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

Author

Listed:
  • Yu Feng

    (School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China
    College of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, 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)

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.

Suggested Citation

  • Yu Feng & Xianfeng Xu & Yun Meng, 2019. "Short-Term Load Forecasting with Tensor Partial Least Squares-Neural Network," Energies, MDPI, vol. 12(6), pages 1-9, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:990-:d:213800
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    Citations

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    Cited by:

    1. Tian Shi & Fei Mei & Jixiang Lu & Jinjun Lu & Yi Pan & Cheng Zhou & Jianzhang Wu & Jianyong Zheng, 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting," Energies, MDPI, vol. 12(22), pages 1-17, November.
    2. Bin Li & Mingzhen Lu & Yiyi Zhang & Jia Huang, 2019. "A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction," Energies, MDPI, vol. 12(20), pages 1-19, October.

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