Power load forecasting by wavelet least squares support vector machine with improved fruit fly optimization algorithm

Niu Dongxiao, Ma Tiannan () and Liu Bingyi
Additional contact information
Niu Dongxiao: North China Electric Power University
Ma Tiannan: North China Electric Power University
Liu Bingyi: North China Electric Power University

Journal of Combinatorial Optimization, 2017, vol. 33, issue 3, No 19, 1122-1143

Abstract: Abstract As is affected by many factors, mid-long term power load forecasting has become the nonlinear and multi-dimension complex problem, and its accuracy affects the decision and layout of power generation sector. In order to improve the accuracy and convergence ability of the single least square support vector machine (LSSVM), this paper proposes the improved fruit fly optimization algorithm applied to wavelet least square support vector machine (IFOA-w-LSSVM). Firstly, the Gaussian kernel function of LSSVM is replaced by the wavelet kernel function and wavelet least square support vector machine (w-LSSVM) is built. Secondly, the ordinary fruit fly optimization algorithm (FOA) is improved from three aspects: (1) dividing fruit fly group into two parts: (2) improving the taste detection function; (3) using Cauchy mutation process to make fruit fly individuals variant. Finally, w-LSSVM is optimized by IFOA for seeking the optimal parameters and achieving the forecasting accuracy. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in mid-long term power load forecasting.

Keywords: Power load forecasting; Fruit fly optimization algorithm; Least square support vector machine; Wavelet kernel function (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s10878-016-0027-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:33:y:2017:i:3:d:10.1007_s10878-016-0027-7

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-016-0027-7

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().