Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation

Rana Adnan,Binquan Li,Muhammad Akhlaq,Ozgur Kisi,Zhongmin Liang,Xiaohui Yuan

doi:10.3390/en12020329

Abstract

Accurate predictions of wind speed and wind energy are essential in renewable energy planning and management. This study was carried out to test the accuracy of two different neuro fuzzy techniques (neuro fuzzy system with grid partition (NF-GP) and neuro fuzzy system with substractive clustering (NF-SC)), and two heuristic regression methods (least square support vector regression (LSSVR) and M5 regression tree (M5RT)) in the prediction of hourly wind speed and wind power using a cross-validation method. Fourfold cross-validation was employed by dividing the data into four equal subsets. LSSVR’s performance was superior to that of the M5RT, NF-SC, and NF-GP models for all datasets in wind speed prediction. The overall average root-mean-square errors (RMSE) of the M5RT, NF-GP, and NF-SC models decreased by 11.71%, 1.68%, and 2.94%, respectively, using the LSSVR model. The applicability of the four different models was also investigated in the prediction of one-hour-ahead wind power. The results showed that NF-GP’s performance was superior to that of LSSVR, NF-SC, and M5RT. The overall average RMSEs of LSSVR, NF-SC, and M5RT decreased by 5.52%, 1.30%, and 15.6%, respectively, using NF-GP.

Highlights

Because of increasing environmental pollution and the energy crisis, wind energy is very important for the energy industry
The results indicated that least square support vector regression (LSSVR) alone provided better accuracy compared to the others
The accuracy of LSSVR, M5 regression tree (M5RT), neuro-fuzzy system (NF)-grid partition (GP), and NF-soft computing (SC) was tested for hourly wind power prediction

Summary

Introduction

Because of increasing environmental pollution and the energy crisis, wind energy is very important for the energy industry. In 2017, according to the report of World Wind Energy Association, the total installed wind power capacity (WPC) of the whole world increased to 539 GW with recent installation of 52.6 GW [2], while the global growth rate was 10.8%. In the same year in China, the recently installed WPC was 15 GW, and the total capacity reached 163.67 GW with a 21.3% increment. Both the wind power capacity and the growth rate of China were larger than those of other countries in 2017.

Methods

Results

Conclusion