A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm
•A novel hesitant fuzzy wind speed forecasting system is proposed for the first time.•Multi-fuzzification methods are proposed to deal with the non-determinism problem.•The weights of intervals are determined by multi-objective optimization algorithm.•A new defuzzification model is developed to obta...
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| Veröffentlicht in: | Expert systems with applications Jg. 168; S. 114364 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
15.04.2021
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •A novel hesitant fuzzy wind speed forecasting system is proposed for the first time.•Multi-fuzzification methods are proposed to deal with the non-determinism problem.•The weights of intervals are determined by multi-objective optimization algorithm.•A new defuzzification model is developed to obtain accurate and reliable forecasts.•The proposed system outperforms comparison models with high accuracy and efficiency.
Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency. |
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| AbstractList | •A novel hesitant fuzzy wind speed forecasting system is proposed for the first time.•Multi-fuzzification methods are proposed to deal with the non-determinism problem.•The weights of intervals are determined by multi-objective optimization algorithm.•A new defuzzification model is developed to obtain accurate and reliable forecasts.•The proposed system outperforms comparison models with high accuracy and efficiency.
Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency. Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership level. Therefore, in this study, the fuzzy series forecasting model was improved based on hesitant fuzzy sets. A hesitant fuzzy wind speed forecasting system with a novel defuzzification method and multiobjective optimization algorithm was developed. First, an advanced decomposition model is employed to extract the effective feature and remove the noise component from the raw wind speed series. Then, the universe of discourse is partitioned into equal and unequal intervals by multifuzzification methods and merged by aggregating hesitant information. A multiobjective intelligent optimization algorithm is applied to determine the optimal weights of different intervals accurately and stably. Furthermore, a novel defuzzification model based on an ordered weighted averaging operator and a regular increasing monotone quantifier is proposed to calculate the final forecasting results. The crucial strengths of the developed system are verifying the possibility of enhancing the performance of wind speed forecasting models by improving conventional fuzzy time series forecasting models and integrating them with decomposition models and artificial-intelligence models. Typical wind speed series datasets with different resolutions were selected to evaluate the performance of the proposed system, and experimental results prove that the proposed system outperforms other comparison models with high forecasting accuracy and computing efficiency. |
| ArticleNumber | 114364 |
| Author | Wang, Ying Wang, Jianzhou Li, Hongmin Lu, Haiyan |
| Author_xml | – sequence: 1 givenname: Jianzhou surname: Wang fullname: Wang, Jianzhou organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 2 givenname: Hongmin orcidid: 0000-0002-7554-573X surname: Li fullname: Li, Hongmin email: hongminli0911@126.com organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 3 givenname: Ying surname: Wang fullname: Wang, Ying organization: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China – sequence: 4 givenname: Haiyan orcidid: 0000-0001-5655-0237 surname: Lu fullname: Lu, Haiyan organization: Centre for Artificial Intelligence, School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia |
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| Cites_doi | 10.1016/j.asoc.2020.106294 10.1016/j.advengsoft.2017.07.002 10.1016/j.energy.2019.06.132 10.1016/j.apenergy.2019.114259 10.1016/0165-0114(94)90067-1 10.1016/j.jclepro.2019.03.036 10.1109/TSG.2013.2280649 10.1016/j.apenergy.2018.09.012 10.1016/j.jempfin.2018.03.002 10.1016/j.apenergy.2013.08.025 10.1016/j.asoc.2020.106350 10.1016/j.renene.2019.04.157 10.1016/j.apenergy.2017.09.063 10.1016/j.apenergy.2017.04.017 10.1016/j.enconman.2018.03.098 10.1016/j.asoc.2019.105972 10.1016/j.enconman.2019.111975 10.1016/j.eswa.2016.07.044 10.1049/iet-its.2016.0208 10.1016/j.renene.2017.02.014 10.1016/j.energy.2014.08.064 10.1016/j.ijar.2010.09.002 10.1007/s41066-018-00144-4 10.1016/j.renene.2016.10.030 10.1016/0165-0114(93)90372-O 10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z 10.1016/j.asoc.2018.07.030 10.1016/0165-0114(95)00220-0 10.1016/j.knosys.2014.11.003 10.1016/j.jenvman.2019.109855 10.1016/j.renene.2019.01.031 10.1016/j.enconman.2018.02.034 10.1016/j.apm.2019.07.001 10.1109/TPWRS.2017.2787667 10.1016/j.renene.2020.02.016 10.1016/j.apenergy.2020.115561 10.1109/ACCESS.2019.2957062 10.1016/S0165-0114(00)00057-9 10.1016/j.seta.2019.100601 10.1109/JESTPE.2016.2590834 10.1016/j.seta.2018.04.010 10.1016/j.seta.2019.100582 10.1016/j.jocs.2018.05.008 10.1016/j.apenergy.2010.10.031 10.1016/j.ijepes.2020.106056 10.1016/j.ijar.2007.05.006 10.1016/j.apenergy.2019.114137 10.1016/j.enconman.2020.112524 10.1109/21.87068 10.1016/j.asoc.2019.03.035 10.1109/ACCESS.2020.2973746 10.1016/j.renene.2017.09.089 10.1016/j.energy.2019.02.194 10.1016/j.enconman.2020.112869 10.1016/j.ijepes.2015.04.019 10.1016/j.asoc.2019.105587 10.1016/j.apenergy.2018.11.012 10.1016/j.enconman.2019.06.041 10.1016/j.ymssp.2017.03.035 10.1016/j.renene.2014.11.084 |
