Sequential grid approach based support vector regression for short-term electric load forecasting

Youlong Yang, Jinxing Che, Chengzhi Deng and Li Li

Applied Energy, 2019, vol. 238, issue C, 1010-1021

Abstract: Short-term electric load forecasting is important for evaluating the power utility performance in terms of price and income elasticities, energy transfer scheduling, unit commitment and load dispatch. Support vector regression (SVR) approach applies a simple linear regression in the high-dimensional feature space (Hilbert space) by using kernel functions and has many attractive features and profound empirical performances for small sample, nonlinearity and high dimensional dataset. However, the SVR modeling processing has computation complexity of order O(K×N3) (where N is the size of the training dataset, and K is the evaluation number of the parameter selection process). To forecast short-term power load accurately, quickly and efficiently, a sequential grid approach based support vector regression (SGA-SVR) is proposed in this work. Specifically, for a given data set, parameter regression surface is conducted in SVR modeling processing with its forecasting performance as dependent variable and the three parameters (ε,C,γ) as independent variables. Then, a novel grid algorithm is presented to provide a new way for fitting the parameter regression surface. The statistical inference is also given by introducing the asymptotic normality of a fixed grid point of parameters. The numerical experiments using SGA-SVR model demonstrate the superiority over the standard SVR model and accuracy of forecast is greatly improved especially for short-term forecasts.

Keywords: Electric load forecasting; Support vector regression; Subsampling; Sequential grid approach; Model selection (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261919301503
Full text for ScienceDirect subscribers only

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:eee:appene:v:238:y:2019:i:c:p:1010-1021

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2019.01.127

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().