Volume 9 Number 8 (Aug. 2014)
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JSW 2014 Vol.9(8): 2093-2104 ISSN: 1796-217X
doi: 10.4304/jsw.9.8.2093-2104

Modified Parallel Cat Swarm Optimization in SVM Modeling for Short-term Cooling Load Forecasting

Yuanmei Wen, Yanyu Chen
School of Information Engineering, Guangdong University of Technology, Guangzhou, China

Abstract—In order to improve forecasting accuracy of cooling load, this paper applies support vector machine (SVM) model with modified parallel cat swarm optimization (MPCSO) to forecast next-day cooling load in district cooling system(DCS). By extracting the Eigen value of the input historical load data, principal component analysis (PCA) algorithm is used to reduce the complexity of the data sequence. Based on cats’ cooperation and competition, an MPCSO algorithm is proposed to optimize the hyper parameters for the SVM model. Finally, the SVM model with MPCSO (namely MPCSO-SVM) is established to conduct the short-term cooling load forecasting. Numerical example results show that the proposed model outperforms the existing alternative models. Thus, the proposed model is effective and applicable to cooling load forecasting.

Index Terms—ad forecasting model, support vector machine, parallel cat swarm optimization, principal component analysis


Cite: Yuanmei Wen, Yanyu Chen, "Modified Parallel Cat Swarm Optimization in SVM Modeling for Short-term Cooling Load Forecasting," Journal of Software vol. 9, no. 8, pp. 2093-2104, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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