Skip to content

README.md

Latest commit

 

History

History
144 lines (97 loc) · 5.27 KB

README.md

File metadata and controls

144 lines (97 loc) · 5.27 KB

Energy consumption forecasting

Overview

This project focuses on modeling and predicting energy consumption using a variety of machine-learning techniques. It involves loading, preprocessing, and exploring energy consumption data, engineering relevant features, building predictive models, and evaluating their performance. The project also implements a Flask API to serve the model evaluation results.

File Structure

├── app.py                       # Flask application for serving model results via API
├── data_preprocessing.py        # Data loading, preprocessing, feature engineering, and visualization
├── model_building.py            # Model training, evaluation (including LSTM), and time series cross-validation
├── energy_consumption_data.csv  # The dataset used for training and evaluation
├── requirements.txt             # List of Python dependencies for the project
└── README.md                    # This file (project overview and setup instructions)

Dependencies

The project relies on the following Python libraries:

  • pandas: For data manipulation and analysis
  • scikit-learn: For various machine learning models and evaluation metrics
  • tensorflow/keras: For building and training the LSTM model
  • Flask: For creating the web API
  • numpy: For numerical computations

To install all required dependencies, it is recommended to use a virtual environment and run:

pip install -r requirements.txt

A sample requirements.txt is shown below:

flask==3.1.0
pandas==2.2.3
scikit-learn==1.6.1
tensorflow==2.18.0
numpy==2.02
seaborn==0.13.2
matplotlib==3.10.1

Usage

  1. Clone the repository:

    git clone https://github.com/hydrogeohc/Energy-forecasting-API
cd Energy-forecasting-API

  2. Create a virtual environment:

    python3 -m venv venv  # create the virtual environment
source venv/bin/activate   # activate on macOS/Linux
# venv\Scripts\activate   # activate on Windows

  3. Install dependencies:

    pip install --upgrade pip # upgrade pip
pip install -r requirements.txt # Install the dependencies

  4. Run the Flask API:

    python app.py

  5. Access the API endpoints:

    • http://127.0.0.1:5000/ - Returns a welcome message.
    • http://127.0.0.1:5000/model_results - Retrieves model evaluation results in JSON format. Note: The AirPlay Receiver service on macOS may conflict with port 5000. If you encounter issues, try changing the Flask port to 5001 in app.py and accessing http://127.0.0.1:5001/model_results.

Models

The following machine learning models are implemented and evaluated in this project:

  • Linear Regression
  • Random Forest
  • Gradient Boosting
  • LSTM (Long Short-Term Memory)

Note: The traditional models (Linear Regression, Random Forest, and Gradient Boosting) are evaluated using Time Series Cross-Validation. However, the current implementation evaluates the LSTM using a single train/test split. The LSTM results are therefore not directly comparable to the other models.

Data

The energy_consumption_data.csv file contains the historical energy consumption data used to train and evaluate the models. The dataset includes features such as:

  • timestamp: Date and time of the reading
  • temperature: Temperature at the time of the reading
  • humidity: Humidity at the time of the reading
  • energy_consumption: The target variable, representing the energy consumed

The project also engineers new features such as:

  • temp_humidity_interaction: Interaction term between temperature and humidity
  • energy_consumption_lag1: Energy consumption from the previous time period (lag 1)
  • energy_consumption_lag2: Energy consumption from two time periods prior (lag 2)
  • hour, day_of_week, is_weekend: Temporal features derived from the timestamp.

Evaluation Metrics

The models are evaluated based on the following metrics:

  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Bias

Model Selection

Model Evaluation Results

The following table presents the model evaluation results, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Bias:

Model MSE MAE Bias
Random Forest 14245.34 104.40 -8.63
Gradient Boosting 15696.48 106.75 -9.58
Linear Regression 16427.58 109.70 -12.03
LSTM 18726.25 109.30 -18.30

Base on the above results, the Random Forest seems to be the best with all the lower MSE, MAE, and closeer to zero bias.

  • note:
  • MSE might be the most important metric when the project wants to avoid large errors
  • MAE might be preferred when the project want a model that's generally accurate
  • Bias is important when the project need a model that doesn't systematically over- or under-predict.

Troubleshooting

  • ModuleNotFoundError: Ensure that all Python dependencies are installed in your virtual environment. If you see the error, install the libraries that are missing.
  • File Permissions: Make sure that the energy_consumption_data.csv file is readable by the Flask application.

License

MIT License