doi: 10.4304/jsw.9.8.2093-2104
Modified Parallel Cat Swarm Optimization in SVM Modeling for Short-term Cooling Load Forecasting
- 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.
Keywords
- ad forecasting model
- support vector mac
How to Cite
Yuanmei Wen and 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. https://doi.org/ 10.4304/jsw.9.8.2093-2104
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).