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| References | Du, Wang, Yang, Niu (b0070) 2019; 80 Liu, Cao, Zhang, Wang, Huang, Luo (b0140) 2020; 121 Xu, Liu, Long (b0270) 2020; 37 Tian, Hao, Hu (b0225) 2018; 231 Wu, Wang, Chen, Du, Yang (b0260) 2020; 146 Heng, Wang, Xiao, Lu (b0095) 2017; 208 Singh, Dhiman (b0210) 2018; 27 Cheng, Wang (b0055) 2020; 92 Yang, Wang, Lu, Niu, Du (b0295) 2019; 222 Jiang, Liu (b0110) 2019; 82 Mao, Ling, Chang, Hatziargyriou, Zhang, Ding (b0165) 2016; 4 Chen (b0045) 1996; 81 Yager (b0275) 1988; 18 Zhang, Zhang, Wang, Niu (b0305) 2020; 277 Yager (b0280) 1996; 11 Erdem, Shi (b0075) 2011; 88 Rodrigues Moreno, Gomes da Silva, Cocco Mariani, dos Santos Coelho (b0200) 2020; 213 Jiang, Yang, Heng (b0115) 2019; 235 Aasim, Singh, Mohapatra (b0005) 2019; 136 Xia, Xu (b0265) 2011; 52 Peng, Peng, Fu, Lu, Tang, Wang, Li (b0185) 2020; 207 Liu, Han (b0145) 2008; 48 Yang, Zhu, Li, Li (b0290) 2020; 87 Zhao, Chen, Wu, Chen, Liu (b0315) 2017; 11 Mirjalili, Gandomi, Mirjalili, Saremi, Faris, Mirjalili (b0170) 2017; 114 Ridha, Gomes, Hizam, Mirjalili (b0195) 2020; 153 Liu, Qin, Zhang, Pei, Jiang, Feng, Zhou (b0150) 2020; 260 Ding, Meng (b0060) 2020; 93 Pei, Qin, Zhang, Yao, Wang, Wang, Liu, Jiang, Zhou, Yi (b0180) 2019; 196 Wang, Xiong (b0245) 2014; 76 Guo, Zhang, Liu, Wang (b0085) 2020; 8 Wang, Du, Hao, Ma, Niu, Yang (b0235) 2020; 255 Fei, He (b0080) 2015; 73 Dong, Sun, Li (b0065) 2017; 102 Shukur, Lee (b0205) 2015; 76 Wang, Wei, Wu, Yin (b0250) 2018; 47 Song, Chissom (b0215) 1993; 54 Chen, Zeng, Zhou, Du, Lu (b0035) 2018; 165 Bisht, Kumar (b0010) 2016; 64 Chen, Yu (b0040) 2014; 113 Zhang, Wei, Tan (b0300) 2020; 190 Yan, Zhang, Liu, Han, Li, Lu (b0285) 2018; 33 Physiology, Andrews, Andrews (b0190) 1980; 210 Cai, Zhang, Zheng, Leung (b0025) 2015; 74 Liu, Duan, Chen, Wu (b0135) 2019; 199 Torra, Narukawa (b0230) 2009; 2009 Jahangir, Golkar, Alhameli, Mazouz, Ahmadian, Elkamel (b0105) 2020; 38 Huarng (b0100) 2001; 123 Lee, Baldick (b0120) 2014; 5 He, Chen, Shang, Li, Li, Xu (b0090) 2019; 76 Wang, Li, Lu (b0240) 2018; 71 Cheng, Liu, Bourgeois, Wu, Haupt (b0050) 2017; 107 Li, Zhu, Yang, Li (b0125) 2019; 174 Zhao, Guo, Xiao, Wang, Chi, Guo (b0310) 2017; 197 Campbell, J. Y., & Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? | Review of Financial Studies | onAcademic. https://www.onacademic.com/detail/journal_1000037294651810_384a.html. Liu, Jin, Zuo, Feng (b0160) 2017; 95 Song, Chissom (b0220) 1994; 62 Liu, Jiang, Zhang, Niu (b0155) 2020; 259 Pearre, Swan (b0175) 2018; 27 Wang, Xie, Hu, Xiong (b0255) 2018; 163 Li, Wang, Lu, Guo (b0130) 2018; 116 Bo, Niu, Wang (b0020) 2019; 7 Bisht, Kumar (b0015) 2019; 4 Pearre (10.1016/j.eswa.2020.114364_b0175) 2018; 27 Erdem (10.1016/j.eswa.2020.114364_b0075) 2011; 88 Fei (10.1016/j.eswa.2020.114364_b0080) 2015; 73 Wang (10.1016/j.eswa.2020.114364_b0250) 2018; 47 Bisht (10.1016/j.eswa.2020.114364_b0015) 2019; 4 Chen (10.1016/j.eswa.2020.114364_b0040) 2014; 113 Wang (10.1016/j.eswa.2020.114364_b0235) 2020; 255 Yager (10.1016/j.eswa.2020.114364_b0275) 1988; 18 Xia (10.1016/j.eswa.2020.114364_b0265) 2011; 52 Liu (10.1016/j.eswa.2020.114364_b0160) 2017; 95 He (10.1016/j.eswa.2020.114364_b0090) 2019; 76 Jiang (10.1016/j.eswa.2020.114364_b0110) 2019; 82 Ridha (10.1016/j.eswa.2020.114364_b0195) 2020; 153 Liu (10.1016/j.eswa.2020.114364_b0145) 2008; 48 Chen (10.1016/j.eswa.2020.114364_b0035) 2018; 165 10.1016/j.eswa.2020.114364_b0030 Mao (10.1016/j.eswa.2020.114364_b0165) 2016; 4 Wang (10.1016/j.eswa.2020.114364_b0245) 2014; 76 Rodrigues Moreno (10.1016/j.eswa.2020.114364_b0200) 2020; 213 Peng (10.1016/j.eswa.2020.114364_b0185) 2020; 207 Wang (10.1016/j.eswa.2020.114364_b0240) 2018; 71 Li (10.1016/j.eswa.2020.114364_b0130) 2018; 116 Jiang (10.1016/j.eswa.2020.114364_b0115) 2019; 235 Yager (10.1016/j.eswa.2020.114364_b0280) 1996; 11 Liu (10.1016/j.eswa.2020.114364_b0150) 2020; 260 Yang (10.1016/j.eswa.2020.114364_b0290) 2020; 87 Wu (10.1016/j.eswa.2020.114364_b0260) 2020; 146 Huarng (10.1016/j.eswa.2020.114364_b0100) 2001; 123 Singh (10.1016/j.eswa.2020.114364_b0210) 2018; 27 Dong (10.1016/j.eswa.2020.114364_b0065) 2017; 102 Chen (10.1016/j.eswa.2020.114364_b0045) 1996; 81 Ding (10.1016/j.eswa.2020.114364_b0060) 2020; 93 Cai (10.1016/j.eswa.2020.114364_b0025) 2015; 74 Zhang (10.1016/j.eswa.2020.114364_b0300) 2020; 190 Bisht (10.1016/j.eswa.2020.114364_b0010) 2016; 64 Du (10.1016/j.eswa.2020.114364_b0070) 2019; 80 Wang (10.1016/j.eswa.2020.114364_b0255) 2018; 163 Guo (10.1016/j.eswa.2020.114364_b0085) 2020; 8 Liu (10.1016/j.eswa.2020.114364_b0155) 2020; 259 Mirjalili (10.1016/j.eswa.2020.114364_b0170) 2017; 114 Pei (10.1016/j.eswa.2020.114364_b0180) 2019; 196 Cheng (10.1016/j.eswa.2020.114364_b0050) 2017; 107 Physiology (10.1016/j.eswa.2020.114364_b0190) 1980; 210 Bo (10.1016/j.eswa.2020.114364_b0020) 2019; 7 Shukur (10.1016/j.eswa.2020.114364_b0205) 2015; 76 Torra (10.1016/j.eswa.2020.114364_b0230) 2009; 2009 Zhang (10.1016/j.eswa.2020.114364_b0305) 2020; 277 Aasim (10.1016/j.eswa.2020.114364_b0005) 2019; 136 Zhao (10.1016/j.eswa.2020.114364_b0310) 2017; 197 Heng (10.1016/j.eswa.2020.114364_b0095) 2017; 208 Li (10.1016/j.eswa.2020.114364_b0125) 2019; 174 Cheng (10.1016/j.eswa.2020.114364_b0055) 2020; 92 Yan (10.1016/j.eswa.2020.114364_b0285) 2018; 33 Tian (10.1016/j.eswa.2020.114364_b0225) 2018; 231 Xu (10.1016/j.eswa.2020.114364_b0270) 2020; 37 Jahangir (10.1016/j.eswa.2020.114364_b0105) 2020; 38 Liu (10.1016/j.eswa.2020.114364_b0135) 2019; 199 Lee (10.1016/j.eswa.2020.114364_b0120) 2014; 5 Zhao (10.1016/j.eswa.2020.114364_b0315) 2017; 11 Liu (10.1016/j.eswa.2020.114364_b0140) 2020; 121 Song (10.1016/j.eswa.2020.114364_b0220) 1994; 62 Yang (10.1016/j.eswa.2020.114364_b0295) 2019; 222 Song (10.1016/j.eswa.2020.114364_b0215) 1993; 54 |
| References_xml | – volume: 136 start-page: 758 year: 2019 end-page: 768 ident: b0005 article-title: Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting publication-title: Renewable Energy – volume: 52 start-page: 395 year: 2011 end-page: 407 ident: b0265 article-title: Hesitant fuzzy information aggregation in decision making publication-title: International Journal of Approximate Reasoning – volume: 190 start-page: 115615 year: 2020 ident: b0300 article-title: An adaptive hybrid model for short term wind speed forecasting publication-title: Energy – volume: 33 start-page: 3276 year: 2018 end-page: 3284 ident: b0285 article-title: Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping publication-title: IEEE Transactions on Power Systems – volume: 76 start-page: 717 year: 2019 end-page: 740 ident: b0090 article-title: A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm publication-title: Applied Mathematical Modelling – volume: 81 start-page: 311 year: 1996 end-page: 319 ident: b0045 article-title: Forecasting enrollments based on fuzzy time series publication-title: Fuzzy Sets and Systems – volume: 102 start-page: 241 year: 2017 end-page: 257 ident: b0065 article-title: A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China publication-title: Renewable Energy – volume: 71 start-page: 783 year: 2018 end-page: 799 ident: b0240 article-title: Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China publication-title: Applied Soft Computing – volume: 197 start-page: 183 year: 2017 end-page: 202 ident: b0310 article-title: Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method publication-title: Applied Energy – volume: 210 start-page: 559 year: 1980 end-page: 574 ident: b0190 article-title: Communication between individuals in salp chains. II. Physiology publication-title: Proceedings of the Royal Society of London. Series B. Biological Sciences – volume: 87 start-page: 105972 year: 2020 ident: b0290 article-title: A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight publication-title: Applied Soft Computing – volume: 11 start-page: 49 year: 1996 end-page: 73 ident: b0280 article-title: Quantifier guided aggregation using OWA operators publication-title: International Journal of Intelligent Systems – volume: 174 start-page: 1219 year: 2019 end-page: 1237 ident: b0125 article-title: An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization publication-title: Energy – volume: 277 start-page: 115561 year: 2020 ident: b0305 article-title: Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting publication-title: Applied Energy – volume: 18 start-page: 183 year: 1988 end-page: 190 ident: b0275 article-title: On ordered weighted averaging aggregation operators in multicriteria decisionmaking publication-title: IEEE Transactions on Systems, Man, and Cybernetics – volume: 213 start-page: 112869 year: 2020 ident: b0200 article-title: Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network publication-title: Energy Conversion and Management – volume: 11 start-page: 68 year: 2017 end-page: 75 ident: b0315 article-title: LSTM network: A deep learning approach for short-term traffic forecast publication-title: IET Intelligent Transport Systems – volume: 199 start-page: 111975 year: 2019 ident: b0135 article-title: A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model publication-title: Energy Conversion and Management – volume: 82 start-page: 105587 year: 2019 ident: b0110 article-title: Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting publication-title: Applied Soft Computing – volume: 255 start-page: 109855 