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)

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.

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.

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

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

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

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

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

📈 Predict future product demand in e-commerce with machine learning models, optimizing inventory and minimizing overstocking or understocking risks.

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