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'

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

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.

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

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.

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

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

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

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

Data Pipeline Automations with GitHub Actions (in VS-code Dev Containers)