{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T14:47:11Z","timestamp":1778424431924,"version":"3.51.4"},"reference-count":52,"publisher":"Emerald","issue":"1","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2023,1,25]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Given the effects of natural and social factors, data on both the supply and demand sides of electricity will produce obvious seasonal fluctuations. The purpose of this article is to propose a new dynamic seasonal grey model based on PSO-SVR to forecast the production and consumption of electric energy.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>In the model design, firstly, the parameters of the SVR are initially optimized by the PSO algorithm for the estimation of the dynamic seasonal operator. Then, the seasonal fluctuations in the electricity demand data are eliminated using the dynamic seasonal operator. After that, the time series after eliminating of the seasonal fluctuations are used as the training set of the DSGM(1, 1) model, and the corresponding fitted, and predicted values are calculated. Finally, the seasonal reduction is performed to obtain the final prediction results.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>This study found that the electricity supply and demand data have obvious seasonal and nonlinear characteristics. The dynamic seasonal grey model based on PSO-SVR performs significantly better than the comparative model for hourly and monthly data as well as for different time durations, indicating that the model is more accurate and robust in seasonal electricity forecasting.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Considering the seasonal and nonlinear fluctuation characteristics of electricity data. In this paper, a dynamic seasonal grey model based on PSO-SVR is established to predict the consumption and production of electric energy.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-10-2021-0159","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T03:29:12Z","timestamp":1656905352000},"page":"141-171","source":"Crossref","is-referenced-by-count":27,"title":["Electric supply and demand forecasting using seasonal grey model based on PSO-SVR"],"prefix":"10.1108","volume":"13","author":[{"given":"Xianting","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0344-4646","authenticated-orcid":false,"given":"Shuhua","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"key2023012406044391900_ref001","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.epsr.2016.06.003","article-title":"ARIMA-based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation","volume":"140","year":"2016","journal-title":"Electric Power Systems Research"},{"issue":"6","key":"key2023012406044391900_ref002","doi-asserted-by":"publisher","first-page":"3186","DOI":"10.1016\/j.eneco.2008.06.003","article-title":"Short term forecasting of electricity prices for MISO hubs: evidence from ARIMA-EGARCH models","volume":"30","year":"2008","journal-title":"Energy Economics"},{"key":"key2023012406044391900_ref003","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1016\/j.energy.2016.09.065","article-title":"Support vector regression with fruit fly optimization algorithm for seasonal electricity consumption forecasting","volume":"115","year":"2016","journal-title":"Energy"},{"key":"key2023012406044391900_ref004","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1016\/j.apenergy.2017.03.070","article-title":"Short-term prediction of electric demand in building sector via hybrid support vector regression","volume":"204","year":"2017","journal-title":"Applied Energy"},{"key":"key2023012406044391900_ref005","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.119952","article-title":"Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach","volume":"222","year":"2021","journal-title":"Energy"},{"key":"key2023012406044391900_ref006","doi-asserted-by":"crossref","unstructured":"Chow, J.H., Wu, F.F. and Momoh, J.A. (2005), \u201cApplied mathematics for restructured electric power systems\u201d, in Applied Mathematics for Restructured Electric Power Systems, Springer, Boston, MA, pp. 1-9.","DOI":"10.1007\/0-387-23471-3_1"},{"key":"key2023012406044391900_ref007","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2020.110343","article-title":"Short-term metropolitan-scale electric load forecasting based on load decomposition and ensemble algorithms","volume":"225","year":"2020","journal-title":"Energy and Buildings"},{"key":"key2023012406044391900_ref008","doi-asserted-by":"publisher","first-page":"1014","DOI":"10.1109\/TPWRS.2002.804943","article-title":"ARIMA models to predict next-day electricity prices","volume":"18","year":"2003","journal-title":"IEEE Transactions on Power Systems"},{"issue":"5","key":"key2023012406044391900_ref009","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/S0167-6911(82)80025-X","article-title":"Control problems of grey systems","volume":"1","year":"1982","journal-title":"Systems & Control Letters"},{"key":"key2023012406044391900_ref010","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.113644","article-title":"A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting","volume":"227","year":"2021","journal-title":"Energy Conversion and Management"},{"key":"key2023012406044391900_ref011","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.apenergy.2015.01.122","article-title":"Short-term smart learning electrical load prediction algorithm for home energy management systems","volume":"147","year":"2015","journal-title":"Applied Energy"},{"issue":"7","key":"key2023012406044391900_ref012","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1007\/s00521-017-3183-5","article-title":"Forecasting of Turkey's monthly electricity demand by seasonal artificial neural