Hybrid prediction model for cold load in large public buildings based on mean residual feedback and improved SVR

Abstract

In order to control the energy efficiency of air conditioning systems and design energy management strategies, accurate and effective prediction of building cooling demands is a crucial step. We propose a time-granular residual feedback-based and improved sparrow algorithm for optimizing support vector regression models (TGRF-ISSA-SVR). To begin, select the input features using Random forest. Second, a method (TGRF) is proposed to calculate and feedback the mean residual with time granularities of hour, day, and month. Then, three strategies are used to improve the Sparrow Algorithm (ISSA) and apply it to SVR parameter optimization. Finally, the measured data of a large commercial building in Xi'an are used for experimental verification. The RMSE of TGRF-ISSA-SVR is 4.33 and the MAPE is 0.66, indicating that the model has low error. The RMSE and MAPE of TGRF-ISSA-SVR are 75% and 74% lower than those of TGRF-SVR. The RMSE and MAPE of TGRF-SVR are 37% and 38% lower than those of SVR, demonstrating that the improved model has accuracy improvement. In addition, compared with models such as LSTM and GRNN, the TGRF-ISSA-SVR model has higher prediction accuracy and shorter prediction time, so TGRF-ISSA-SVR can provide effective support for load prediction of large public buildings.