ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners

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 constru...

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Main Authors: Mengran Zhou, Qiqi Zhang, Feng Hu, Ling Wang, Guangyao Zhou, Weile Kong, Changzhen Wu, Enhan Cui
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Energy Science & Engineering
Subjects:
Online Access:https://doi.org/10.1002/ese3.1986
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author Mengran Zhou
Qiqi Zhang
Feng Hu
Ling Wang
Guangyao Zhou
Weile Kong
Changzhen Wu
Enhan Cui
author_facet Mengran Zhou
Qiqi Zhang
Feng Hu
Ling Wang
Guangyao Zhou
Weile Kong
Changzhen Wu
Enhan Cui
author_sort Mengran Zhou
collection DOAJ
description 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.
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spelling doaj-art-9aa630673b11463bbf8a4f9a7afa501d2025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-0113116017510.1002/ese3.1986ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air ConditionersMengran Zhou0Qiqi Zhang1Feng Hu2Ling Wang3Guangyao Zhou4Weile Kong5Changzhen Wu6Enhan Cui7School of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaSchool of Electrical and Information Engineering Anhui University of Science and Technology Huainan ChinaABSTRACT 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.https://doi.org/10.1002/ese3.1986air conditioning operation characteristicsdeep belief networkimproved seagull optimization algorithmrestricted Boltzmann machine
spellingShingle Mengran Zhou
Qiqi Zhang
Feng Hu
Ling Wang
Guangyao Zhou
Weile Kong
Changzhen Wu
Enhan Cui
ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
Energy Science & Engineering
air conditioning operation characteristics
deep belief network
improved seagull optimization algorithm
restricted Boltzmann machine
title ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
title_full ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
title_fullStr ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
title_full_unstemmed ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
title_short ISOA‐DBN: A New Data‐Driven Method for Studying the Operating Characteristics of Air Conditioners
title_sort isoa dbn a new data driven method for studying the operating characteristics of air conditioners
topic air conditioning operation characteristics
deep belief network
improved seagull optimization algorithm
restricted Boltzmann machine
url https://doi.org/10.1002/ese3.1986
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