Automated Machine Learning pipelines. Builds the Open Short Term Energy Forecasting package.

A curated list of awesome energy forecasting resources such as, code libraries, datasets, courses, tutorials, research papers, competitions and communities.

This study considers the prediction and forecasting of solar and wind power generation on a country-wide basis for the Greek energy grid.

Comparison of LSTM models and FCNN on Energy forecasting project using ASHRAE's data base for the Great Energy Predictor III competition on Kaggle.

A benchmark for energy forecasting with PatchTST, Autoformer, Informer, and classical baselines using the ETT Dataset

Multi-model ML forecasting for Home Assistant — benchmark, promote, and publish calibrated forecasts as HA sensors. Pi 5 ready.

Kernel quantile regression

End-to-end energy consumption forecasting for Portugal — XGBoost + conformal prediction (MAPE 1.44%), FastAPI, React/Vite, Prometheus metrics, deployed on HF Spaces.

Solar power production calculator from forecast data.

⚡ Short-term load forecasting using machine learning on the UK-DALE dataset. Predicts household power usage with scalable ML models.

Time-series forecasting of hourly energy consumption using Long Short-Term Memory (LSTM) neural networks. This project explores deep learning models for energy forecasting and compares their performance to traditional statistical models like ARIMA and SARIMA. Part of my transition into computational energy systems research.

This repository is about Hybrid Energy Forecasting and Trading Competition results performed by NICE_forecast.

Overview of how the market operates and EDA of some of the variables in it

AI-powered energy consumption forecasting system using machine learning, anomaly detection, and interactive dashboard for smart city and energy optimization applications.

A Python-based real-time dashboard for monitoring the Greek electricity grid, that fetches data from ENTSO-E Transparency Platform and OpenWeatherMap API.

Energy usage forecasting using Python (time series regression on smart meter data)

Most solar forecasters throw raw ML at the weather. This one doesn't - a clear-sky physics model + plane-of-array transposition set the baseline, and ML learns only the residual. Beats clear-sky and persistence (0.85 / 0.69 skill) on real data. Python, scikit-learn, live Streamlit demo.

AI-powered energy consumption forecasting system using machine learning to predict future usage patterns and optimize energy management.

Solving the "Adaptive Micro-Grid" challenge by addressing the failure of UET Mardan’s Smart Grid pilot. This project moves beyond failed global linear regression models to handle non-stationary, multi-modal energy data through context-aware segmentation (e.g., distinguishing between weekday peak loads and weekend inactivity).

Forecasting household energy consumption using XGBoost for smarter energy management.