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.
Build an Electricity Demand Prediction XGBoost ML Model in Python (Start-to-End Project)
An educational application to predict the grid balance in Alberta. Capstone project for my Machine Learning and AI Bootcamp.
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.
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.
Multi-User-Personality-Electricity-Load-Forecasting
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 Fuzzy system to predict the hourly electricity demand. Used triangular membership funcitons with 13 real world rules.
Statistical evaluation of renewable and non-renewable electricity generation in the EU.
Predicting electricity demand using LSTM and Random Forest models. A Comparative study with load & weather data
Exploring probabilistic time series methods for electricity demand forecasting
Electricity Load Forecasting
Robust regression for electricity demand forecasting against cyberattacks
📈 Predict future product demand in e-commerce with machine learning models, optimizing inventory and minimizing overstocking or understocking risks.
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