Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM

Authors:
Qi Wu
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing, Jiangsu 210096, China and School of Mechanical Engineering, Southeast ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

This paper presents a new load forecasting model based on hybrid particle swarm optimization with Gaussian and adaptive mutation (HAGPSO) and wavelet v-support vector machine (Wv-SVM). Firstly, it is proved that mother wavelet function can build a set of complete base through horizontal floating and form the wavelet kernel function. And then, Wv-SVM with wavelet kernel function is proposed in this paper. Secondly, aiming to the disadvantage of standard PSO, HAGPSO is proposed to seek the optimal parameter of Wv-SVM. Finally, the load forecasting model based on HAGPSO and Wv-SVM is proposed in this paper. The results of application in load forecasts show the proposed model is effective and feasible.