Forecast Energy Grid Load with Moirai and Prophet

                      
                     
"""
Production implementation for Forecast Energy Grid Load with Moirai and Prophet.
Provides secure, scalable operations to forecast energy demands based on historical data.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import pandas as pd
import numpy as np
from prophet import Prophet
import psycopg2
from psycopg2 import sql
from contextlib import contextmanager

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    retry_attempts: int = int(os.getenv('RETRY_ATTEMPTS', 3))

@contextmanager
def db_connection() -> psycopg2.extensions.connection:
    """
    Context manager for database connection.
    
    Yields:
        A database connection object.
    """
    conn = psycopg2.connect(Config.database_url)
    try:
        yield conn
    finally:
        conn.close()

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate request data for forecasting.
    
    Args:
        data: A dictionary containing input data to validate.
    Returns:
        True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'timestamp' not in data or 'load' not in data:
        raise ValueError('Missing required fields: timestamp, load')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields for security.
    
    Args:
        data: Input data dictionary.
    Returns:
        Sanitized data dictionary.
    """
    return {key: str(value).strip() for key, value in data.items()}

def fetch_data(query: str, params: Tuple) -> pd.DataFrame:
    """
    Fetch data from the database.
    
    Args:
        query: SQL query string.
        params: Tuple of parameters for the query.
    Returns:
        DataFrame containing fetched data.
    Raises:
        Exception: If database fetch fails.
    """
    try:
        with db_connection() as conn:
            df = pd.read_sql(query, conn, params=params)
            logger.info('Data fetched successfully.')
            return df
    except Exception as e:
        logger.error(f'Error fetching data: {str(e)}')
        raise

def save_to_db(data: pd.DataFrame) -> None:
    """
    Save forecast results to the database.
    
    Args:
        data: DataFrame containing forecast results.
    Raises:
        Exception: If saving fails.
    """
    query = sql.SQL("INSERT INTO energy_forecasts (timestamp, load_forecast) VALUES (%s, %s)")
    try:
        with db_connection() as conn:
            with conn.cursor() as cursor:
                cursor.executemany(query, data.values.tolist())
            conn.commit()
            logger.info('Data saved successfully.')
    except Exception as e:
        logger.error(f'Error saving data: {str(e)}')
        raise

def normalize_data(df: pd.DataFrame) -> pd.DataFrame:
    """
    Normalize data for forecasting.
    
    Args:
        df: Input DataFrame containing historical load data.
    Returns:
        Normalized DataFrame ready for forecasting.
    """
    df['load'] = (df['load'] - df['load'].mean()) / df['load'].std()  # Standard normalization
    return df

def transform_records(df: pd.DataFrame) -> pd.DataFrame:
    """
    Transform records for Prophet forecasting.
    
    Args:
        df: Input DataFrame containing normalized data.
    Returns:
        DataFrame formatted for Prophet.
    """
    df = df.rename(columns={'timestamp': 'ds', 'load': 'y'})
    return df[['ds', 'y']]

def call_api(data: Dict[str, Any]) -> Any:
    """
    Placeholder for external API call to fetch data.
    
    Args:
        data: Input parameters for the API call.
    Returns:
        Response from the API.
    """
    # Implement API call logic here
    pass

class EnergyForecast:
    """
    Main orchestrator class for forecasting energy load.
    """
    def __init__(self, data: Dict[str, Any]):
        validate_input(data)  # Validate input data
        self.data = sanitize_fields(data)  # Sanitize input data

    def forecast(self) -> pd.DataFrame:
        """
        Perform forecasting using Prophet.
        
        Returns:
            DataFrame containing forecast results.
        """
        logger.info('Starting forecast process.')
        historical_data = fetch_data("SELECT timestamp, load FROM energy_data ORDER BY timestamp DESC LIMIT 1000", ())  # Fetch historical data
        historical_data = normalize_data(historical_data)  # Normalize the data
        transformed_data = transform_records(historical_data)  # Transform for Prophet
        model = Prophet(yearly_seasonality=True)
        model.fit(transformed_data)  # Fit the model
        future = model.make_future_dataframe(periods=24, freq='H')  # Create future DataFrame
        forecast = model.predict(future)  # Predict future load
        logger.info('Forecasting completed.')
        return forecast[['ds', 'yhat']]

    def execute(self) -> None:
        """
        Execute the forecasting and save to DB.
        
        Raises:
            Exception: If any step fails.
        """
        try:
            forecast_results = self.forecast()  # Get forecast results
            save_to_db(forecast_results)  # Save results to DB
        except Exception as e:
            logger.error(f'Execution failed: {str(e)}')
            raise

if __name__ == '__main__':
    # Example usage
    sample_data = {'timestamp': '2023-10-01 00:00:00', 'load': 500}
    forecast = EnergyForecast(sample_data)
    forecast.execute()  # Execute the forecasting process