Improving short-term HVAC load forecasting via temporal feature engineering: insights from the PLEIAData building dataset

Building energy consumption accounts for a significant portion of global energy use, with heating, ventilation, and air conditioning (HVAC) systems representing one of the largest contributors to building energy demand. Accurate short-term HVAC load forecasting is therefore essential for efficient building operation, demand-response strategies, and intelligent energy-management systems.This study investigates the impact of temporal feature representation on one-hour-ahead HVAC load forecasting using real operational data from the PLEIAData building dataset. Three forecasting models representing both machine learning and deep learning paradigms—Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—are evaluated under progressively enriched temporal feature configurations. The experiments systematically compare baseline environmental variables, autoregressive load features, rolling statistical representations, and extended temporal feature sets designed to capture recent HVAC operational dynamics.The results demonstrate that models relying only on contemporaneous environmental variables exhibit limited predictive capability, whereas incorporating temporal load-history information substantially improves forecasting accuracy. Extended temporal representations based on autoregressive and rolling statistical features consistently enhance model performance across all forecasting architectures. Among the evaluated configurations, XGBoost combined with the extended temporal feature representation achieves one of the best and most stable forecasting performances with an R² value of approximately 0.799. Additional ablation and interpretability analyses further show that recent temporal load information is the primary contributor to forecasting performance, while extending the feature space with increasingly complex rolling statistics and larger temporal representations provides only marginal additional gains. Overall, the study highlights the importance of temporal feature design, robustness-oriented evaluation, and interpretable forecasting analysis in HVAC load prediction applications.