project-energy

This project is for analysing the time-series algorithms via a study of electricity load forecasting on univariate and multivariate datasets in the MM Region using various classical/moving averages (SMA, WMA, CMA, EMA), exponential smoothing methods (SES, DES, TES), statistical models (ARIMA, SARIMAX) & DL models (LSTM, GRU, RNN) for univariate and multivariate datasets using Keras API

Datasets

It is divided into two primary datasets:

• Univariate dataset having only the load
• Multivariate dataset having weather (Temperature, Dew Point, Humiditiy, Wind Speed, Pressure) as well as the load

Models

Each of the dataset forecasts using the following methods:

• Classical Methods: SMA, WMA, CMA, EMA
• Exponential Methods: SES, DES, TES
• Deep Learning Models: LSTM, GRU, RNN
• Statistical Models: ARIMA, SARIMAX