LSTM-Power-Demand-Forecasting-Smart-Grid

This repository contains code implementing a Long Short-Term Memory (LSTM) neural network model for Power Demand Forecasting. Inspired by the techniques outlined in my paper titled "Power Demand Forecasting using LSTM"

Key Features:

1. LSTM-based neural network for power demand prediction.
2. Time series data preprocessing and feature engineering.
3. Training, validation, and testing procedures for model evaluation.
4. Visualization tools for analyzing model performance.

How to Use:

1. Ensure you have the required dependencies installed, please refer to the "requirements.txt" file.
2. Refer the following .ipynb file "LSTM-Power-Demand-Forecasting.ipynb" for the code and details on how to the code works.
3. Preprocess your time series data, try using different values for lookback.
4. Train the LSTM model using the provided scripts, try modifying the layers.
5. Evaluate the model's performance on a validation set.
6. Use the trained model for making power demand forecasts.