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

Kernel quantile regression

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

Solar power production calculator from forecast data.

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.

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)

Optimized demand forecasting using time series modeling with Prophet and NeuralProphet. Includes autoregressive memory, holiday effects, time-aware cross-validation, and hyperparameter tuning. Delivers interpretable, multi-horizon predictions for short-term accuracy and long-term grid planning purposes.

A machine learning project to predict household energy consumption using historical smart meter data. Implements data preprocessing, XGBoost regression, and performance evaluation with RMSE, MSE, and MAE metrics.

⚡ Time series forecasting of energy demand using machine learning

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.

Energy Consumption Forecasting System - Infosys Springboard AI Internship Final Project

Energy Consumption Forecating using Stack LSTM, hosted online with Binder

This is a time-series forecast project I did with Max Wang during the Summer 2022 to benchmark Facebook AI's Neuroprophet along other forecasting techniques.