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---
title: LSTM Model for Energy Consumption Prediction
description: >-
This model predicts energy consumption based on meteorological data and
historical usage.
license: gpl
---
# 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** |