An incremental electric load forecasting model based on support vector regression

• Incremental electric load forecasting under a batch arriving and large sample is studied.
• A representative data reconstruction method is proposed for quickly re-training.
• Phase space reconstruction is implemented to determine an optimal embedding.
• The I-SVR model whose parameters are tuned by a nested PSO is built to make forecast.
• The obtained results confirm the applicability and superiority of the model.

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.