https://github.com/akshadk7/production_forecasting
Time Series Forecasts for Energy Production using LSTM based RNNs
https://github.com/akshadk7/production_forecasting
forecasting lstm-neural-networks rnn-tensorflow time-series
Last synced: 4 months ago
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Time Series Forecasts for Energy Production using LSTM based RNNs
- Host: GitHub
- URL: https://github.com/akshadk7/production_forecasting
- Owner: AkshadK7
- License: mit
- Created: 2022-01-28T09:03:34.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-02-10T10:53:38.000Z (4 months ago)
- Last Synced: 2025-02-10T11:36:49.046Z (4 months ago)
- Topics: forecasting, lstm-neural-networks, rnn-tensorflow, time-series
- Language: Jupyter Notebook
- Homepage:
- Size: 133 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Energy Production Forecasting
Time Series Forecasts for Energy Production using LSTM-based RNNs.
## Overview
This project focuses on forecasting energy production using time series data and Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs). The model is trained to predict future energy outputs based on historical data.
## Repository Contents
- `Production_Forecast.ipynb`: Jupyter Notebook containing the data preprocessing steps, model architecture, training process, and evaluation metrics.
- `energy_produce_dataset.csv`: Dataset used for training and testing the model.
- `LICENSE`: MIT License file.
## Requirements
- Python 3.x
- Jupyter Notebook
- TensorFlow
- Pandas
- NumPy
- Matplotlib
## Setup Instructions
1. **Clone the Repository**:
```bash
git clone https://github.com/AkshadK7/Production_Forecasting.git
cd Production_Forecasting
```
2. **Install Dependencies**:
It's recommended to use a virtual environment to manage dependencies.
```bash
pip install -r requirements.txt
```
3. **Run the Jupyter Notebook**:
```bash
jupyter notebook Production_Forecast.ipynb
```
## Usage
- Open the `Production_Forecast.ipynb` notebook.
- Follow the steps outlined to preprocess the data, train the LSTM model, and evaluate its performance.
- Modify the notebook as needed to experiment with different model architectures or parameters.
## Results
The model's performance metrics and visualizations of the predictions versus actual energy production are provided in the notebook. These insights can help in understanding the model's accuracy and areas for improvement.
## License
This project is licensed under the MIT License. See the [LICENSE](https://github.com/AkshadK7/Production_Forecasting/blob/main/LICENSE) file for details.
## Acknowledgements
Special thanks to the contributors of the datasets and the open-source community for providing tools and libraries that made this project possible.
```
*Note: Ensure that the `requirements.txt` file includes all necessary dependencies for the project. If it doesn't exist, you may need to create it by listing the required packages.*