Short-Term Load Forecasting Based on Sequential Relevance Vector Machine

Jang, Youngchan

  • Received : 2015.05.13
  • Accepted : 2015.09.14
  • Published : 2015.09.30


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.


Sequential Relevance Vector Machine;Support Vector Machine;Online Learning;Short-Term Load Forecast;Prediction


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