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Time-series analysis of the electricity load consumption in the Mumbai Metropolitan Region.

Python 100.00%
exponential-smoothing-models moving-average deep-learning-models statistical-models sarimax arima lstm-model gru-model rnn-model keras-tensorflow

project-energy's Introduction

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

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