Water Consumption Forecasting Using Digital Twins and AI/ML

Overview

This project focuses on forecasting water consumption using machine learning (ML) and artificial intelligence (AI) models, leveraging data from Digital Twins implemented for various villages in Spain. Digital Twins provide daily real-time and historical data for water consumption, allowing for accurate predictions and enhanced resource management.

We implemented a digital twin system that collects data from various sources, such as water meters, meteorology stations, and programmable logic controllers (PLCs). The data is then processed using AI/ML models to predict water consumption and detect leakages.

Digital Twin Diagram

Below is a preview of the Digital Twin system diagram:

Project Structure

• Data Sources:

  • Historical Water Consumption Data: Collected from sensors and databases over time.
  • Real-time Water Consumption Data: Continuously monitored and collected daily.
  • Meteorological Data: Sourced from meteorology stations to enhance the accuracy of the predictions.

• Main Features:

  • Water Consumption Prediction: AI and ML models, including LSTM and Prophet, are used for forecasting daily water usage.
  • Leakage Detection: Early detection of water leakages by analyzing consumption patterns.
  • Energy Consumption and CO2 Footprint: Monitoring the energy impact of water distribution and associated CO2 emissions.

• Pre-Processing and AI/ML Pipeline: The project follows a well-defined data pipeline that includes:

  • Pre-processing Stage: Data cleaning, normalization, and preparation for analysis.
  • Analysis Stage: Applying models like LSTM and Prophet for time series forecasting.
  • Post-Processing Stage: Interpretation of the model outputs to provide actionable insights regarding water consumption and leakage detection.

Project Workflow

1. Data Input: Collection of historical and real-time water consumption data along with meteorological data.
2. Pre-Processing: Cleaning and preparation of the data for machine learning models.
3. AI/ML Processing: Applying LSTM and Prophet models to predict future water consumption and detect anomalies such as leakages.
4. Output: Predictions and analytics related to water usage, energy consumption, and environmental impact.

Models Used

• LSTM (Long Short-Term Memory): Used for time-series forecasting, particularly effective in handling sequential data.
• Prophet: A model designed for forecasting time series data, especially when the data exhibits strong seasonal effects.

Technologies

• Digital Twins: Simulating the water consumption of villages in Spain, providing a virtual representation for better decision-making and forecasting.
• AI/ML Techniques: Leveraging advanced machine learning algorithms to generate accurate forecasts and detect anomalies in water usage.

How to Run

1. Set up the Environment:

   • Install necessary dependencies by running:

     pip install -r requirements.txt
     

2. Run the Jupyter Notebook:

   • The core of the project is contained in the Jupyter notebook Water_Consumption_Forecasting.ipynb. Open it to explore the data and model workflow:

     jupyter notebook Water_Consumption_Forecasting.ipynb
     

Future Work

• Expand the data sources to include additional villages.
• Improve the models' accuracy by incorporating more environmental and socioeconomic data.
• Integrate real-time alerts for water leakage and unusual consumption patterns.

Contributors

• [Your Name]

License

This project is licensed under the MIT License - see the LICENSE file for details.