Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network

Yu Feng, Xianfeng Xu and Yun Meng
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Yu Feng: School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
Xianfeng Xu: School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
Yun Meng: School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China

Energies, 2019, vol. 12, issue 6, 1-9

Abstract: Short-term load forecasting is very important for power systems. The load is related to many factors which compose tensors. However, tensors cannot be input directly into most traditional forecasting models. This paper proposes a tensor partial least squares-neural network model (TPN) to forecast the power load. The model contains a tensor decomposition outer model and a nonlinear inner model. The outer model extracts common latent variables of tensor input and vector output and makes the residuals less than the threshold by iteration. The inner model determines the relationship between the latent variable matrix and the output by using a neural network. This model structure can preserve the information of tensors and the nonlinear features of the system. Three classical models, partial least squares (PLS), least squares support vector machine (LSSVM) and neural network (NN), are selected to compare the forecasting results. The results show that the proposed model is efficient for short-term load and daily load peak forecasting. Compared to PLS, LSSVM and NN, the TPN has the best forecasting accuracy.

Keywords: power load; forecasting model; tensor PLS; neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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