Predicting energy consumption of chiller plant using WOA-BiLSTM hybrid prediction model: A case study for a hospital building

Abstract

It is important to study building energy consumption considering the current state of global climate and its close relationship with building energy consumption. This study proposes a hybrid Whale Optimization Algorithm-Bidirectional Long Short-Term Memory (WOA-BiLSTM) model to predict energy consumption using a hospital building located in Beijing as a case study. First, a simulation is performed using building simulation software and the model is calibrated using energy bill and electricity meter data. Subsequently, the energy consumption of the calibrated model is decomposed to determine the energy consumption of the chillers and pumps. Finally, a BiLSTM model is established and the WOA is employed to enhance the generalization and performance of the BiLSTM model. Results show 1) for hour scale, the Mean Absolute Percentage Error (MAPE) of the WOA-BiLSTM model is reduced by 2.93 % and 0.75 % over the WOA-LSTM and BiLSTM models, respectively. 2) the time-lag effect of energy consumption is discussed and the results show that the optimal window size is approximately 18 h. 3) the optimization results of different iterations and population size of WOA show that performance is optimal when 400 iterations and a population size of 5, and larger datasets require more iterations based on a comparison of two datasets (hour and day scales).