Predicting the Energy Consumption of Commercial Buildings Based on Deep Forest Model and Its Interpretability

Building energy assessment models are considered to be one of the most informative methods in building energy efficiency design, and most of the current building energy assessment models have been developed based on machine learning algorithms. Deep learning models have proved their effectiveness in fields such as image and fault detection. This paper proposes a deep learning energy assessment framework with interpretability to support building energy efficiency design. The proposed framework is validated using the Commercial Building Energy Consumption Survey dataset, and the results show that the wrapper feature selection method (Sequential Forward Generation) significantly improves the performance of deep learning and machine learning models compared with the filtered (Mutual Information) and embedded (Least Absolute Shrinkage and Selection Operator) feature selection algorithms. Moreover, the Deep Forest model has an R2 of 0.90 and outperforms the Deep Multilayer Perceptron, the Convolutional Neural Network, the Backpropagation Neural Network, and the Radial Basis Function Network in terms of prediction performance. In addition, the model interpretability results reveal how the features affect the prediction results and the contribution of the features to the energy consumption in a single building sample. This study helps building energy designers assess the energy consumption of new buildings and develop improvement measures.

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