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annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm年度电力负荷预测的最小二乘支持向量机与果蝇优化算法.pdf

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annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm年度电力负荷预测的最小二乘支持向量机与果蝇优化算法

Energies 2012, 5, 4430-4445; doi:10.3390/en5114430 OPEN ACCESS energies ISSN 1996-1073 /journal/energies Article Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm Hongze Li 1, Sen Guo 1,*, Huiru Zhao 1, Chenbo Su 2 and Bao Wang 1 1 School of Economics and Management, North China Electric Power University, Beijing 102206, China; E-Mails: lihongze@163.com (H.L.); huiruzhao@163.com (H.Z.); wangbao610@163.com (B.W.) 2 School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China; E-Mail: scb2636@ * Author to whom correspondence should be addressed; E-Mail: guosen324@163.com; Tel.: +86-10-5196-3626; Fax: +86-10-8079-6904. Received: 14 September 2012; in revised form: 18 October 2012 / Accepted: 2 November 2012 / Published: 8 November 2012 Abstract: The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy