An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting

Jinxing Che, Jianzhou Wang and Guangfu Wang

Energy, 2012, vol. 37, issue 1, 657-664

Abstract: Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR) for small sample and balanced data, this paper presents an adaptive fuzzy combination model based on the self-organizing map (SOM), the SVR and the fuzzy inference method. The adaptive fuzzy combination model can effectively count for electric load forecasting with good accuracy and interpretability at the same time. The key idea behind the combination is to build a human-understandable knowledge base by constructing a fuzzy membership function for each homogeneous sub-population. The comparison of different mathematical models and the effectiveness of the presented model are shown by the real data of New South Wales electricity market. The obtained results confirm the validity of the developed model.

Keywords: Electric load prediction; Support vector regression; Fuzzy membership function; Self-organizing map (SOM) (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (37)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544211006931
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:energy:v:37:y:2012:i:1:p:657-664

DOI: 10.1016/j.energy.2011.10.034

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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