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LSTM for Energy Consumption Prediction

Description

This model applies Long Short-Term Memory (LSTM) architecture to predict energy consumption over a 48-hour period using historical energy usage and weather data from 2021 to 2023.

Model Details

Model Type: LSTM
Data Period: 2021-2023
Variables Used:

  1. LSTM with Energy consumption data and weather data
  2. LSTM with Energy consumption data and two additional variables: 'Lastgang_Moving_Average' and 'Lastgang_First_Difference'

Features

The model uses a sequence length of 192 (48 hours) to create input sequences for training and testing.

Installation and Execution

To run this model, you need Python along with the following libraries:

  • pandas
  • numpy
  • matplotlib
  • scikit-learn
  • torch
  • gputil
  • psutil
  • torchsummary

Steps to Execute the Model:

  1. Install Required Packages

  2. Load Your Data

  3. Preprocess the Data According to the Specifications

  4. Run the Script

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