year: 2020 ident: b0235 article-title: An innovative hybrid model based on outlier detection and correction algorithm and heuristic intelligent optimization algorithm for daily air quality index forecasting publication-title: Journal of Environmental Management – volume: 62 start-page: 1 year: 1994 end-page: 8 ident: b0220 article-title: Forecasting enrollments with fuzzy time series — part II publication-title: Fuzzy Sets and Systems – volume: 48 start-page: 77 year: 2008 end-page: 97 ident: b0145 article-title: Orness and parameterized RIM quantifier aggregation with OWA operators: A summary publication-title: International Journal of Approximate Reasoning – volume: 4 start-page: 655 year: 2019 end-page: 669 ident: b0015 article-title: Hesitant fuzzy set based computational method for financial time series forecasting publication-title: Granular Computing – volume: 207 start-page: 112524 year: 2020 ident: b0185 article-title: A novel deep learning ensemble model with data denoising for short-term wind speed forecasting publication-title: Energy Conversion and Management – volume: 231 start-page: 301 year: 2018 end-page: 319 ident: b0225 article-title: A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization publication-title: Applied Energy – volume: 93 start-page: 106350 year: 2020 ident: b0060 article-title: Point and interval forecasting for wind speed based on linear component extraction publication-title: Applied Soft Computing – volume: 95 start-page: 468 year: 2017 end-page: 487 ident: b0160 article-title: Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis publication-title: Mechanical Systems and Signal Processing – volume: 73 start-page: 625 year: 2015 end-page: 631 ident: b0080 article-title: Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine publication-title: International Journal of Electrical Power & Energy Systems – reference: Campbell, J. Y., & Thompson, S. B. (2008). Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average? | Review of Financial Studies | onAcademic. https://www.onacademic.com/detail/journal_1000037294651810_384a.html. – volume: 121 start-page: 106056 year: 2020 ident: b0140 article-title: Short-term wind speed forecasting based on the Jaya-SVM model publication-title: International Journal of Electrical Power & Energy Systems – volume: 259 start-page: 114137 year: 2020 ident: b0155 article-title: A combined forecasting model for time series: Application to short-term wind speed forecasting publication-title: Applied Energy – volume: 27 start-page: 180 year: 2018 end-page: 191 ident: b0175 article-title: Statistical approach for improved wind speed forecasting for wind power production publication-title: Sustainable Energy Technologies and Assessments – volume: 165 start-page: 681 year: 2018 end-page: 695 ident: b0035 article-title: Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization publication-title: Energy Conversion and Management – volume: 64 start-page: 557 year: 2016 end-page: 568 ident: b0010 article-title: Fuzzy time series forecasting method based on hesitant fuzzy sets publication-title: Expert Systems with Applications – volume: 235 start-page: 786 year: 2019 end-page: 801 ident: b0115 article-title: A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting publication-title: Applied Energy – volume: 123 start-page: 387 year: 2001 end-page: 394 ident: b0100 article-title: Effective lengths of intervals to improve forecasting in fuzzy time series publication-title: Fuzzy Sets and Systems – volume: 260 start-page: 114259 year: 2020 ident: b0150 article-title: Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model publication-title: Applied Energy – volume: 7 start-page: 178063 year: 2019 end-page: 178081 ident: b0020 article-title: Wind speed forecasting system based on the variational mode decomposition strategy and immune selection multi-objective dragonfly optimization algorithm publication-title: IEEE Access – volume: 107 start-page: 340 year: 2017 end-page: 351 ident: b0050 article-title: Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation publication-title: Renewable Energy – volume: 114 start-page: 163 year: 2017 end-page: 191 ident: b0170 article-title: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software – volume: 146 start-page: 149 year: 2020 end-page: 165 ident: b0260 article-title: A novel hybrid system based on multi-objective optimization for wind speed forecasting publication-title: Renewable Energy – volume: 76 start-page: 637 year: 2015 end-page: 647 ident: b0205 article-title: Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA publication-title: Renewable Energy – volume: 74 start-page: 61 year: 2015 end-page: 68 ident: b0025 article-title: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression publication-title: Knowledge-Based Systems – volume: 92 start-page: 106294 year: 2020 ident: b0055 article-title: A new combined model based on multi-objective salp swarm optimization for wind speed forecasting publication-title: