Electricity demand forecasting for Austin, TX, using a combination of timeseries methods and regression models

Use RL to balance the electrical power grid with electric vehicle fleets

This project will present an applied and game-like approach to simulating the load growth, investment decisions by two types of generation technologies, demand-price responsiveness, and reliability, of a test-case power system. The simulation begins as a 9-bus system with existing generation (3 generators) and transmission lines (8 lines). Syste…

Multi-User-Personality-Electricity-Load-Forecasting

Forecasting time series data with MLP by Google TensorFlow.

An educational application to predict the grid balance in Alberta. Capstone project for my Machine Learning and AI Bootcamp.

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

Tokyo-area electricity demand forecasting and anomaly monitoring using TEPCO data, automated ETL, and a GitHub Pages dashboard

This is my final year Capstone project . The aim of the project is to predict electricity usuage for the next hour and the next day based on previous data by implementing three models: ARIMA,MLP,ANFIS.

A study on energy demand forecasting based on smart meters data. The report and the presentation of the study are also provided in this repository.

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.

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.

Multi-User-Personality-Electricity-Load-Forecasting

Regime-adaptive probabilistic forecasting of NYISO Zone J (NYC) hourly electricity demand, with test-time recalibration that stays calibrated through the 2020 COVID shock, no retraining.

A Fuzzy system to predict the hourly electricity demand. Used triangular membership funcitons with 13 real world rules.

Electricity Load Forecasting

Exploring probabilistic time series methods for electricity demand forecasting

Statistical evaluation of renewable and non-renewable electricity generation in the EU.

Robust regression for electricity demand forecasting against cyberattacks

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