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

•TGRF is proposed for mean residual feedback to improve the model accuracy.•SSA is improved using three strategies to enhance the algorithm performance.•The performance advantages of TGRF-ISSA-SVR were tested with practical examples. In order to control the energy efficiency of air conditioning syst...

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Vydané v:Energy and buildings Ročník 294; s. 113229
Hlavní autori: Liu, Haiyan, Yu, Junqi, Dai, Junwei, Zhao, Anjun, Wang, Meng, Zhou, Meng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.09.2023
Predmet:
Building cooling loads
Improved sparrow search algorithm
Residual feedback
Short-term forecasting
Support vector machines
Support vector machines
Building cooling loads
Short-term forecasting
Improved sparrow search algorithm
Residual feedback
ISSN:0378-7788
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Shrnutí:•TGRF is proposed for mean residual feedback to improve the model accuracy.•SSA is improved using three strategies to enhance the algorithm performance.•The performance advantages of TGRF-ISSA-SVR were tested with practical examples. 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.
ISSN:0378-7788
DOI:10.1016/j.enbuild.2023.113229