Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings

Building energy consumption prediction plays a key role in energy-efficiency decision making. With the advancement in data analytics, a number of machine learning-based building energy consumption prediction models have been developed in recent years. However, existing prediction models do not sufficiently take occupant behavior into account. Towards addressing this gap, this paper presents a machine-learning approach for predicting building energy consumption in an occupant-behavior-sensitive manner. In this approach, a model learns from a large set of energy-use cases that were modelled and simulated in EnergyPlus. The machine-learning prediction model was trained using a large dataset that includes 3-month hourly data for 5760 energy-use cases representing different combinations of building characteristics, outdoor weather conditions, and occupant behaviors. In developing the model, four machine-learning algorithms were tested and compared in terms of their prediction accuracy and computational efficiency: classification and regression trees (CART), ensemble bagging trees (EBT), artificial neural networks (ANN), and deep neural networks (DNN). The simulation results demonstrated the high impact of the variables considered in this study. For example, the highest energy-consuming case consumed over 3432 times more energy than the lowest-consuming case. Occupant behavior made a difference up to over 7 times in energy consumption. The DNN model with four hidden layers achieved 2.97% coefficient of variation (CV). Such high performance shows the potential of the proposed approach. The approach could help better understand the impact of occupant behavior on building energy consumption and identify opportunities for behavioral energy-saving measures.