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Workshop: I Can’t Believe It’s Not Better: Understanding Deep Learning Through Empirical Falsification

Dynamic Statistical Learning with Engineered Features Outperforms Deep Neural Networks for Smart Building Cooling Load Predictions

Yiren Liu · S. Joe Qin · Xiangyu Zhao · Yixiao HUANG · Shenglong Yao · Guo Han


Cooling load predictions for smart build operations play an important role in optimizing the operation of heating, ventilation, and air-conditioning systems. In this paper we report the cooling load prediction solution of real municipal buildings in Hong Kong set up by a recent AI competition. We show that dynamic statistical learning models with engineered features from domain knowledge outperform deep learning alternatives. The proposed solution for the global competition was conferred a Grand Prize and a Gold Award by the panel of internationally renowned experts. We report the data preprocessing based on cooling operation knowledge, feature engineering from control system knowledge, and interpretable learning algorithms to build the models. To find the best model to predict the cooling load, deep learning models with LSTM and Gated recurrent units are extensively studied and compared with our proposed solution.

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