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Sari95/
LSTM-for-Energy-Consumption-Prediction

Name: LSTM-for-Energy-Consumption-Prediction
Availability: InStock

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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:

LSTM with Energy consumption data and weather data
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:

Install Required Packages
Load Your Data
Preprocess the Data According to the Specifications
Run the Script

Sari95/LSTM-for-Energy-Consumption-Prediction