Forecaster

The data_engine.forecaster package is dedicated to generating and managing time-series forecasts within the data engine. It provides the necessary tools to predict future data points based on historical trends, which is essential for proactive decision-making and optimization.

ForecastRetriever

This module contains the ForecastRetriever class, which is responsible for computing and managing forecasts, with a particular focus on non-controllable loads. It leverages historical data from InfluxDB and uses forecasting models like Prophet to generate accurate predictions.

Key Responsibilities:

• Forecast Generation: Computes forecasts for non-controllable loads.
• Historical Data Analysis:
  • Retrieves historical data for total power consumption and all controllable loads (e.g., thermostats, batteries, EVs, water heaters) from InfluxDB.
  • Calculates the historical non-controllable load by subtracting the consumption of controllable devices from the total consumption.
• Prophet Model Integration:
  • Training: Trains a Prophet forecasting model using the computed historical non-controllable load data. The model is configured with daily seasonality and a flat growth trend.
  • Prediction: Uses the trained model to generate future forecasts for a specified time period and interval.
• Data Persistence:
  • Saves the generated forecast back to InfluxDB for future use and analysis.
  • Stores the historical non-controllable loads in InfluxDB as well.
• Configuration Management:
  • Loads necessary configurations, such as InfluxDB bucket names and device information, from a Redis database.
• Output Formatting:
  • Returns the final forecast as a dictionary with timestamps as keys and forecasted values as values, making it easy to consume by other services.