Efficacy of historical context and exogenous features on deep learning for cooling load forecasting in chilled water plants.

Abstract

Accurate day-ahead cooling load forecasting is a time series forecasting problem that is essential for optimizing the scheduling and energy efficiency of chilled water plants (CWPs). While previous studies have explored different forecasting models and input features, the combined effects of key factors such as historical context length (including regressor lags), sampling resolution, and exogenous inputs remain insufficiently examined for real industrial data. This study examines, under varying regressors and data granularity, the amount of historical context that provides stable and reliable forecasts with low computational cost. The experimental results show that (i) weekly scale look-back windows (LBW) provide an optimal balance between accuracy and computational cost for day-ahead horizons, (ii) hourly inputs often match or exceed finer-resolution accuracy and reveal distinct compute scaling: deep-learning (DL) models remain comparatively efficient as resolution increases, whereas the machine learning (ML) model, XGBoost, grows steeply, (iii) incorporating weather and calendar features enhances prediction accuracy around weekends and holidays. The DL model NHiTS, when integrating exogenous features and a 7-day LBW, improves forecasting accuracy by 50.8%, outperforming all other deep learning, machine learning, and statistical forecasting models by achieving a MASE of 1.88, compared to 3.82 for the baseline. These findings provide practical insights for smart-building and industrial applications, guiding practitioners such as building-energy engineers and facility managers in selecting optimal historical data lengths and parameter combinations to improve forecasting accuracy.