Next Article in Journal
Unsteady Simulation of a Full-Scale CANDU-6 Moderator with OpenFOAM
Previous Article in Journal
A Coupled, Semi-Numerical Model for Thermal Analysis of Medium Frequency Transformer
Previous Article in Special Issue
Probabilistic Wind Power Forecasting Approach via Instance-Based Transfer Learning Embedded Gradient Boosting Decision Trees
Article Menu
Issue 2 (January-2) cover image

Export Article

Open AccessArticle
Energies 2019, 12(2), 329; https://doi.org/10.3390/en12020329

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

1
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
3
School of Hydropower and Information Engineering, Huazhong University of Science & Technology, Wuhan 430074, China
4
Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi 0162, Georgia
5
Faculty of Agricultural Engineering and Technology, PMAS-Arid Agriculture University, Rawalpindi 46300, Pakistan
*
Authors to whom correspondence should be addressed.
Received: 8 December 2018 / Revised: 15 January 2019 / Accepted: 16 January 2019 / Published: 21 January 2019
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
Full-Text   |   PDF [2889 KB, uploaded 21 January 2019]   |  

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. View Full-Text
Keywords: wind speed; wind power; forecasting; least square support vector regression; M5 regression tree; neuro-fuzzy system; Sotavento Galicia wind farm wind speed; wind power; forecasting; least square support vector regression; M5 regression tree; neuro-fuzzy system; Sotavento Galicia wind farm
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Adnan, R.M.; Liang, Z.; Yuan, X.; Kisi, O.; Akhlaq, M.; Li, B. Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation. Energies 2019, 12, 329.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top