Attention-based interpretable neural network for building cooling load prediction

Ao Li, Fu Xiao, Chong Zhang and Cheng Fan

Applied Energy, 2021, vol. 299, issue C, No S0306261921006590

Abstract: Machine learning has gained increasing popularity in building energy management due to its powerful capability and flexibility in model development as well as the rich data available in modern buildings. While machine learning is becoming more powerful, the models developed, especially artificial neural networks like Recurrent Neural Networks (RNN), are becoming more complex, resulting in “darker models” with lower model interpretability. The sophisticated inference mechanism behind machine learning prevents ordinary building professionals from understanding the models, thereby lowering trust in the predictions made. To address this, attention mechanisms have been widely implemented to improve the interpretability of deep learning; these mechanisms enable a deep learning-based model to track how different inputs influence outputs at each step of inference.

Keywords: Cooling load prediction; Attention mechanism; Recurrent neural network; Interpretable machine learning; Building energy management (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (35)

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DOI: 10.1016/j.apenergy.2021.117238

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