Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks
文献类型：期刊论文
| 作者 | Yin, H.1,2; Tang, Z.1,2; Yang, C.1,2 |
| 刊名 | JOURNAL OF BUILDING ENGINEERING
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 76页码:16 |
| 关键词 | Chiller Prediction Support vector regression Artificial neural networks Electricity consumption |
| DOI | 10.1016/j.jobe.2023.107179 |
| 通讯作者 | Yin, H.(yinhua@ms.giec.ac.cn) |
| 英文摘要 | It is essential to predicting the electricity consumption of air-conditioning systems. In this study, three models, namely, backpropagation neural network (BPNN), cascade correlation neural network (CCNN), and support vector regression (SVR), were proposed for the prediction of the electricity consumption of chillers in subway stations. For demonstration, historical data from a subway station in Guangzhou were used for training and testing. The simulation results showed that the proposed models could predict the electricity consumption of chillers effectively. In comparison with BPNN, CCNN and SVR showed higher accuracy. For the refrigeration of public spaces (PSs), weak electricity regions (WERs), and comprehensive management regions (CMRs), CCNN exhibited the minimum mean absolute percentage error (MAPE) and root-mean-square error (RMSE) for the test samples, the values of which were 1.56% and 3.15, respectively, followed by those of SVR and BPNN. For the refrigeration of strong electricity regions (SERs), PSs, WERs, and CMRs, SVR presented the smallest error, and its MAPE and RMSE for the test samples were 2.01% and 6.09, respectively, followed by those of CCNN and BPNN. Through the comprehensive consideration of input characteristics, the comparison of different prediction models and the distinction of different operating conditions, the prediction accuracy was improved effectively. Based on the forecasting, the operational parameters of the air-conditioning system can be optimized in advance, which is of great significance for energy saving in subway stations. |
| WOS关键词 | ENERGY-CONSUMPTION ; COOLING LOAD ; REGRESSION ; ANN ; SIMULATION ; SVM |
| 资助项目 | Natural Science Foundation of Guangdong Province[2018A030310084] |
| WOS研究方向 | Construction & Building Technology ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001038315400001 |
| 出版者 | ELSEVIER |
| 资助机构 | Natural Science Foundation of Guangdong Province |
| 源URL | [http://ir.giec.ac.cn/handle/344007/39230]  |
| 专题 | 中国科学院广州能源研究所 |
| 通讯作者 | Yin, H. |
| 作者单位 | 1.Chinese Acad Sci, Guangzhou Inst Energy Convers, Key Lab Renewable Energy, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China 2.Guangdong Key Lab New & Renewable Energy Res & Dev, 2 Nengyuan Rd, Guangzhou 510640, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yin, H.,Tang, Z.,Yang, C.. Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks[J]. JOURNAL OF BUILDING ENGINEERING,2023,76:16. |
| APA | Yin, H.,Tang, Z.,&Yang, C..(2023).Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks.JOURNAL OF BUILDING ENGINEERING,76,16. |
| MLA | Yin, H.,et al."Predicting hourly electricity consumption of chillers in subway stations: A comparison of support vector machine and different artificial neural networks".JOURNAL OF BUILDING ENGINEERING 76(2023):16. |
入库方式： OAI收割
来源：广州能源研究所
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