{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:41:47Z","timestamp":1777704107179,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T00:00:00Z","timestamp":1579478400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,2,6]]},"abstract":"<jats:p>The ability to accurately and reliably predict annual electricity demand is essential in modern society for effective planning, economic development, and to ensure the sustainability of the electricity supply. Considering the correlation between annual electricity demand and economic development, as well as annual electricity demand under low-carbon-economy targets, this paper proposes an improved quantum clustering algorithm (particle swarm optimization\u2013weighted distance quantum clustering, PSO-WDQC) as a power demand forecasting model. This method can not only improve the accuracy of predictions but also accurately evaluate the economic development of a region. To demonstrate this ability, the\u00a0paper applies the proposed method to low-dimensional Iris data as well as high-dimensional Wine data in order to verify the effectiveness of the method. Then, the method is combined with ridge regression to predict the demand for electricity under the low-carbon-economy target of China. The experimental results show that the method can accurately predict annual power demand with a relative error of 0.1674%. Moreover, the model accurately reflects that the Chinese economy has entered a new normal state since 2012, meaning that the economic growth rate has changed from high-speed to medium-high-speed.<\/jats:p>","DOI":"10.3233\/jifs-191325","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T12:30:30Z","timestamp":1579609830000},"page":"2359-2367","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["An improved quantum clustering algorithm with weighted distance based on PSO and research on the prediction of electrical power demand"],"prefix":"10.1177","volume":"38","author":[{"given":"Decheng","family":"Fan","sequence":"first","affiliation":[{"name":"School of Economics and Management, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhilong","family":"Song","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Harbin Engineering University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Jon","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Science, Pyongyang, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JuHyok","family":"U","sequence":"additional","affiliation":[{"name":"Department of Physics, Kim Chaek University of Technology, Pyongyang, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,1,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/15567249.2014.893040"},{"issue":"2","key":"e_1_3_2_3_2","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1016\/j.enconman.2010.08.023","article-title":"Annual electricity consumption analysis and forecasting of China based on few observations methods","volume":"52","author":"Meng M.","year":"2011","unstructured":"MengM. and NiuD., Annual electricity consumption analysis and forecasting of China based on few observations methods, Energy Conversion and Management 52(2) (2011), 953\u2013957.","journal-title":"Energy Conversion and Management"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/en5114430"},{"issue":"6","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.omega.2011.07.007","article-title":"Forecasting short-term electricity consumption using the adaptive grey-based approach\u2014 An Asian case","volume":"40","author":"Li D.-C.","year":"2012","unstructured":"LiD.-C., ChangC.-J., ChenC.-C. and ChenW.-C., Forecasting short-term electricity consumption using the adaptive grey-based approach\u2014 An Asian case, Omega 40(6) (2012), 767\u2013773.","journal-title":"Omega"},{"issue":"3","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1007\/s11063-017-9627-1","article-title":"A hybrid model equipped with the minimum cycle decomposition concept for short-term forecasting of electrical load time series","volume":"46","author":"He Z.","year":"2017","unstructured":"HeZ., LiC., ShenY. and HeA., A hybrid model equipped with the minimum cycle decomposition concept for short-term forecasting of electrical load time series, Neural Processing Letters 46(3) (2017), 1059\u20131081.","journal-title":"Neural Processing Letters"},{"issue":"2017","key":"e_1_3_2_7_2","first-page":"473","article-title":"Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China","volume":"118","author":"Xu N.","unstructured":"XuN., DangY. and GongY., Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China, Energy 118(2017), 473\u2013480.","journal-title":"Energy"},{"issue":"2018","key":"e_1_3_2_8_2","first-page":"427","article-title":"A monthly electricity consumption forecasting method based on vector error correction model and self-adaptive screening method","volume":"95","author":"Guo H.","unstructured":"GuoH., ChenQ., XiaQ., KangC. and ZhangX., A monthly electricity consumption forecasting method based on vector error correction model and self-adaptive screening method, International Journal of Electrical Power & Energy Systems 95(2018), 427\u2013439.","journal-title":"International Journal of Electrical Power & Energy Systems"},{"issue":"2017","key":"e_1_3_2_9_2","first-page":"123","article-title":"A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting","volume":"75","author":"Shao Z.","unstructured":"ShaoZ., ChaoF., YangS.-L. and ZhouK.