Power load forecasting in energy system based on improved extreme learning machine

Xu-Dong Chen,Yang Hai-Yue,Ching-Hsin Wang,Jhang-Shang Wun,Ling-Ling Li,Chien-Hung Wu

doi:10.1177/0144598720903797

Abstract

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

Highlights

With the rapid development of social economy, people’s demand for electricity is increasing (Abdel-Aal, 2004; Almuhtady et al, 2019; Li et al, 2017a, 2018b; Pai and Hong, 2005)
neural network (NN) model is suitable for large sample prediction; support vector machine (SVM) model is suitable for small sample prediction; Bayesian model is based on conditional probability, its calculation speed is slow; Gaussian process regression (GPR) model has the advantages of super parameter adaptive acquisition, but its calculation cost is large
The hyper parameters of the extreme learning machine (ELM) model are optimized by the NCSO optimizer, and the power load is predicted by the NCSO optimization extreme learning machine (NCSOELM) model

Summary

Introduction

With the rapid development of social economy, people’s demand for electricity is increasing (Abdel-Aal, 2004; Almuhtady et al, 2019; Li et al, 2017a, 2018b; Pai and Hong, 2005). Keywords Energy system, power load forecasting, extreme learning machine, optimizer, levy mutation strategy The super parameters of SVM are optimized by whale optimizer, and the power load is predicted by SVM model. Extreme learning machine (ELM) model is used as the power load forecasting model.

Results

Conclusion