Renewable Energy Demand Forecasting Using Time Series

This project provides a solution for forecasting renewable energy demand using time series models, particularly Long Short-Term Memory (LSTM) networks.

Overview

• Task: Time Series Forecasting of renewable energy demand.
• Model: LSTM (Long Short-Term Memory) Neural Network.
• Data: Historical energy demand data.
• Libraries: TensorFlow, Keras, Pandas, Matplotlib, scikit-learn.

Project Structure

• data.csv: CSV file containing historical energy demand data.
• prepare_data(): Function to prepare the time series dataset.
• build_lstm_model(): Function defining the LSTM model.
• train_test_split(): Split data into training and testing sets.
• train_model(): Function to train the model.
• forecast(): Function to make future predictions.

How to Run

1. Prepare a dataset (data.csv) with timestamps and demand values.
2. Load and preprocess the dataset.
3. Train the LSTM model.
4. Evaluate and forecast future energy demand.

Requirements

• Python 3.8+
• TensorFlow 2.x
• Pandas
• Matplotlib
• scikit-learn

Install dependencies:

pip install tensorflow pandas matplotlib scikit-learn

Output

• Trained LSTM model for demand forecasting.
• Forecasted energy demand plot.

Author

Okes Imoni

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Feel free to fork and contribute to the project!