae4e920 --- /dev/null +++ b/demo_script.txt @@ -0,0 +1,149 @@ +OptiGrid Demo Script +==================== + +Before you start +---------------- +- Open optigrid.seth-tang.me in a clean browser tab. +- Make sure you are on the live site, not a local screenshot. +- Be ready for the backend to take 5-10 seconds on the first run because the Render service has to wake up. + + +1. Opening pitch (30 seconds) +----------------------------- +Say: +"This is OptiGrid, a battery dispatch optimizer. It takes live wholesale electricity prices from AEMO -- the Australian Energy Market Operator -- and figures out the best times for a battery to charge and discharge to make the most money." + +Say: +"It is a classic energy arbitrage problem: buy electricity when it is cheap, store it, and sell it back when it is expensive." + + +2. Walk through the UI (1 minute) +--------------------------------- +Point to the left sidebar. + +Say: +"On the left we configure the battery. Right now it is set to 5 MWh of capacity and 2.5 MW max power output." + +Explain the two sliders: + +- Battery Capacity (5 MWh): the size of the energy tank. Maximum amount of electricity it can hold. +- Max Power Output (2.5 MW): the size of the pipe. Fastest rate it can charge or discharge. + +Say: +"Think of it like a water tank. Capacity is how big the tank is. Max power is how fast the tap can fill or empty it." + +Then point to the three cards on the right. + +Say: +"These three cards will show us the solver status, projected profit, and how much energy we moved through the battery." + + +3. Run the optimization (1 minute) +---------------------------------- +Click Run Optimization. + +Say: +"When I click this, the frontend sends the battery specs to a FastAPI backend. The backend fetches live AEMO spot prices from the Open Electricity API, resamples them to 30-minute intervals, and runs a linear programming solver to find the optimal schedule." + +Wait for the loading screen to finish. + +Say: +"This takes a few seconds because the backend service is waking up and fetching fresh market data." + +Once it finishes, the cards update: + +- Status: Optimal +- Projected Profit: e.g. $946.75 +- Energy Cycled: e.g. 23.2 MWh + +Say: +"Status Optimal means the solver found a feasible schedule. Projected profit is the total arbitrage profit over the next 24 hours. Energy cycled tells us how much energy we pulled from the grid into the battery." + + +4. Read the chart (2 minutes) +----------------------------- +Point to the chart. + +Say: +"The green line is the spot price in Australian dollars per megawatt-hour. The blue bars are the battery's State of Charge -- how full the battery is at each 30-minute interval." + +Then explain the pattern. + +Say: +"The basic strategy is simple: charge when prices are low or negative, discharge when prices are high." + +Use the 02:00 example. + +Say: +"At 02:00 the price is negative -- about minus fifteen dollars per MWh. That means the market is paying us to take electricity. So the solver charges the battery as fast as it can. That is why the blue bar jumps up." + +Say: +"At 01:30 the price is over a hundred dollars per MWh, so the battery is empty -- it sold everything it had." + +If the employer asks why the chart starts at 02:00: + +Say: +"The chart starts at 02:00 AEST because the Open Electricity API returns a fixed 24-hour window of AEMO data starting at that time. It is not aligned to when I clicked the button. In production I would make the datetime timezone-aware so the window matches the current time." + + +5. Explain the math briefly (1 minute) +-------------------------------------- +If they want to know how it works under the hood. + +Say: +"The optimizer is a linear program built with PuLP. The decision variables are charge, discharge, and state of charge for each 30-minute interval." + +Say: +"The objective is to maximize profit: sum of (discharge minus charge) times price times half an hour." + +Say: +"The constraints are physical: you cannot charge faster than 2.5 MW, you cannot store more than 5 MWh, and the state of charge has to balance from one interval to the next." + + +6. Mention the architecture (1 minute) +-------------------------------------- +Say: +"The stack is React with Tailwind on the frontend, FastAPI on the backend, and PuLP for the optimization. The backend pulls live prices from AEMO via the Open Electricity API. Everything is containerized and deployed through GitHub Actions to Render for the backend and S3 plus CloudFront for the frontend." + + +7. Be honest about limitations (1 minute) +----------------------------------------- +Say: +"A few things to keep in mind. This version assumes perfect foresight of prices, which is fine for a demo but not realistic. In production you would use price forecasts and Model Predictive Control." + +Say: +"It also only optimizes one region, SA1, and ignores things like battery degradation, transmission losses, and FCAS revenue. Those would be the next features to add." + + +8. Clarify what this dashboard is +--------------------------------- +If they ask whether this controls a real battery. + +Say: +"This is a planning and visualization dashboard. The solver produces a recommended dispatch schedule. In a real deployment that schedule would be sent as setpoints to the battery's control system or SCADA. The battery itself, through its inverter and battery management system, would actually execute it." + + +9. Closing line +--------------- +Say: +"So in short: this tool reads live market prices, runs an optimizer, and shows you when to charge and discharge a battery to maximize arbitrage profit." + + +Quick demo checklist +-------------------- +1. Open optigrid.seth-tang.me +2. Introduce the project +3. Explain capacity vs max power +4. Click Run Optimization +5. Explain Status, Profit, Energy Cycled +6. Walk through the chart +7. Mention the stack +8. Mention limitations +9. Clarify dashboard vs real BESS control + + +If something goes wrong +----------------------- +- Chart stays empty: wait a bit, then click Run Optimization again. The backend may still be cold-starting. +- Profit looks weird: prices change every day, so the number will be different each time you run it. +- Chart starts at 02:00: that is expected because of the API timezone handling. Use it as a chance to talk about production improvements. diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx index ad6f5fc..5e7d700 100644 --- a/frontend/src/App.tsx +++ b/frontend/src/App.tsx @@ -10,6 +10,7 @@ import { Tooltip, XAxis, YAxis } from 'recharts'; +import NavTabs from './NavTabs'; import type { ScheduleAction } from './types'; // Loading Overlay Component - Similar to Render's cold start page @@ -86,12 +87,14 @@ export default function App() { {/* LEFT SIDEBAR */}
    +
    + +

