A hybrid deep learning-based method for short-term building energy load prediction combined with an interpretation process
Data driven-based building energy load prediction is of great value for building energy management tasks such as fault diagnosis and optimal control. However, there are two challenges for conventional data driven-based prediction methods. The first challenge is that time-lag measurements such as historical cooling loads still cannot be taken full advantage of. To deal with this challenge, a hybrid prediction method is proposed based on long short-term memory networks and artificial neural networks. The second challenge is that data driven-based models are hard to explain by domain knowledge. To deal with this challenge, an interpretation method is proposed based on a dimensionless sensitivity index and a weighted Manhattan distance. Operation data of a public building are utilized to evaluate the proposed methods. Results show that the proposed hybrid prediction method has higher prediction accuracy than conventional prediction methods in one-hour-ahead cooling load prediction. Crucial factors affecting building cooling loads are revealed successfully based on the proposed sensitivity index. Moreover, the weighted Manhattan distance is utilized to quantify the difference between predicted conditions and known conditions of training data. Results show that the prediction accuracy of data driven-based methods is reduced with the increase of the weighted Manhattan distance. It is further discovered that relationships between logarithmic prediction residuals and corresponding logarithmic weighted Manhattan distances are approximatively linear.