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

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 (N...

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Bibliographic Details
Published in:Energies (Basel) 2019-01, Vol.12 (2), p.329
Main Authors: Rana, Muhammad Adnan, Liang, Zhongmin, Yuan, Xiaohui, Kisi, Ozgur, Akhlaq, Muhammad, Li, Binquan
Format: Article
Language:English
Subjects:
Accuracy
Alternative energy sources
Artificial intelligence
Classification
Clustering
Electric power
Electricity
Electricity distribution
Energy
Energy industry
Energy modeling
Engineering
forecasting
Fuzzy systems
Hydrology
least square support vector regression
M5 regression tree
Methods
Neural networks
neuro-fuzzy system
Predictions
Sotavento Galicia wind farm
Stream flow
Support vector machines
Time series
Wavelet transforms
Weather forecasting
Wind power
Wind speed
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en12020329