    Battery Dispatch

    SA1 Region Optimization

    - Forecast backtest →
    diff --git a/frontend/src/Backtest.tsx b/frontend/src/Backtest.tsx index 6f4dfed..0656077 100644 --- a/frontend/src/Backtest.tsx +++ b/frontend/src/Backtest.tsx @@ -1,4 +1,4 @@ -import { Loader2 } from 'lucide-react'; +import { ChevronLeft, ChevronRight, Loader2, TriangleAlert, Trophy, Zap, ZoomIn } from 'lucide-react'; import { useEffect, useMemo, useState } from 'react'; import { Bar, @@ -14,6 +14,7 @@ import { XAxis, YAxis } from 'recharts'; +import NavTabs from './NavTabs'; import type { BacktestDay, BacktestIndex, BacktestMethod, ForecastMethod } from './types'; const BASE_URL = import.meta.env.VITE_API_BASE_URL || "http://127.0.0.1:8000/api/v1"; @@ -106,7 +107,9 @@ export default function Backtest() { .then((r) => (r.ok ? r.json() : Promise.reject(new Error(`HTTP ${r.status}`)))) .then((idx: BacktestIndex) => { setIndex(idx); - setSelected(idx.best_day); + // #/backtest/YYYY-MM-DD opens that day; otherwise start on the best day + const fromUrl = window.location.hash.split("/")[2]; + setSelected(idx.days.some((d) => d.date === fromUrl) ? fromUrl : idx.best_day); }) .catch((e) => setError(`Could not load backtest results (${e.message}).`)); }, []); @@ -159,16 +162,25 @@ export default function Backtest() { if (dates.includes(value)) { setError(""); setSelected(value); + window.history.replaceState(null, "", `#/backtest/${value}`); } }; + const pos = dates.indexOf(selected); + const JUMPS = [ + { key: "best_day", label: "Best day", icon: Trophy, colour: "text-emerald-400", hint: `highest ${LABEL[index?.best_model ?? "lstm"]} capture` }, + { key: "worst_day", label: "Worst day", icon: TriangleAlert, colour: "text-red-400", hint: `lowest ${LABEL[index?.best_model ?? "lstm"]} capture` }, + { key: "biggest_spike_day", label: "Biggest spike", icon: Zap, colour: "text-amber-400", hint: "highest actual price" }, + ] as const; + const navButton = "p-2 rounded-lg border border-zinc-800 text-zinc-300 hover:bg-zinc-800 hover:text-white disabled:opacity-30 disabled:hover:bg-transparent cursor-pointer disabled:cursor-default transition-colors"; + return (
    {/* LEFT SIDEBAR */}
    +
    - ← Live simulation -

    Forecast Backtest

    +

    Forecast Backtest

    SA1 day-ahead forecasts scheduled by the same LP, scored on actual prices

    @@ -182,35 +194,49 @@ export default function Backtest() {

    Battery: {index.summary.battery.capacity_mwh} MWh / {index.summary.battery.max_mw} MW / {index.summary.battery.efficiency * 100}%

