Data-driven high-precision hourly-scale building energy consumptionprediction employing occupant energy-use behavior probability andswarm intelligence optimization

This research proposes an improved probability model of occupant energy-use behavior, which is integrated into energy consumption prediction as a new input. The sparrow search algorithm (SSA) is introduced to optimize the hyperparameter selection of the long short-term memory neural network (LSTM). A high-precision hourly-scale building energy consumption prediction system is established. The practical application in a building demonstrates that compared with the traditional prediction system, the improved energy consumption prediction system can increase the coefficient of determination (R2) by 0.201 on average, and decrease the average absolute percentage error (MAPE) by 18.10% and the coefficient of variation of the root mean square error (CV-RMSE) by 0.176 on average.