Applied Soft Computing – volume: 4 start-page: 1206 year: 2016 end-page: 1216 ident: b0165 article-title: A novel short-term wind speed prediction based on MFEC publication-title: IEEE Journal of Emerging and Selected Topics in Power Electronics – volume: 163 start-page: 384 year: 2018 end-page: 406 ident: b0255 article-title: Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning publication-title: Energy Conversion and Management – volume: 2009 start-page: 1378 year: 2009 end-page: 1382 ident: b0230 article-title: On hesitant fuzzy sets and decision publication-title: IEEE International Conference on Fuzzy Systems – volume: 222 start-page: 942 year: 2019 end-page: 959 ident: b0295 article-title: Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China publication-title: Journal of Cleaner Production – volume: 47 start-page: 90 year: 2018 end-page: 104 ident: b0250 article-title: Oil and the short-term predictability of stock return volatility publication-title: Journal of Empirical Finance – volume: 208 start-page: 845 year: 2017 end-page: 866 ident: b0095 article-title: Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting publication-title: Applied Energy – volume: 54 start-page: 269 year: 1993 end-page: 277 ident: b0215 article-title: Fuzzy time series and its models publication-title: Fuzzy Sets and Systems – volume: 196 start-page: 779 year: 2019 end-page: 792 ident: b0180 article-title: Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network publication-title: Energy Conversion and Management – volume: 5 start-page: 501 year: 2014 end-page: 510 ident: b0120 article-title: Short-term wind power ensemble prediction based on Gaussian processes and neural networks publication-title: IEEE Transactions on Smart Grid – volume: 8 start-page: 33039 year: 2020 end-page: 33059 ident: b0085 article-title: A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation publication-title: IEEE Access – volume: 80 start-page: 93 year: 2019 end-page: 106 ident: b0070 article-title: A novel hybrid model for short-term wind power forecasting publication-title: Applied Soft Computing – volume: 38 start-page: 100601 year: 2020 ident: b0105 article-title: Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN publication-title: Sustainable Energy Technologies and Assessments – volume: 153 start-page: 1330 year: 2020 end-page: 1345 ident: b0195 article-title: Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system publication-title: Renewable Energy – volume: 76 start-page: 526 year: 2014 end-page: 541 ident: b0245 article-title: A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China publication-title: Energy – volume: 37 start-page: 100582 year: 2020 ident: b0270 article-title: A distributed computing framework for wind speed big data forecasting on Apache Spark publication-title: Sustainable Energy Technologies and Assessments – volume: 113 start-page: 690 year: 2014 end-page: 705 ident: b0040 article-title: Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach publication-title: Applied Energy – volume: 88 start-page: 1405 year: 2011 end-page: 1414 ident: b0075 article-title: ARMA based approaches for forecasting the tuple of wind speed and direction publication-title: Applied Energy – volume: 116 start-page: 669 year: 2018 end-page: 684 ident: b0130 article-title: Research and application of a combined model based on variable weight for short term wind speed forecasting publication-title: Renewable Energy – volume: 27 start-page: 370 year: 2018 end-page: 385 ident: b0210 article-title: A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches publication-title: Journal of Computational Science – volume: 92 start-page: 106294 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0055 article-title: A new combined model based on multi-objective salp swarm optimization for wind speed forecasting publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106294 – volume: 114 start-page: 163 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0170 article-title: Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.07.002 – volume: 190 start-page: 115615 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0300 article-title: An adaptive hybrid model for short term wind speed forecasting publication-title: Energy doi: 10.1016/j.energy.2019.06.132 – volume: 260 start-page: 114259 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0150 article-title: Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model publication-title: Applied Energy doi: 10.1016/j.apenergy.2019.114259 – volume: 62 start-page: 1 issue: 1 year: 1994 ident: 10.1016/j.eswa.2020.114364_b0220 article-title: Forecasting enrollments with fuzzy time series — part II publication-title: Fuzzy Sets and Systems doi: 10.1016/0165-0114(94)90067-1 – volume: 222 start-page: 942 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0295 article-title: Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China publication-title: Journal of Cleaner Production doi: 10.1016/j.jclepro.2019.03.036 – volume: 5 start-page: 501 issue: 1 year: 2014 ident: 10.1016/j.eswa.2020.114364_b0120 article-title: Short-term wind power ensemble prediction based on Gaussian processes and neural networks publication-title: IEEE Transactions on Smart Grid doi: 10.1109/TSG.2013.2280649 – volume: 231 start-page: 301 issue: March year: 2018 ident: 10.1016/j.eswa.2020.114364_b0225 article-title: A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization publication-title: Applied Energy doi: 10.1016/j.apenergy.2018.09.012 – volume: 47 start-page: 90 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0250 article-title: Oil and the short-term predictability of stock return volatility publication-title: Journal of Empirical Finance doi: 10.1016/j.jempfin.2018.03.002 – volume: 113 start-page: 690 year: 2014 ident: 10.1016/j.eswa.2020.114364_b0040 article-title: Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach publication-title: Applied Energy doi: 10.1016/j.apenergy.2013.08.025 – volume: 93 start-page: 106350 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0060 article-title: Point and interval forecasting for wind speed based on linear component extraction publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106350 – volume: 146 start-page: 149 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0260 article-title: A novel hybrid system based on multi-objective optimization for wind speed forecasting publication-title: Renewable Energy doi: 10.1016/j.renene.2019.04.157 – volume: 208 start-page: 845 issue: August year: 2017 ident: 10.1016/j.eswa.2020.114364_b0095 article-title: Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting publication-title: Applied Energy doi: 10.1016/j.apenergy.2017.09.063 – volume: 197 start-page: 183 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0310 article-title: Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method publication-title: Applied Energy doi: 10.1016/j.apenergy.2017.04.017 – volume: 165 start-page: 681 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0035 article-title: Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2018.03.098 – volume: 87 start-page: 105972 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0290 article-title: A novel combined forecasting system for air pollutants concentration based on fuzzy theory and optimization of aggregation weight publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.105972 – volume: 199 start-page: 111975 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0135 article-title: A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2019.111975 – volume: 2009 start-page: 1378 year: 2009 ident: 10.1016/j.eswa.2020.114364_b0230 article-title: On hesitant fuzzy sets and decision publication-title: IEEE International Conference on Fuzzy Systems – volume: 64 start-page: 557 year: 2016 ident: 10.1016/j.eswa.2020.114364_b0010 article-title: Fuzzy time series forecasting method based on hesitant fuzzy sets publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2016.07.044 – volume: 11 start-page: 68 issue: 2 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0315 article-title: LSTM network: A deep learning approach for short-term traffic forecast publication-title: IET Intelligent Transport Systems doi: 10.1049/iet-its.2016.0208 – volume: 107 start-page: 340 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0050 article-title: Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation publication-title: Renewable Energy doi: 10.1016/j.renene.2017.02.014 – volume: 76 start-page: 526 year: 2014 ident: 10.1016/j.eswa.2020.114364_b0245 article-title: A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China publication-title: Energy doi: 10.1016/j.energy.2014.08.064 – volume: 52 start-page: 395 issue: 3 year: 2011 ident: 10.1016/j.eswa.2020.114364_b0265 article-title: Hesitant fuzzy information aggregation in decision making publication-title: International Journal of Approximate Reasoning doi: 10.1016/j.ijar.2010.09.002 – volume: 4 start-page: 655 issue: 4 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0015 article-title: Hesitant fuzzy set based computational method for financial time series forecasting publication-title: Granular Computing doi: 10.1007/s41066-018-00144-4 – volume: 102 start-page: 241 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0065 article-title: A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China publication-title: Renewable Energy doi: 10.1016/j.renene.2016.10.030 – volume: 54 start-page: 269 issue: 3 year: 1993 ident: 10.1016/j.eswa.2020.114364_b0215 article-title: Fuzzy time series and its models publication-title: Fuzzy Sets and Systems doi: 10.1016/0165-0114(93)90372-O – volume: 11 start-page: 49 issue: 1 year: 1996 ident: 10.1016/j.eswa.2020.114364_b0280 article-title: Quantifier guided aggregation using OWA operators publication-title: International Journal of Intelligent Systems doi: 10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z – volume: 71 start-page: 783 