An incremental electric load forecasting model based on support vector regression

YouLong Yang, JinXing Che, YanYing Li, YanJun Zhao and SuLing Zhu

Energy, 2016, vol. 113, issue C, 796-808

Abstract: With the smart portable systems and the daily growth of databases on the web, there are ever-increasing requirements to learn the batch arriving and large sample data set. In this paper, an incremental learning model of support vector regression (SVR) is proposed to forecast the electric load under the batch arriving and large sample. For modeling with SVR, the optimal embedding of time series is constructed by phase space reconstruction (PSR). Then, an optimal training subset for the training of SVR is extracted based on the current data set, which enables us to cut the high time and space complexity by reducing the full training data set. When newly-increased data are added into the system, a representative data set reconstruction method is presented for quickly re-training the current SVR, and a nested particle swarm optimization (NPSO) framework is presented to select the parameters of the incremental SVR model. Experiments of incremental electric load forecasting demonstrate the computational superiority of the presented model over the comparison models.

Keywords: Electric load forecasting; Phase space reconstruction; Support vector regression; Representative data set reconstruction method; Nested particle swarm optimization (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:113:y:2016:i:c:p:796-808

DOI: 10.1016/j.energy.2016.07.092

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