An open source software to model branch- and technology-specific electricity load profiles in the tertiary sector and analyse potentials of demand side response measures.

Code and data to replicate the analysis of the paper titled 'Role of residential air circulation and cooling for universal household electrification'

AI-powered electricity demand forecasting for Delhi Power Grid. Predicts demand at 5-min, hourly, and daily resolutions using 7 ML models (0.18% MAPE). Built with FastAPI, Next.js, LightGBM, XGBoost, PyTorch. Deployed on Vercel + Render + Supabase.

Build an Electricity Demand Prediction XGBoost ML Model in Python (Start-to-End Project)

Code to process, document data and analyse data from the Renewable Energy and the Smart Grid (GREEN Grid) project.

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.

Hybrid time series forecasting for PJME hourly energy load using Facebook Prophet and XGBoost. Includes feature engineering, cross-validation, and operational evaluation on realistic forecast horizons.

Short-term hourly electricity demand forecasting for the SOCO balancing authority using historical load, weather data, SARIMAX, Prophet, and XGBoost.

Multi-output forecasting of half-hourly electricity demand for France (regions + metropoles) from weather and calendar features: PyTorch MLP and gradient boosting.

A reproducible data pipeline for analyzing and managing electricity demand ramps in England & Wales (2009–2024). Integrates risk metrics, Monte Carlo hedging simulations, and ESG-aligned dashboards to support portfolio optimization and policy evaluation in the electricity market.

Predicting electricity demand using LSTM and Random Forest models. A Comparative study with load & weather data

Analysis of how humidity and temperature influence electricity demand across Australian states using AEMO and BOM data, with non-linear regression and interaction effects.

Python ML system forecasting India's hourly grid electricity demand — Ridge Regression (R²>0.90), time-series feature engineering, and live Streamlit dashboard

SIH 2024 prototype: Flask web app for electricity demand forecasting (daily/hourly/5-min) using SARIMAX with Visual Crossing weather data and Firebase auth. Superseded by Gridalytics.

Time series forecasting of electricity consumption (15-minute intervals) for single-day prediction

Économétrie des séries temporelles : étude de la demande d'électricité en Irlande. Analyse via ACF/PACF et tests de racines unitaires/ruptures (DF, ADF, ZA, LS).

Repo supporting the Renewable Energy and the Smart Grid (GREEN Grid) project

4th place at Datathon 2025 – Electricity demand forecasting model.

Electricity demand forecasting using time-series feature engineering and ML models (Linear Regression, Random Forest, XGBoost) with strong baseline comparison.

📊 Predict future product demand for e-commerce using machine learning, enhancing inventory planning to prevent overstocking and understocking.