Sari95/LSTM-for-Energy-Consumption-Prediction

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