High-accuracy calibrated quantile LightGBM model for forecasting Netherlands demand. Resolution: 15-minute intervals. Trained on multi-year data (2023–2026) from ENTSO-E, featuring multi-scale lags, cyclical encodings, and conformal calibration for well-calibrated 80% prediction intervals ($P10, P50, P90$).

Model Highlights

Country / Zone: Netherlands (NL)
Target: Electricity Demand in MW
Resolution: 15-minute intervals
Outputs: Point forecast ($P50$), 80% prediction interval ($P10$ to $P90$)
Algorithm: LightGBM Multi-Quantile Regressor with Conformal Calibration & Monotonicity
Dataset: ENTSO-E European Transparency Platform (Zone: NL)
Training Samples: 99,380 observations (2023–2026)

Performance & Benchmark Comparison

Evaluated on out-of-sample test sets against official seasonal persistence benchmarks:

Metric	LightGBM Forecaster	7-Day Seasonal Persistence	Improvement
MAE	228.837 MW	1655.395 MW	+86.2%
RMSE	760.680 MW	—	—

WAPE: 1.68% | P10 Pinball Loss | 147.679 | — | — | | P90 Pinball Loss | 54.656 | — | — | | P10–P90 Interval Coverage | 72.4% | — | Target: 75–85% | | Winkler Score | 2023.352 | — | — |

Quickstart: Python Inference

python

import pandas as pd
from huggingface_hub import hf_hub_download
import joblib

# 1. Download model artifacts
model_path = hf_hub_download(repo_id="ORGANIZATION/netherlands-demand-forecaster", filename="models.joblib")
models = joblib.load(model_path)

# 2. Predict P10, P50 (point), and P90 quantiles
X_test = pd.read_csv("sample_input.csv")
p10 = models[0.1].predict(X_test)
p50 = models[0.5].predict(X_test)
p90 = models[0.9].predict(X_test)

print("Forecast Point Estimate:", p50[:5])
print("80% Lower Bound (P10):", p10[:5])
print("80% Upper Bound (P90):", p90[:5])

Features Used

The model uses 37 leakage-safe features:

hour
quarter
day_of_week
day_of_year
month
is_weekend
is_holiday
is_morning_peak
is_evening_peak
sin_hour
cos_hour
sin_day_of_week
cos_day_of_week
sin_day_of_year
cos_day_of_year
lag_1h
lag_2h
lag_3h
lag_4h
lag_24h
lag_48h
lag_7d
lag_14d
diff_1h
diff_2h
diff_24h
diff_7d
acceleration_1h
ema_4step
ema_12step
rolling_std_4step
rolling_mean_24h
rolling_std_24h
rolling_min_24h
rolling_max_24h
rolling_mean_7d
rolling_std_7d

Intended Use & Advisory

This model is intended for research, energy market analytics, grid load planning, and educational forecasting demonstrations. It is advisory only and not intended for automated trading execution or real-time grid dispatch.

Citation & Attribution

Data published under the ENTSO-E Transparency framework:

Transparency Platform: https://transparency.entsoe.eu/

hsilvosa/netherlands-electricity-demand-forecaster

Electricity Demand Forecaster for Netherlands (NL)