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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/2/329 |
| _version_ | 1818012450461581312 |
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| author | Rana Muhammad Adnan Zhongmin Liang Xiaohui Yuan Ozgur Kisi Muhammad Akhlaq Binquan Li |
| author_facet | Rana Muhammad Adnan Zhongmin Liang Xiaohui Yuan Ozgur Kisi Muhammad Akhlaq Binquan Li |
| author_sort | Rana Muhammad Adnan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:20:20Z |
| format | Article |
| id | doaj.art-276288e7631c4272acd6a7823abb6490 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T06:20:20Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-276288e7631c4272acd6a7823abb64902022-12-22T02:08:03ZengMDPI AGEnergies1996-10732019-01-0112232910.3390/en12020329en12020329Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-ValidationRana Muhammad Adnan0Zhongmin Liang1Xiaohui Yuan2Ozgur Kisi3Muhammad Akhlaq4Binquan Li5College of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, ChinaFaculty of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, GeorgiaFaculty of Agricultural Engineering and Technology, PMAS-Arid Agriculture University, Rawalpindi 46300, PakistanCollege of Hydrology and Water Resources, Hohai University, Nanjing 210098, ChinaAccurate 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.https://www.mdpi.com/1996-1073/12/2/329wind speedwind powerforecastingleast square support vector regressionM5 regression treeneuro-fuzzy systemSotavento Galicia wind farm |
| spellingShingle | Rana Muhammad Adnan Zhongmin Liang Xiaohui Yuan Ozgur Kisi Muhammad Akhlaq Binquan Li Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation Energies wind speed wind power forecasting least square support vector regression M5 regression tree neuro-fuzzy system Sotavento Galicia wind farm |
| title | Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation |
| title_full | Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation |
| title_fullStr | Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation |
| title_full_unstemmed | Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation |
| title_short | Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation |
| title_sort | comparison of lssvr m5rt nf gp and nf sc models for predictions of hourly wind speed and wind power based on cross validation |
| topic | wind speed wind power forecasting least square support vector regression M5 regression tree neuro-fuzzy system Sotavento Galicia wind farm |
| url | https://www.mdpi.com/1996-1073/12/2/329 |
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