Electricity demand forecasting for Austin, TX, using a combination of timeseries methods and regression models
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Updated
May 13, 2018 - Jupyter Notebook
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
Multi-User-Personality-Electricity-Load-Forecasting
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…
Forecasting time series data with MLP by Google TensorFlow.
Build an Electricity Demand Prediction XGBoost ML Model in Python (Start-to-End Project)
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.
An educational application to predict the grid balance in Alberta. Capstone project for my Machine Learning and AI Bootcamp.
Multi-User-Personality-Electricity-Load-Forecasting
Statistical evaluation of renewable and non-renewable electricity generation in the EU.
A Fuzzy system to predict the hourly electricity demand. Used triangular membership funcitons with 13 real world rules.
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.
Electricity Load Forecasting
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
Robust regression for electricity demand forecasting against cyberattacks
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