XGBoost-based Energy Demand Forecasting System: Time Series Feature Engineering and Machine Learning Applications in Energy Management

This article analyzes an open-source energy demand forecasting system based on XGBoost and time series feature engineering, exploring its technical value and application prospects in smart grids, energy dispatching, and sustainable development. Addressing challenges such as supply-demand balance in modern energy systems and fluctuations in renewable energy output, the system provides accurate short-to-medium-term forecasts to support refined management of power systems.

Modern energy systems face multiple challenges: supply-demand balance pressure (real-time matching of power to demand), renewable energy fluctuations (uncertain output from wind and photovoltaic power), demand-side changes (e.g., popularization of electric vehicles), and marketization of electricity prices (forecast accuracy affects revenue). Energy demand forecasting is divided into ultra-short-term (minute-level), short-term (hour to day), medium-term (week to month), and long-term (year to decade). This project focuses on short-to-medium-term forecasting.

XGBoost Algorithm Principles: Ensemble learning idea (serial training of weak learners), regularization mechanism (L1/L2 to prevent overfitting), second-order Taylor expansion optimization, parallel processing, automatic missing value handling.

Time Series Feature Engineering: Time features (hour/week/holiday, etc.), lag features (electricity consumption in the past 1/24/168 hours), sliding window statistics (mean/standard deviation, etc.), difference features (first-order difference), external variable integration (meteorological/economic/calendar events).

Data Processing Flow: Data acquisition (smart meters/SCADA/public data), cleaning (outlier/missing value handling), feature construction, time-series split (training/validation/test sets).

Model Training and Tuning: Hyperparameter optimization (learning rate/number of trees/depth, etc.), time-series cross-validation, evaluation metrics (MAE/RMSE/MAPE/R²).

Streamlit Deployment: Real-time forecast display (comparison between historical and future data), model diagnosis (feature importance/residual analysis), interactive exploration (parameter adjustment/scenario simulation).

Grid Dispatching Optimization: Unit commitment optimization, reserve capacity allocation, renewable energy absorption.

Power Market Transactions: Day-ahead market bidding, real-time market arbitrage, demand response management.

Energy Planning Decisions: Distribution network expansion, distributed energy configuration, electric vehicle charging facility planning.

Challenges: Data quality (missing values/outliers/granularity differences/concept drift), model limitations (insufficient nonlinear capture/weak extreme event prediction/multi-step error accumulation).

Improvement Strategies: Deep learning integration (LSTM/CNN/Transformer), ensemble forecasting framework (ARIMA+XGBoost+LSTM), probabilistic forecasting (quantile regression/Bayesian deep learning), external data integration (satellite remote sensing/social media/macroeconomics).

Support for Carbon Neutrality Goals: Improve renewable energy utilization, optimize energy storage dispatching, support demand-side management.

Socio-Economic Benefits: Reduce electricity costs, improve power supply reliability, promote energy equity (support electrification in remote areas).

The XGBoost-based energy demand forecasting system demonstrates the application potential of machine learning in the traditional energy industry, providing reliable decision support for grid operation, market transactions, and energy planning. In the future, it will become more intelligent, probabilistic, and multi-modal, integrating physical models with data-driven methods to help build a clean, low-carbon, safe, and efficient modern energy system.