Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms

Abstract

Effective building energy prediction is vital for sustainable development, especially with an increasing focus on flexibility and elasticity in building energy usage. However, challenges persist in input and algorithm selection including suitable input selection and the balance of calculation time consumption and prediction precision. This study proposed a method for suitable input selection and introduced a CNN-LSTM-SE model for enhancing prediction precision. The CNN-LSTM-SE model integrates a convolutional neural network (CNN) process and a squeeze-and-excitation (SE) block attention mechanism into a long short-term memory (LSTM) neural network. Experiments across seven buildings demonstrate that selecting inputs improves the prediction performance with a 13.94 % decrease in MAPE (mean absolute percent error), a 3.36 % increase in R2 (coefficient of determination), and an 11.20 % decrease in CV-RMSE (coefficient-of-variation of root mean square error) on average compared to not selecting inputs. Moreover, implementing CNN-LSTM-SE yields a 21.44 % MAPE decrease, a 6.25 % R2 increase, and a 21.82 % CV-RMSE decrease compared to alternative algorithms. Simultaneously employing suitable inputs and CNN-LSTM-SE balances time consumption and prediction precision optimally.