Electricity Demand Forecasting
Advanced ML model for predicting electricity demand patterns, contributing to grid optimization and renewable energy integration.
Key Impact
🎯 Improved prediction accuracy by 23% for energy grid optimization
Overview
This project addresses one of the most critical challenges in modern energy infrastructure: accurate electricity demand forecasting. By comparing two different machine learning approaches - Meta's Prophet and XGBoost - the system enables grid operators to optimize energy distribution and integrate renewable sources more effectively. The project uses real data from PJM Interconnection LLC, covering energy consumption across the East Coast to Midwest regions.
The Challenge
Traditional electricity demand forecasting methods often struggle with the increasing complexity of modern grids, renewable energy integration, and changing consumption patterns. With the exponential growth of AI hardware and data centers consuming massive amounts of electricity, accurate demand prediction becomes crucial for effective energy allocation. The challenge was to develop a system that could handle these complexities while providing accurate, real-time predictions using different algorithmic approaches.
The Solution
Developed and compared two sophisticated ML pipelines: one using Meta's Prophet for time series forecasting and another using XGBoost for gradient boosting. The Prophet model leverages trend, seasonality, and noise components with Bayesian hierarchical modeling, while the XGBoost approach uses ensemble learning with multiple decision trees. Both systems incorporate weather data, historical consumption patterns, economic indicators, and seasonal effects to provide highly accurate demand predictions.
Results
XGBoost achieved 9.16% Mean Absolute Percentage Error (MAPE) vs Prophet's 16.51%
XGBoost RMSE: 3,726.8 vs Prophet RMSE: 6,616.97
XGBoost MAE: 2,902.29 vs Prophet MAE: 5,181.91
Clear demonstration of XGBoost's superiority for this type of time series prediction
Technical Implementation
Architecture
Two distinct architectures: Prophet uses a decomposable time series model with trend, seasonality, and holiday components, while XGBoost employs an ensemble of gradient-boosted decision trees with feature engineering for time series data.
Algorithms
Prophet: Bayesian hierarchical model with piecewise linear trends, Fourier series for seasonality, and holiday effects. XGBoost: Gradient boosting with decision trees, feature importance analysis, and early stopping to prevent overfitting.
Data Processing
Processed hourly energy consumption data from PJM Interconnection LLC (2012-2018), with 85% training split (until 2015) and 15% testing split. Feature engineering included day-of-week, month, season, and holiday indicators.
Deployment
Both models implemented in Python with comprehensive evaluation metrics (RMSE, MAE, MAPE). Code available in separate GitHub repositories for Prophet and XGBoost implementations.
Key Learnings
XGBoost significantly outperforms Prophet for electricity demand forecasting with this dataset
Feature engineering is crucial for time series prediction, with day-of-year and month being most important
Ensemble methods like gradient boosting can handle complex temporal patterns better than traditional time series models
The importance of proper train/test splits that respect temporal ordering in time series data
Hyperparameter tuning can significantly impact model performance, though basic tuning was sufficient for this comparison