How accurate is EnergyMap's LightGBM demand model?

What this model is for #

27 of 36 Indian states and UTs do not publish realtime electricity demand to a stable public API. For grid-wide nowcasting and analytics, we need a uniform 15-minute demand series across every state — even where there is no live SLDC feed. We model demand for those states.

For the 11 states that do publish a usable realtime feed (Andhra Pradesh, Delhi, Gujarat, Himachal Pradesh, Karnataka, Kerala, Punjab, Rajasthan, Tamil Nadu, Telangana, West Bengal), we still maintain a parallel modelled curve. That sounds redundant — until you join across states. Each SLDC publishes at a different cadence, drops samples, and has its own timezone quirks. The modelled layer guarantees a gap-free 96-block grid every day, every state. The actuals stay the canonical source of truth where they exist; the model is the safety net.

The current production model is a per-state LightGBM gradient boosting regressor with the source label modeled_ml_v1. There is also a 7-day-ahead recursive forecast variant called modeled_ml_forecast_v1 using the same boosters. Code lives in scripts/train_demand_model.py (training, weekly cron) and scripts/run_demand_forecast.py (inference, every 15 minutes).

What the LightGBM trains on #

One booster per state. Same feature set across states, but each state learns its own response.

Targets are state demand_mw rows from the official SLDC scrape. Train/test split is rolling-time (no leakage). Validation is per-state with seasonal cross-validation on month boundaries.

Headline numbers #

We pair every 15-min model prediction with the closest actual SLDC reading within ±5 minutes, drop predictions without a paired actual, then aggregate. Last 30 days, all hours.

Four states cluster in the production-quality range — single-digit MAPE, R² above 0.78, and bias that's small relative to absolute load. Andhra Pradesh at 2.5% MAPE on a 10.8 GW typical load with R² = 0.91 is the best-performing state. Punjab is the most calibrated, with a mean bias of just +7 MW on a ~6.5 GW load — the model is essentially unbiased on average.

Andhra Pradesh deep dive — the 2.5% MAPE state #

A single day, three layers — actual SLDC reading, the LightGBM nowcast for the same instant, and the LightGBM's 7-day-ahead forecast issued before the day started:

Daily MAPE for AP, last 21 days — this is the stability story we care about. A model that's 2% one day and 12% the next isn't useful no matter what its mean is.

Predicted vs actual scatter, coloured by hour-of-day — shows where the model tracks the diagonal and where it diverges:

Where it misses #

MAPE broken out by hour of day for AP — this answers “when should I trust the model and when should I cross-check?”:

Residual distribution — predicted minus actual:

States not yet production-ready #

Two of the six states where the booster is currently emitting rows are flagged as not production-ready. We're keeping them visible — the data is there if you want to inspect it — but we don't advertise them as a shipped product. The other five OFFICIAL_FEED_STATES (Karnataka, Rajasthan, Tamil Nadu, Telangana, West Bengal) have boosters trained but not yet deployed to production inference.

Himachal Pradesh in particular is interesting: the booster trained, the inference pipeline runs, but the absolute load is so small (~310 MW peak) that any over-prediction looks catastrophic in percentage terms. The fix is per-state target normalisation, queued as part of the next training run.

Reproduce these numbers #

You can reproduce every number on this page two ways: pull our metrics snapshot directly, or hit the live API and re-run the comparison yourself. The CodeSamples below copy-paste cleanly into a fresh terminal — set API_KEY first.

# requires: pip install requests
import os, requests
r = requests.get(
    "https://www.energymap.in/blog/lightgbm/metrics.json",
    headers={"User-Agent": "energymap-readme"},
    timeout=10,
)
r.raise_for_status()
data = r.json()
print(f"window_days={data['window_days']}, hero_state={data['hero_state']}")
for s in data["states"]:
    if s["mape_pct"] is not None:
        print(f"{s['state']:<18} MAPE={s['mape_pct']:>5.2f}%  R²={s['r2']:.3f}")

# requires: pip install requests
import os, requests
API_KEY = os.environ["API_KEY"]
r = requests.get(
    "https://api.energymap.in/api/intelligence/state-demand",
    params={"state": "andhra-pradesh", "source": "modeled_ml_v1"},
    headers={"Authorization": f"Bearer {API_KEY}"},
    timeout=15,
)
r.raise_for_status()
print(r.json()["count"], "blocks returned")

# requires: pip install requests
import os, requests
API_KEY = os.environ["API_KEY"]
r = requests.get(
    "https://api.energymap.in/api/intelligence/state-demand-forecast",
    params={"state": "andhra-pradesh", "horizon_days": 7},
    headers={"Authorization": f"Bearer {API_KEY}"},
    timeout=15,
)
r.raise_for_status()
print("first block:", r.json()["forecast"][0])

Or grab the raw artefacts that back this post — the per-state metrics, the JSON variant, and the Cloudinary plot manifest:

Want to re-run the full evaluation script against the database? It is scripts/eval_demand_models.py in the atlas backend repo:

pip install psycopg[binary] sqlalchemy matplotlib pandas numpy
export DATABASE_URL='postgresql://USER:PASS@HOST:25060/grid?sslmode=require'
python scripts/eval_demand_models.py --out eval-output/ --days 30

Limitations & open work #

• Historical depth. Most clean SLDC data starts September 2025. The booster has seen one summer and one winter. Expect material seasonal-bias correction once we have a full annual cycle plus a year of holdout for validation.
• No real-time signal in the model. demand_lag_24h and demand_lag_7d are observations from the past. Anything unusual today (heatwave, regional outage, tariff change) only enters via weather and the calendar — not directly. The model under-predicts on high-stress days and we have an open ticket to add a 1-hour lag with proper feature freshness handling.
• Hourly weather resolution. Open-Meteo gives 1-hour temperature; AP morning ramp can swing 500+ MW within a single hour. We're effectively low-pass filtering temperature. A 15-min weather feed would close most of the daytime MAPE gap.
• No cross-state spillover signal. Each state's model knows All-India demand and its own lags, but not what's happening live in neighbouring states. Pooled multi-state models on the next iteration.
• 5 of 11 trained states not yet emitting predictions. Karnataka, Rajasthan, Tamil Nadu, Telangana, and West Bengal have trained boosters but the inference cron is not yet writing rows for them. Tracked internally; expect them in production by the end of the next sprint.

Computed from 3,823 matched 15-minute samples over the last 30 days. Snapshot generated 3 May 2026. Code: scripts/eval_demand_models.py.