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However, there exists considerable randomness and instabilities in wind speed data so that it is difficult to obtain accurate forecasting results. In this paper, we propose a novel fuzzy inference method based hybrid model for accurate wind speed forecasting. In this hybrid model, we adopt two strategies to enhance the estimation performance. On one hand, we propose the purification machine which utilize the Irregular Information Reduction Module (IIRM) and the Irrelevant Variable Reduction Module (IVRM) to reduce the randomness and instabilities of the data and to eliminate the variables with zero or negative effect in the wind speed time series. On the other hand, we adopt the developed Single-Input-Rule-Modules based Fuzzy Inference System (SIRM-FIS), the functionally weighted SIRM-FIS (FWSIRM-FIS) to realize the prediction of wind speed. This FWSIRM-FIS utilizes the multi-variable functional weights to dynamically measure the importance of the input variables so that the input-output mapping can be strengthened and more accurate forecasting results can be achieved. Furthermore, detailed experiments and comparisons are given. Experimental results demonstrate that the proposed FWSIRM-FIS and purification machine contributes greatly to deal with the randomness and instability in the wind speed data and yield more accurate forecasting results than those existing excellent forecasting models.<\/jats:p>","DOI":"10.3233\/jifs-200205","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T13:06:12Z","timestamp":1591707972000},"page":"4059-4070","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel purification machine and fuzzy inference method based hybrid model for wind speed forecasting"],"prefix":"10.1177","volume":"39","author":[{"given":"Weina","family":"Ren","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Automation, Shandong Labor Vocational and Technical College, Jinan, Shandong, China"}]},{"given":"Chengdong","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Buildings Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, Shandong, China"}]},{"given":"Peng","family":"Wen","sequence":"additional","affiliation":[{"name":"Jinan Municipal Engineering Design and Research Institute (Group) Co., Ltd, Jinan, Shandong, China"}]}],"member":"179","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2006.10.007"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2004.09.013"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2010.10.031"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/en9020109"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0960-1481(01)00193-8"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1018628609742"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2003.11.009"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2006.12.001"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSTE.2015.2434387"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2016.11.111"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.04.021"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2017.11.053"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2015.05.009"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2019.103269"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2018.11.018"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.seta.2018.01.001"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2011.04.015"},{"key":"e_1_3_1_21_2","first-page":"410","article-title":"SIRMs dynamically connected fuzzy inference model and its applications","volume":"3","author":"Yubazaki N.","year":"1997","unstructured":"YubazakiN., SIRMs dynamically connected fuzzy inference model and its applications, Proc. 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