{"title":"A deep belief network-based energy consumption prediction model for water source heat pump system","authors":"","doi":"10.1016/j.applthermaleng.2024.124000","DOIUrl":null,"url":null,"abstract":"<div><p>To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R<sup>2</sup>) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.</p></div>","PeriodicalId":8201,"journal":{"name":"Applied Thermal Engineering","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Thermal Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1359431124016685","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R2) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.
期刊介绍:
Applied Thermal Engineering disseminates novel research related to the design, development and demonstration of components, devices, equipment, technologies and systems involving thermal processes for the production, storage, utilization and conservation of energy, with a focus on engineering application.
The journal publishes high-quality and high-impact Original Research Articles, Review Articles, Short Communications and Letters to the Editor on cutting-edge innovations in research, and recent advances or issues of interest to the thermal engineering community.