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Energies 2019, 12(6), 990; https://doi.org/10.3390/en12060990

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

1,2,*
,
1
and
1
1
School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
2
College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Received: 2 February 2019 / Revised: 7 March 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
(This article belongs to the Section Electrical Power and Energy System)
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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. View Full-Text
Keywords: power load; forecasting model; tensor PLS; neural network power load; forecasting model; tensor PLS; neural network
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Feng, Y.; Xu, X.; Meng, Y. Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network. Energies 2019, 12, 990.

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