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0240 article-title: Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.07.030 – volume: 81 start-page: 311 issue: 3 year: 1996 ident: 10.1016/j.eswa.2020.114364_b0045 article-title: Forecasting enrollments based on fuzzy time series publication-title: Fuzzy Sets and Systems doi: 10.1016/0165-0114(95)00220-0 – volume: 74 start-page: 61 year: 2015 ident: 10.1016/j.eswa.2020.114364_b0025 article-title: A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2014.11.003 – volume: 255 start-page: 109855 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0235 article-title: An innovative hybrid model based on outlier detection and correction algorithm and heuristic intelligent optimization algorithm for daily air quality index forecasting publication-title: Journal of Environmental Management doi: 10.1016/j.jenvman.2019.109855 – volume: 136 start-page: 758 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0005 article-title: Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting publication-title: Renewable Energy doi: 10.1016/j.renene.2019.01.031 – volume: 163 start-page: 384 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0255 article-title: Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2018.02.034 – volume: 76 start-page: 717 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0090 article-title: A novel wind speed forecasting model based on moving window and multi-objective particle swarm optimization algorithm publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2019.07.001 – volume: 33 start-page: 3276 issue: 3 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0285 article-title: Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping publication-title: IEEE Transactions on Power Systems doi: 10.1109/TPWRS.2017.2787667 – volume: 153 start-page: 1330 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0195 article-title: Multiple scenarios multi-objective salp swarm optimization for sizing of standalone photovoltaic system publication-title: Renewable Energy doi: 10.1016/j.renene.2020.02.016 – volume: 277 start-page: 115561 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0305 article-title: Hybrid system based on a multi-objective optimization and kernel approximation for multi-scale wind speed forecasting publication-title: Applied Energy doi: 10.1016/j.apenergy.2020.115561 – volume: 7 start-page: 178063 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0020 article-title: Wind speed forecasting system based on the variational mode decomposition strategy and immune selection multi-objective dragonfly optimization algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2957062 – volume: 123 start-page: 387 issue: 3 year: 2001 ident: 10.1016/j.eswa.2020.114364_b0100 article-title: Effective lengths of intervals to improve forecasting in fuzzy time series publication-title: Fuzzy Sets and Systems doi: 10.1016/S0165-0114(00)00057-9 – volume: 38 start-page: 100601 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0105 article-title: Short-term wind speed forecasting framework based on stacked denoising auto-encoders with rough ANN publication-title: Sustainable Energy Technologies and Assessments doi: 10.1016/j.seta.2019.100601 – ident: 10.1016/j.eswa.2020.114364_b0030 – volume: 4 start-page: 1206 issue: 4 year: 2016 ident: 10.1016/j.eswa.2020.114364_b0165 article-title: A novel short-term wind speed prediction based on MFEC publication-title: IEEE Journal of Emerging and Selected Topics in Power Electronics doi: 10.1109/JESTPE.2016.2590834 – volume: 27 start-page: 180 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0175 article-title: Statistical approach for improved wind speed forecasting for wind power production publication-title: Sustainable Energy Technologies and Assessments doi: 10.1016/j.seta.2018.04.010 – volume: 210 start-page: 559 issue: 1181 year: 1980 ident: 10.1016/j.eswa.2020.114364_b0190 article-title: Communication between individuals in salp chains. II. Physiology publication-title: Proceedings of the Royal Society of London. Series B. Biological Sciences – volume: 37 start-page: 100582 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0270 article-title: A distributed computing framework for wind speed big data forecasting on Apache Spark publication-title: Sustainable Energy Technologies and Assessments doi: 10.1016/j.seta.2019.100582 – volume: 27 start-page: 370 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0210 article-title: A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches publication-title: Journal of Computational Science doi: 10.1016/j.jocs.2018.05.008 – volume: 88 start-page: 1405 issue: 4 year: 2011 ident: 10.1016/j.eswa.2020.114364_b0075 article-title: ARMA based approaches for forecasting the tuple of wind speed and direction publication-title: Applied Energy doi: 10.1016/j.apenergy.2010.10.031 – volume: 121 start-page: 106056 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0140 article-title: