Chi-Tsun Cheng

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

The experimental results show that weekly scale look-back windows (LBW) provide an optimal balance between accuracy and computational cost for day-ahead horizons and 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.