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--- |
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title: LSTM Model for Energy Consumption Prediction |
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description: >- |
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This model predicts energy consumption based on meteorological data and |
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historical usage. |
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license: gpl |
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--- |
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# LSTM for Energy Consumption Prediction |
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## Description |
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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. |
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## Model Details |
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**Model Type:** LSTM |
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**Data Period:** 2021-2023 |
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**Variables Used:** |
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1. LSTM with Energy consumption data and weather data |
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2. LSTM with Energy consumption data and two additional variables: 'Lastgang_Moving_Average' and 'Lastgang_First_Difference' |
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## Features |
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The model uses a sequence length of 192 (48 hours) to create input sequences for training and testing. |
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## Installation and Execution |
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To run this model, you need Python along with the following libraries: |
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- `pandas` |
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- `numpy` |
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- `matplotlib` |
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- `scikit-learn` |
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- `torch` |
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- `gputil` |
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- `psutil` |
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- `torchsummary` |
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### Steps to Execute the Model: |
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1. **Install Required Packages** |
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2. **Load Your Data** |
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3. **Preprocess the Data According to the Specifications** |
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4. **Run the Script** |
