Here, the most popular Electric Load Forecasting datasets are collected centrally. Feel free to support this work. 🔥
-
Updated
Apr 27, 2026 - Python
Here, the most popular Electric Load Forecasting datasets are collected centrally. Feel free to support this work. 🔥
This repo contains data and code for Task-Aware Machine Unlearning with Application to Load Forecasting.
Grid-Aware STGNN for Multi-horizon Power Load Forecasting
This is the official repo for the paper E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning, to be appeared in AAAI-24.
Implementation of two different models (TF2/Keras) from literature and a custom model for day-ahead load forecasting (short term load forecasting) on two different datasets.
Official repository for the ParDeeB framework and the Shahrekord Energy Dataset: A high-resolution 4-year hourly benchmark (30,000+ samples) featuring 23 meteorological and temporal determinants for short-term load forecasting.
Hourly electricity load forecasting (AEP) with XGBoost - calendar + lag features, baseline comparison, recursive 24h forecast, and a Streamlit demo.
Public model cards (Voltcrown v7.9 + lineage) and a dependency-light forecasting toolkit for the ENTSO-E load-forecast challenge — with OpenSSF Scorecard hardening.
Professional energy data science portfolio — wildfire risk modeling, grid investment optimization, load forecasting, and energy economics research tools built for utility infrastructure analytics.
AI-powered electricity load forecasting and grid demand planning for Southern California. SARIMA time series forecasting, EV adoption and solar penetration scenario builder, and executive capacity planning dashboard.
Hybrid quantum-classical XGBoost experiments for short-term electricity load forecasting in power systems.
Context-aware power load forecasting system using XGBoost + Optuna hyperparameter tuning. Predicts 15-minute interval electricity consumption by training separate models per season, time-of-day period, and day type (weekday/weekend). Outputs results to formatted Excel with per-day sheets and tuning history.
Production XGBoost quantile forecasters for the German day-ahead market. The load model beats the TSO's published forecast by 21 % across a 14-month holdout.
Add a description, image, and links to the load-forecasting topic page so that developers can more easily learn about it.
To associate your repository with the load-forecasting topic, visit your repo's landing page and select "manage topics."