Forecast Factory Energy Demand and Variance Intervals with AutoGluon TimeSeries and Darts

"""
Production implementation for forecasting factory energy demand using AutoGluon TimeSeries and Darts.

This module provides secure, scalable operations for predicting energy demand and variance intervals based on historical data.
"""

from typing import Dict, List, Any
import os
import logging
from darts import TimeSeries
from darts.models import AutoGluon
from darts.utils import timeseries_generation as tg
from sqlalchemy import create_engine, text

# Setup logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///energy.db')

    def __init__(self):
        if not self.database_url:
            logger.error('DATABASE_URL not set')
            raise ValueError('DATABASE_URL must be set')

# Validate input data
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'timestamp' not in data or 'demand' not in data:
        raise ValueError('Missing required fields: timestamp and demand')
    return True

# Fetch data from the database
async def fetch_data(engine: Any) -> List[Dict[str, Any]]:
    """Fetch energy demand data from the database.
    
    Args:
        engine: Database engine
    Returns:
        List of records
    """
    with engine.connect() as conn:
        result = conn.execute(text('SELECT timestamp, demand FROM energy_demand'))
        return [{'timestamp': row[0], 'demand': row[1]} for row in result]

# Transform records into a TimeSeries object
async def transform_records(data: List[Dict[str, Any]]) -> TimeSeries:
    """Transform fetched records into a TimeSeries.
    
    Args:
        data: List of records
    Returns:
        Darts TimeSeries object
    """
    series = TimeSeries.from_dataframe(data, time_col='timestamp', value_cols='demand')
    return series

# Train the model using AutoGluon
async def train_model(series: TimeSeries) -> AutoGluon:
    """Train AutoGluon model on TimeSeries data.
    
    Args:
        series: Darts TimeSeries object
    Returns:
        Trained AutoGluon model
    """
    model = AutoGluon()  # Initialize AutoGluon model
    model.fit(series)
    return model

# Generate forecast
async def generate_forecast(model: AutoGluon, steps: int) -> TimeSeries:
    """Generate forecast for future time steps.
    
    Args:
        model: Trained AutoGluon model
        steps: Number of future steps to forecast
    Returns:
        Forecasted TimeSeries
    """
    forecast = model.predict(n=steps)
    return forecast

# Save forecast results to the database
async def save_to_db(engine: Any, forecast: TimeSeries) -> None:
    """Save forecast results to the database.
    
    Args:
        engine: Database engine
        forecast: Forecasted TimeSeries object
    """
    with engine.connect() as conn:
        for index, value in enumerate(forecast.values()):
            timestamp = forecast.time_index[index]
            conn.execute(text('INSERT INTO forecast_results (timestamp, forecast) VALUES (:timestamp, :forecast)'),
                         {'timestamp': timestamp, 'forecast': value})

# Main orchestrator class
class EnergyDemandForecaster:
    """Main class to orchestrate the energy demand forecasting workflow.
    """
    def __init__(self, config: Config):
        self.config = config
        self.engine = create_engine(self.config.database_url)

    async def run(self) -> None:
        """Execute the forecasting workflow.
        """
        try:
            logger.info('Starting energy demand forecasting process')
            data = await fetch_data(self.engine)  # Fetch data
            await validate_input(data)  # Validate input data
            series = await transform_records(data)  # Transform data
            model = await train_model(series)  # Train model
            forecast = await generate_forecast(model, steps=10)  # Generate forecast
            await save_to_db(self.engine, forecast)  # Save forecast
            logger.info('Forecasting process completed successfully')
        except Exception as e:
            logger.error(f'Error occurred: {str(e)}')

if __name__ == '__main__':
    config = Config()  # Load configuration
    forecaster = EnergyDemandForecaster(config)  # Initialize forecaster
    import asyncio
    asyncio.run(forecaster.run())  # Run main workflow