Open Access Open Access  Restricted Access Subscription or Fee Access

Short-term Load Forecasting based on Support Vector Machine Optimized by Catfish Particle Swarm Optimization Algorithm

Xiaoyan SHi, Zhenbi Li

Abstract


In order to accurately, effectively forecast short-term load, a short-term load forecasting based on support vector machine optimized by catfish particle swarm optimization algorithm (CFPSO-SVM) is proposed. First, the short-term load time series is reconstructed based on chaos theory, and then the support vector machine (SVM) parameters are taken as a particle location string, and catfish effect is introduced to overcome the shortcomings of particle swarm algorithm to find the optimal parameters of support vector machine through the particle interactions. Finally, short-term load forecasting model is built according to the optimum parameters. The model performance is test by simulation experiment and the results show that, compared with other forecasting models, CFPSO-SVM accelerates the parameters optimizing speed of support vector machine and improves the forecasting precision of short term load, and it is more suitable for short-term load forecasting needs.

Keywords


short-time load, support vector machine, chaotic theory, particle swarm optimization algorithm, catfish effect.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.