Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions

Traditional building energy prediction(BEP) methods usually solve time-series prediction problems using either recursive strategy or direct strategy, which may ignore time-dependence between continuous building energy data in building energy systems. To overcome this issue, a sequence-to-sequence(Seq2seq) model combined with attention mechanism(Seq2seq-Att) is developed to realize multi-step ahead BEP. Compared with the original Seq2seq, both parameter-tuning and attention mechanism in the Seq2seq-Att model have great impacts on BEP performance improvement. To obtain quantitative analyses of performance improvement of these two aspects, this study conducted a comprehensive performance evaluation of four Seq2seq models (i.e., before and after parameter-tuning, adding attention and without attention). In this study, the length of sliding window is 24-h and prediction time steps ranges from 1-h to 12-h ahead. From the open-source Building Data Genome Project 2 , 36 buildings are selected. Results indicate that adding attention to Seq2seq together with parameter-tuning, the multi-step ahead prediction performance can be increased by 8%(parameter-tuning around 6% while adding attention about 2%) on average. For prediction time step less than 3-h, parameter-tuning is a convenient way to improve the Seq2seq-based multi-step ahead BEP model. But for cases of prediction time step over 3-h, combining attention to the Seq2seq after parameter-tuning is recommended. • Evaluate Seq2seq and Attention on 36 buildings for multi-step short-term energy predictions. • Enhance Seq2seq multi-step prediction R 2 averagely by 8% (attention 2%, parameter-tuning 6%). • arameter-tuning is enough to enhance Seq2seq multi-step prediction for time-step<3-h ahead. • Recommend adding attention to Seq2seq after parameter-tuning for time-step>3-h ahead.