    -
    - - pickDate(e.target.value)} - className="w-full bg-zinc-900 border border-zinc-800 rounded-lg px-3 py-2 text-sm text-zinc-100 [color-scheme:dark]" - /> -
    - {([ - ["best_day", "Best day"], - ["worst_day", "Worst day"], - ["biggest_spike_day", "Biggest spike day"], - ] as const).map(([key, label]) => ( +
    + +
    + + pickDate(e.target.value)} + className="flex-1 min-w-0 bg-zinc-900 border border-zinc-700 hover:border-zinc-500 rounded-lg px-3 py-2 text-sm text-zinc-100 cursor-pointer [color-scheme:dark]" + /> + +
    +
    + +
    +

    Jump to

    + {JUMPS.map(({ key, label, icon: Icon, colour, hint }) => { + const active = selected === index[key]; + return ( - ))} -
    + ); + })}
    )} @@ -225,8 +251,45 @@ export default function Backtest() {
    )} + {/* whole-test-period summary first: it is the headline and does not move when a day loads */} + {index && ( +
    +
    +

    Capture rate across all {index.summary.Z_test_days} test days

    +

    + Realised revenue ÷ perfect-foresight revenue ({aud(index.summary.perfect_foresight_aud)}). Dashed line = 100%. +

    +
    + + + + Math.min(0, Math.floor(dataMin)), 100]} stroke="#52525b" fontSize={11} tickFormatter={(v) => `${v}%`} /> + + + + {captureBars.map((b) => )} + `${v}%`} /> + + + +
    + )} + + {index && !day && !error && ( +
    + Loading {selected}... +
    + )} + {day && ( <> +
    +

    + {day.date} · AEST, 30-minute intervals +

    + +
    + {/* per-day KPI cards */}
    {METHODS.map((m) => { @@ -235,7 +298,7 @@ export default function Backtest() { return (

    {LABEL[m]}

    -

    {aud(profit)}

    +

    {aud(profit)}

    {m === "perfect_foresight" ? "upper bound for this day" : pf > 0 ? `${Math.round((100 * profit) / pf)}% of perfect foresight` : "—"}

    @@ -245,41 +308,23 @@ export default function Backtest() {
    -
    -

    {day.date} (AEST, 30-minute intervals)

    - +
    +
    - - `${v}`} height={170} showXAxis={false} domain={index ? [0, index.summary.battery.capacity_mwh] : undefined} /> + `${v}`} height={170} showXAxis={false} domain={[0, index ? index.summary.battery.capacity_mwh : 5]} />
    )} - - {index && ( -
    -

    Capture rate across all {index.summary.Z_test_days} test days

    -

    - Sum of realised revenue ÷ sum of perfect-foresight revenue ({aud(index.summary.perfect_foresight_aud)}). Dashed line = perfect foresight (100%). -

    - - - - Math.min(0, Math.floor(dataMin)), 100]} stroke="#52525b" fontSize={11} tickFormatter={(v) => `${v}%`} /> - - - - {captureBars.map((b) => )} - `${v}%`} /> - - - -
    - )}
    ); diff --git a/frontend/src/NavTabs.tsx b/frontend/src/NavTabs.tsx new file mode 100644 index 0000000..5c78aa8 --- /dev/null +++ b/frontend/src/NavTabs.tsx @@ -0,0 +1,27 @@ +import { Activity, ChartLine } from 'lucide-react'; + +const TABS = [ + { href: '#/', label: 'Live', icon: Activity }, + { href: '#/backtest', label: 'Backtest', icon: ChartLine }, +] as const; + +// page switcher shown at the top of both sidebars +export default function NavTabs({ active }: { active: 'Live' | 'Backtest' }) { + return ( + + ); +} diff --git a/images/backtest_worst_day.png b/images/backtest_worst_day.png new file mode 100644 index 0000000000000000000000000000000000000000..aba8273048b128f26b12337c9c87ee77bc8d2a9b GIT binary patch literal 168892 zcmeFZcUTkM_b&me+~FU?5U^vjHqIiX$Sa00#?#eA|k3vq`b5w1%8vesTp|^ z5m9yh{Sf!LezzeaA{41BE4}tJ-(T|gd+?%-aNNMpGR<^NJ?fp2`fS|0yBp6uEFXR2 z4|q(1AsEW8WnSu)SA)s&;KP zyoW?7B=pn>>oq!Xjr`taOD;n0PU(-siV6j<$M}OEO@l5Cd8*1AbpMFe3Pj#vt>ui3 z`X9}7`q}dZc-2z 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