Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting
نویسندگان
چکیده
منابع مشابه
Hybrid Chaotic Quantum Bat Algorithm with SVR in Electric Load Forecasting
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the electric load forecasting has demonstrated the superiorities in forecasting accuracy improvements. The recently proposed bat algorithm (BA), compared with classical GA and PSO algorithm, has greater potential in forecasting accuracy improvements. However, the original BA still suffers from the embedd...
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Application of support vector regression (SVR) with chaotic sequence and evolutionary algorithms not only could improve forecasting accuracy performance, but also could effectively avoid converging prematurely (i.e., trapping into a local optimum). However, the tendency of electric load sometimes reveals cyclic changes (such as hourly peak in a working day, weekly peak in a business week, and m...
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Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to pre...
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Li-Ling Peng 1, Guo-Feng Fan 1, Min-Liang Huang 2 and Wei-Chiang Hong 3,4,* 1 College of Mathematics & Information Science, Ping Ding Shan University, Pingdingshan 467000, China; [email protected] (L.-L.P.); [email protected] (G.-F.F.) 2 Department of Industrial Management, Oriental Institute of Technology, 58 Sec. 2, Sichuan Rd., Panchiao, New Taipei 220, Taiwan; minglianghuang2016@gmail...
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ژورنال
عنوان ژورنال: Energies
سال: 2017
ISSN: 1996-1073
DOI: 10.3390/en10122180