Multiple kernel support vector machine short-term load forecasting based on multi-source heterogeneous integration of load factors

DOI

10.2991/aest-16.2016.2How to use a DOI?

Keywords

multiple kernel function; load forecasting; multi-source heterogeneous;load factors; big data; SVM model; parallel processing.

Abstract

In this paper we present a novel method developed from multiple kernel function for short term load forecasting to integrate multi-source heterogeneous load factors in big data. Nine kinds of load factors were selected as multi-source heterogeneous factors. In the proposed method, three algorithms (the sample distribution method, single variable method and rank space diversity method) were adopted to establish the optimal multiple kernel functions to integrate the load factors. Experimental results show that the average relative error of multiple kernel SVM is smaller than single kernel SVM, and the accuracy of multiple kernel SVM model based on double layer multiple kernel learning algorithm and lp norm is the highest. Therefore, multiple kernel SVM can deal with the multi-source heterogeneous data in the load forecasting effectively, and the speed and accuracy of load forecasting can be improved by parallel processing.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun Gao
AU  - Meng Nie
AU  - Ying Zhen
AU  - Qianhong Wu
AU  - Yanda Wu
AU  - Pengli Qiao
AU  - Pei Li
PY  - 2016/11
DA  - 2016/11
TI  - Multiple kernel support vector machine short-term load forecasting based on multi-source heterogeneous integration of load factors
BT  - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 10
EP  - 22
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.2
DO  - 10.2991/aest-16.2016.2
ID  - Gao2016/11
ER  -