Prediction of functional zones cooling load for shopping mall using dual attention based LSTM: A case study

Abstract

Prediction of cooling load for each functional zone is essential for implementing an efficient and economic energy management plan in shopping mall. While previous research has been concerned with the overall cooling load prediction of the shopping mall, which could not provide a reasonable control strategy for the on-demand cooling of functional zone in shopping malls. To address this, we present a novel approach to predict cooling load of functional zones in shopping mall which first uses gray relational analysis methods to screen the key influencing factors of cooling load considering the characteristics of cooling load for different functional zones in shopping mall. It then applies LSTM with dual attention mechanism to optimize the output of the prediction model. Feature attention extracts important features through the analysis of the relationship between historical information and input variables autonomously, while temporal attention improves the prediction accuracy by analyzing the importance of load of each historical moment to the prediction of time load. We evaluate the performance of the proposed approach using real data sets from a shopping mall, which locates at Xi 'an, in the north of China. Experimental results show that cooling load prediction based on function zones may not only better satisfy the cooling load demand of diverse zones, but also ensure that chilled water is available on demand for air conditioning systems. Meanwhile, the short-time zonal cooling load prediction model based on dual-attention-LSTM has increased prediction accuracy, generalization ability, and stability when compared to previous prediction models.