-L., A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting, Renewable and Sustainable Energy Reviews 75(2017), 123\u2013136.","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"2018","key":"e_1_3_2_10_2","first-page":"1107","article-title":"A bottom-up methodology for long term electricity consumption forecasting of an industrial - Application to pulp and paper in Brazil","volume":"144","author":"Silva F.L.C.","unstructured":"SilvaF.L.C., SouzaR.C., Cyrino OliveiraF.L., LourencoP.M. and CaliliR.F., A bottom-up methodology for long term electricity consumption forecasting of an industrial - Application to pulp and paper in Brazil, Energy 144(2018), 1107\u20131118 sector.","journal-title":"Energy"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2017.12.051"},{"issue":"1","key":"e_1_3_2_12_2","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.enconman.2010.06.053","article-title":"Forecasting energy consumption using a grey model improved by incorporating genetic programming","volume":"52","author":"Lee Y.-S.","year":"2011","unstructured":"LeeY.-S. and TongL.-I., Forecasting energy consumption using a grey model improved by incorporating genetic programming, Energy Conversion and Management 52(1) (2011), 147\u2013152.","journal-title":"Energy Conversion and Management"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2006.11.014"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2011.01.032"},{"issue":"2018","key":"e_1_3_2_15_2","first-page":"327","article-title":"Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China","volume":"157","author":"Wu L.","unstructured":"WuL., GaoX., XiaoY., YangY. and ChenX., Using a novel multi-variable grey model to forecast the electricity consumption of Shandong Province in China, Energy 157(2018), 327\u2013335.","journal-title":"Energy"},{"issue":"2016","key":"e_1_3_2_16_2","first-page":"353","article-title":"Development of an optimization method for the GM(1,N) model","volume":"55","author":"Zeng B.","unstructured":"ZengB., LuoC., LiuS., BaiY. and LiC., Development of an optimization method for the GM(1,N) model, Engineering Applications of Artificial Intelligence 55(2016), 353\u2013362.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"018702","DOI":"10.1103\/PhysRevLett.88.018702","article-title":"Algorithm for data clustering in pattern recognition problems based on quantum mechanics","volume":"88","author":"Horn D.","year":"2002","unstructured":"HornD. and GottliebA., Algorithm for data clustering in pattern recognition problems based on quantum mechanics, Physical Review Letters 88(1) (2002), 018702.","journal-title":"Physical Review Letters"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-007-9033-z"},{"key":"e_1_3_2_19_2","doi-asserted-by":"crossref","unstructured":"ShaoJ. AhmadiZ. KramerS. Prototype-based learning on concept-drifting data streams in: KDD \u201914 Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining New York NY USA 2014 pp. 412\u2013421.","DOI":"10.1145\/2623330.2623609"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.03.008"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.102"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10773-018-3663-0"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2891956"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.12.001"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","unstructured":"Nasiriyan-RadH. AmirkhaniA. NaimiA. MohammadiK. Learning fuzzy cognitive map with PSO algorithm for grading celiac disease in: 2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME) Tehran Iran 2016 pp. 341\u2013346.","DOI":"10.1109\/ICBME.2016.7890984"},{"issue":"10","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"3113","DOI":"10.1007\/s00521-016-2786-6","article-title":"An improved FCM algorithm with adaptive weights based on SA-PSO","volume":"28","author":"Wu Z.","year":"2016","unstructured":"WuZ., WuZ. and ZhangJ., An improved FCM algorithm with adaptive weights based on SA-PSO, Neural Computing and Applications 28(10) (2016), 3113\u20133118.","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1108\/IJHMA-02-2017-0021"},{"issue":"10","key":"e_1_3_2_28_2","doi-asserted-by":"crossref","first-page":"e1007165","DOI":"10.1371\/journal.pcbi.1007165","article-title":"Estimating influenza incidence using search query deceptiveness and generalized ridge regression","volume":"15","author":"Priedhorsky R.","year":"2019","unstructured":"PriedhorskyR., DaughtonA.R., BarnardM., O\u2019ConnellF. and OsthusD., Estimating influenza incidence using search query deceptiveness and generalized ridge regression, PLoS Computational Biology 15(10) (2019), e1007165.","journal-title":"PLoS Computational Biology"},{"issue":"1","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/14765284.2017.1289454","article-title":"Introduction to China\u2019s new normal economy","volume":"15","author":"Zhang J.","year":"2017","unstructured":"ZhangJ. and ChenJ., Introduction to China\u2019s new normal economy, Journal of Chinese Economic and Business Studies 15(1) (2017), 1\u20134.","journal-title":"Journal of Chinese Economic and Business Studies"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191325","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-191325","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-191325","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:28Z","timestamp":1777455628000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-191325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,20]]},"references-count":28,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,2,6]]}},"alternative-id":["10.3233\/JIFS-191325"],"URL":"https:\/\/doi.org\/10.3233\/jifs-191325","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,20]]}}}