AUTHOR=Wang Rui , Xia Xiaoyi , Li Yanping , Cao Wenming 

TITLE=Clifford Fuzzy Support Vector Machine for Regression and Its Application in Electric Load Forecasting of Energy System

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2021.793078

DOI=10.3389/fenrg.2021.793078

ISSN=2296-598X

ABSTRACT=Electric load forecasting is a prominent topic in energy research. Support vector regression (SVR) has extensively and successfully achieved good performance in electric load forecasting. Clifford Support Vector Regression (CSVR) realizes multiple output by the Clifford geometric algebra which can be used in multi-step forecasting of electric load. However the effect of input is different to the forecasting value. Since the load forecasting value affects the energy reserve and distribution in energy system, the accuracy is important in electric load forecasting. In this paper, a fuzzy support vector machine is proposed based on geometric algebra named Clifford Fuzzy Support Vector Machine for Regression (CFSVR). Through fuzzy membership different input points have different contribution to deciding the optimal regression hyperplane. We evaluate the performance of the proposed CFSVR in fitting tasks on numerical simulation, UCl data set and signal data set, and forecasting tasks on electric load data set and NN3 data set. The result of the experiment indicates that Clifford Fuzzy Support Vector Machine for Regression has better performance than CSVR and SVR of other algorithm which can improve the accuracy of electric load forecasting and achieve multi-step forecasting.