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README.md

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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**