Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms
文献类型：期刊论文
| 作者 | Xu, Yuanjin3; Li, Fei1,2; Asgari, Armin4 |
| 刊名 | ENERGY
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 240页码:12 |
| 关键词 | Biogeography-based optimization Cooling and heating load Evolutionary algorithms Energy-efficient buildings Neural network |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2021.122692 |
| 通讯作者 | Li, Fei(lifeicas@126.com) |
| 英文摘要 | Since cooling and heating loads are regarded as significant parameters to examine the energy performance of buildings, the need to predict and analyze them for the residential buildings seems to be undeniable. Hence, the present paper wants to optimize the multi-layer perceptron neural network using several optimization methods to predict the heating and cooling of energy-efficient buildings. The dataset used in this study consists of eight independent factors: surface area, wall area, roof area, relative compactness, overall height, orientation, glazing area, and glazing area distribution. To prove the reliability and accuracy of the obtained results, test and training data are also considered. According to the statistical results, biogeography-based optimization has the highest value of R-2 and the lowest values of RMSD, normalized RMSD, and MAE in both training data and test data for cooling and heating loads. Hence, the forecasting accuracy of the proposed MLP neural network optimized with the BBO optimization algorithm with the RMSD, R-2, and MAE of 2.82, 0.920, 2.15 in the training phase of heating load and with the RMSD, R-2, and MAE of 3.18, 0.887, 2.97 in the training phase of the cooling load is much better than those of the other models. (C) 2021 Elsevier Ltd. All rights reserved. |
| WOS关键词 | ENERGY PERFORMANCE ; ARTIFICIAL-INTELLIGENCE ; CONSUMPTION ; MODEL ; REGRESSION ; MACHINE ; DEMAND ; DESIGN |
| 资助项目 | Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)[CUGQY1911] ; Key Research Program of the Innovation Academy for Green Manufacture, Chinese Academy of Sciences[IGAM-2019-A16-1] ; Key Research Program of the Alliance of International Science Organizations[ANSO-CRKP-2020-02] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000738791300009 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) ; Key Research Program of the Innovation Academy for Green Manufacture, Chinese Academy of Sciences ; Key Research Program of the Alliance of International Science Organizations |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/169481]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Fei |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.China Univ Geosci, Sch Earth Resources, Inst Math Geol & Remote Sensing Geol, 388 Lumo Rd, Wuhan 430074, Peoples R China 4.Univ Tabriz, Dept Mech Engn, Tabriz, Iran |
| 推荐引用方式 GB/T 7714 | Xu, Yuanjin,Li, Fei,Asgari, Armin. Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms[J]. ENERGY,2022,240:12. |
| APA | Xu, Yuanjin,Li, Fei,&Asgari, Armin.(2022).Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms.ENERGY,240,12. |
| MLA | Xu, Yuanjin,et al."Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms".ENERGY 240(2022):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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