Forecast Energy Grid Load with Moirai and Prophet

                      
                     
"""
Production implementation for Forecast Energy Grid Load.
This module forecasts energy consumption using Moirai and Prophet.
It includes data validation, transformation, and processing.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import pandas as pd
from prophet import Prophet
from sqlalchemy import create_engine, text
import time

# Logger setup for tracking the application's behavior.
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    moirai_api_url: str = os.getenv('MOIRAI_API_URL')

# Validate input data to ensure it meets the required structure.
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 'load' not in data:
        raise ValueError('Invalid input: Missing timestamp or load')
    return True

# Function to sanitize input fields before processing.
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields by stripping whitespace.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: value.strip() for key, value in data.items()}

# Transform raw data into a format suitable for modeling.
def normalize_data(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """Transform the list of records into a DataFrame.
    
    Args:
        data: List of raw input data
    Returns:
        Normalized DataFrame for forecasting
    """
    df = pd.DataFrame(data)
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    return df

# Function to perform batch processing of data.
def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of data for forecasting.
    
    Args:
        data: Batch of input data
    """
    for record in data:
        try:
            validate_input(record)
            sanitized_record = sanitize_fields(record)
            # Log the sanitized record
            logger.info(f'Sanitized Record: {sanitized_record}')
        except ValueError as e:
            logger.error(f'Input validation error: {e}')

# Function to fetch data from Moirai API.
def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from Moirai API.
    
    Returns:
        List of records fetched from the API
    Raises:
        ConnectionError: If the API call fails
    """
    try:
        response = requests.get(Config.moirai_api_url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Data fetched successfully from Moirai API')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise ConnectionError('Failed to fetch data from Moirai API')

# Function to save processed data to the database.
def save_to_db(data: pd.DataFrame) -> None:
    """Save DataFrame records to the database.
    
    Args:
        data: DataFrame containing processed records
    """
    engine = create_engine(Config.database_url)
    with engine.connect() as connection:
        data.to_sql('energy_load', connection, if_exists='append', index=False)
        logger.info('Data saved to database successfully')

# Function to format the output for display.
def format_output(data: pd.DataFrame) -> Dict[str, Any]:
    """Format DataFrame for output.
    
    Args:
        data: DataFrame to format
    Returns:
        Formatted output as a dictionary
    """
    return data.to_dict(orient='records')

# Main orchestrator class for the energy load forecasting process.
class EnergyLoadForecaster:
    """Class to handle the forecasting workflow.
    """
    def __init__(self) -> None:
        self.model = Prophet()

    def train_model(self, df: pd.DataFrame) -> None:
        """Train the Prophet model on the provided DataFrame.
        
        Args:
            df: DataFrame containing historical data
        """
        self.model.fit(df)
        logger.info('Model training completed')

    def make_forecast(self, periods: int) -> pd.DataFrame:
        """Make future forecasts based on the trained model.
        
        Args:
            periods: Number of periods to forecast
        Returns:
            DataFrame with forecasted values
        """
        future = self.model.make_future_dataframe(periods)
        forecast = self.model.predict(future)
        logger.info('Forecasting completed')
        return forecast

# Main function to orchestrate the workflow.
def main() -> None:
    """Main function to run the energy load forecasting process.
    """
    try:
        raw_data = fetch_data()  # Fetch raw data from API
        normalized_data = normalize_data(raw_data)  # Normalize raw data
        # Process the batch of data
        process_batch(raw_data)
        # Create an instance of the forecaster
        forecaster = EnergyLoadForecaster()
        # Train the model on the normalized data
        forecaster.train_model(normalized_data)
        # Forecast the next 30 days
        forecast = forecaster.make_forecast(periods=30)
        # Save the forecast to the database
        save_to_db(forecast)
        # Format the output for display
        output = format_output(forecast)
        logger.info(f'Forecast output: {output}')
    except Exception as e:
        logger.error(f'An error occurred: {e}')

if __name__ == '__main__':
    main()  # Run the main workflow