Short-term wind speed forecasting based on the Jaya-SVM model publication-title: International Journal of Electrical Power & Energy Systems doi: 10.1016/j.ijepes.2020.106056 – volume: 48 start-page: 77 issue: 1 year: 2008 ident: 10.1016/j.eswa.2020.114364_b0145 article-title: Orness and parameterized RIM quantifier aggregation with OWA operators: A summary publication-title: International Journal of Approximate Reasoning doi: 10.1016/j.ijar.2007.05.006 – volume: 259 start-page: 114137 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0155 article-title: A combined forecasting model for time series: Application to short-term wind speed forecasting publication-title: Applied Energy doi: 10.1016/j.apenergy.2019.114137 – volume: 207 start-page: 112524 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0185 article-title: A novel deep learning ensemble model with data denoising for short-term wind speed forecasting publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2020.112524 – volume: 18 start-page: 183 issue: 1 year: 1988 ident: 10.1016/j.eswa.2020.114364_b0275 article-title: On ordered weighted averaging aggregation operators in multicriteria decisionmaking publication-title: IEEE Transactions on Systems, Man, and Cybernetics doi: 10.1109/21.87068 – volume: 80 start-page: 93 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0070 article-title: A novel hybrid model for short-term wind power forecasting publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.03.035 – volume: 8 start-page: 33039 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0085 article-title: A Combined Strategy for Wind Speed Forecasting Using Data Preprocessing and Weight Coefficients Optimization Calculation publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2973746 – volume: 116 start-page: 669 year: 2018 ident: 10.1016/j.eswa.2020.114364_b0130 article-title: Research and application of a combined model based on variable weight for short term wind speed forecasting publication-title: Renewable Energy doi: 10.1016/j.renene.2017.09.089 – volume: 174 start-page: 1219 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0125 article-title: An innovative hybrid system for wind speed forecasting based on fuzzy preprocessing scheme and multi-objective optimization publication-title: Energy doi: 10.1016/j.energy.2019.02.194 – volume: 213 start-page: 112869 year: 2020 ident: 10.1016/j.eswa.2020.114364_b0200 article-title: Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2020.112869 – volume: 73 start-page: 625 year: 2015 ident: 10.1016/j.eswa.2020.114364_b0080 article-title: Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine publication-title: International Journal of Electrical Power & Energy Systems doi: 10.1016/j.ijepes.2015.04.019 – volume: 82 start-page: 105587 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0110 article-title: Variable weights combined model based on multi-objective optimization for short-term wind speed forecasting publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.105587 – volume: 235 start-page: 786 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0115 article-title: A hybrid forecasting system based on fuzzy time series and multi-objective optimization for wind speed forecasting publication-title: Applied Energy doi: 10.1016/j.apenergy.2018.11.012 – volume: 196 start-page: 779 year: 2019 ident: 10.1016/j.eswa.2020.114364_b0180 article-title: Wind speed prediction method based on Empirical Wavelet Transform and New Cell Update Long Short-Term Memory network publication-title: Energy Conversion and Management doi: 10.1016/j.enconman.2019.06.041 – volume: 95 start-page: 468 year: 2017 ident: 10.1016/j.eswa.2020.114364_b0160 article-title: Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2017.03.035 – volume: 76 start-page: 637 year: 2015 ident: 10.1016/j.eswa.2020.114364_b0205 article-title: Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA publication-title: Renewable Energy doi: 10.1016/j.renene.2014.11.084 |
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| Snippet | •A novel hesitant fuzzy wind speed forecasting system is proposed for the first time.•Multi-fuzzification methods are proposed to deal with the non-determinism... Owing to the nondeterministic nature of wind speed, the conventional fuzzy time series forecasting model has difficulty in establishing a common membership... |
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| SubjectTerms | Algorithms Artificial intelligence Decomposition Feature extraction Forecasting Fuzzy sets Fuzzy time series forecasting Hesitant fuzzy sets Intervals Mathematical models Model accuracy Multifuzzification methods Multiobjective optimization algorithm Multiple objective analysis Optimization Optimization algorithms Performance evaluation Time series Wind speed |
| Title | A hesitant fuzzy wind speed forecasting system with novel defuzzification method and multi-objective optimization algorithm |
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