ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners - Mengran Zhou; Qiqi Zhang; Feng Hu; Ling Wang; Guangyao Zhou; Weile Kong; Changzhen Wu; Enhan Cui (Energy Science & Engineering)
ABSTRACT Air conditioning load is a crucial demand response resource for optimizing energy consumption control, and its accurate analysis provides an essential basis for achieving efficient energy management. We aim at solving the problems of scarcity, single type, low accuracy and difficult construction of high‐quality data sets available for air conditioning operation characteristic models at present. This paper proposes a construction method of air conditioning operation characteristic model based on an improved seagull optimization algorithm to optimize deep belief network (ISOA‐DBN). Firstly, the data set for the study of air conditioning operation characteristics is obtained through experiments. Secondly, the Restricted Boltzmann Machine (RBM) and Deep Belief Network (DBN) are used to study the operating characteristics of air conditioning. The results show that the model effect is better when DBN is used to study the operating characteristics of air conditioning, and the coefficient of determination reaches 0.9439. Then, the SOA is improved, and its performance is tested. The results show that ISOA performs better than SOA in the test of 14 standard functions. Finally, the ISOA is used to adjust the DBN parameters finely. The results show that compared with DBN and SOA‐DBN, ISOA‐DBN has a better model effect when used to study the operating characteristics of air conditioners, and the coefficient of determination reaches 0.9534. This can provide strong support for studying air conditioning operating characteristics under different working conditions and has broad application prospects in optimizing energy consumption control.