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

Yu Feng,Xianfeng Xu,Yun Meng

doi:10.3390/en12060990

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.

Highlights

Load forecasting is very important in the planning, operation and maintenance of power system [1,2]
The outer model is built by tensor partial partial least squares and the inner model is built by the neural network
The structure of tensor partial least squares-neural network model (TPN) is least squares and the inner model is built by the neural network

Summary

Introduction

Load forecasting is very important in the planning, operation and maintenance of power system [1,2]. A good prediction model is always the key issue of load forecasting. Forecasting was mainly based on the previous information of a certain time period of the load situation. In spite of the length of the time period, from the perspective of the data structure, the input of the prediction model is a time series of the power load. Besides the previous situation, the power load is related to many factors such as seasons, meteorological conditions and people’s living habits [3,4]. In order to improve the forecasting accuracy of load, the influence of these factors must be considered. The input of the prediction model changes from time series to tensor, which has a complex structure [5]

Methods

Results

Discussion

Conclusion