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http://dx.doi.org/10.7232/iems.2015.14.3.318

Short-Term Load Forecasting Based on Sequential Relevance Vector Machine  

Jang, Youngchan (Weapon System Engineering Department, Korea Army Academy at Yeongcheon)
Publication Information
Industrial Engineering and Management Systems / v.14, no.3, 2015 , pp. 318-324 More about this Journal
Abstract
This paper proposes a dynamic short-term load forecasting method that utilizes a new sequential learning algorithm based on Relevance Vector Machine (RVM). The method performs general optimization of weights and hyperparameters using the current relevance vectors and newly arriving data. By doing so, the proposed algorithm is trained with the most recent data. Consequently, it extends the RVM algorithm to real-time and nonstationary learning processes. The results of application of the proposed algorithm to prediction of electrical loads indicate that its accuracy is comparable to that of existing nonparametric learning algorithms. Further, the proposed model reduces computational complexity.
Keywords
Sequential Relevance Vector Machine; Support Vector Machine; Online Learning; Short-Term Load Forecast; Prediction;
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