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 outputs by the Clifford geometric algebra which can be used in multistep forecasting of electric load. However, the effect of input is different from the forecasting value. Since the load forecasting value affects the energy reserve and distribution in the energy system, the accuracy is important in electric load forecasting. In this study, 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 contributions to deciding the optimal regression hyperplane. We evaluate the performance of the proposed CFSVR in fitting tasks on numerical simulation, UCI 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 algorithms which can improve the accuracy of electric load forecasting and achieve multistep forecasting.
Highlights
Electric load forecasting is used to forecast the value of electric load in the future, which plays an important role in electric system operation
In order to improve the performance of Clifford support vector regression (CSVR), fuzzy membership is set in this study
The result is described by mean absolute error (MAE) and root mean square error (RMSE) (Gao et al, 2020)
Summary
Electric load forecasting is used to forecast the value of electric load in the future, which plays an important role in electric system operation. The input points may be affected by noise and outliers which make the points abnormal In this case, the result of the regression will deviate from the optimal hyperplane by CSVR and SVR of the classical algorithm. The contribution of this study is to apply fuzzy membership to CSVR and reformulate it into the Clifford fuzzy support vector machine for regression (CFSVR). The fuzzy membership reduces the effect of outliers and noise, while in a forecasting situation, the fuzzy membership enables different points in sequence to have different contributions to the predicted value. The experiments on electric load data set and NN3 data set for forecasting demonstrate that the proposed CFSVR effectively improves the accuracy of CSVR and SVR of other algorithms.
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