Multiple kernel support vector machine short-term load forecasting based on multi-source heterogeneous integration of load factors
- Jun Gao, Meng Nie, Ying Zhen, Qianhong Wu, Yanda Wu, Pengli Qiao, Pei Li
- Corresponding Author
- Jun Gao
Available Online November 2016.
- https://doi.org/10.2991/aest-16.2016.2How to use a DOI?
- multiple kernel function; load forecasting; multi-source heterogeneous;load factors; big data; SVM model; parallel processing.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016) PB - Atlantis Press UR - https://doi.org/10.2991/aest-16.2016.2 DO - https://doi.org/10.2991/aest-16.2016.2 ID - Gao2016/11 ER -