network","volume":"31","year":"2017","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"key2023012406044391900_ref013","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.ijepes.2012.08.010","article-title":"Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm","volume":"44","year":"2013","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"key2023012406044391900_ref014","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2019.116779","article-title":"Holt\u2013Winters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption","volume":"193","year":"2020","journal-title":"Energy"},{"key":"key2023012406044391900_ref015","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.apm.2021.03.059","article-title":"Variable order fractional grey model and its application","volume":"97","year":"2021","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref016","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.knosys.2012.08.015","article-title":"A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm","volume":"37","year":"2012","journal-title":"Knowledge-Based Systems"},{"issue":"12","key":"key2023012406044391900_ref017","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.119118","article-title":"Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization","volume":"215","year":"2020","journal-title":"Energy"},{"issue":"3","key":"key2023012406044391900_ref018","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1111\/coin.12059","article-title":"A rolling grey model optimized by particle swarm optimization in economic prediction","volume":"32","year":"2016","journal-title":"Computational Intelligence"},{"key":"key2023012406044391900_ref019","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120492","article-title":"Short-term offshore wind speed forecast by seasonal ARIMA - a comparison against GRU and LSTM","volume":"227","year":"2021","journal-title":"Energy"},{"issue":"7","key":"key2023012406044391900_ref020","doi-asserted-by":"publisher","first-page":"5063","DOI":"10.1016\/j.apm.2015.12.014","article-title":"A novel fractional grey system model and its application","volume":"40","year":"2016","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref021","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.isatra.2020.07.023","article-title":"Fractional grey model based on non-singular exponential kernel and its application in the prediction of electronic waste precious metal content","volume":"107","year":"2020","journal-title":"ISA Transactions"},{"key":"key2023012406044391900_ref022","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106501","article-title":"Grey\u2013Lotka\u2013Volterra model for the competition and cooperation between third-party online payment systems and online banking in China","volume":"95","year":"2020","journal-title":"Applied Soft Computing"},{"key":"key2023012406044391900_ref023","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2020.106083","article-title":"Forecasting hourly supply curves in the Italian Day-Ahead electricity market with a double-seasonal SARMAHX model","volume":"121","year":"2020","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"key2023012406044391900_ref024","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.energy.2021.121145","article-title":"Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization","volume":"234","year":"2021","journal-title":"Energy"},{"key":"key2023012406044391900_ref025","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2020.109945","article-title":"Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion","volume":"130","year":"2020","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"key2023012406044391900_ref026","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2020.118499","article-title":"An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China","volume":"209","year":"2020","journal-title":"Energy"},{"key":"key2023012406044391900_ref027","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1016\/j.apenergy.2010.05.012","article-title":"Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models","volume":"87","year":"2010","journal-title":"Applied Energy"},{"key":"key2023012406044391900_ref028","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.energy.2017.05.126","article-title":"Short-term load forecasting using a two-stage sarimax model","volume":"133","year":"2017","journal-title":"Energy"},{"key":"key2023012406044391900_ref029","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120966","article-title":"One-day-ahead electricity demand forecasting in holidays using discrete-interval moving seasonalities","volume":"231","year":"2021","journal-title":"Energy"},{"key":"key2023012406044391900_ref030","volume-title":"The Nature of Statistical Learning Theory","year":"2013"},{"issue":"1","key":"key2023012406044391900_ref031","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ijepes.2012.04.027","article-title":"Optimization models based on GM (1,1) and seasonal fluctuation for electricity demand forecasting","volume":"43","year":"2012","journal-title":"International Journal of Electrical Power & Energy Systems"},{"key":"key2023012406044391900_ref032","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1016\/j.apm.2017.07.003","article-title":"Grey forecasting method of quarterly hydropower production in China based on a data grouping approach","volume":"51","year":"2017","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref033","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.energy.2018.04.155","article-title":"A seasonal GM(1,1) model for forecasting the electricity consumption of the primary economic sectors","volume":"154","year":"2018","journal-title":"Energy"},{"key":"key2023012406044391900_ref034","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.energy.2020.117460","article-title":"Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors","volume":"200","year":"2020","journal-title":"Energy"},{"key":"key2023012406044391900_ref035","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108002","article-title":"Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables","volume":"113","year":"2021","journal-title":"Applied Soft Computing"},{"key":"key2023012406044391900_ref036","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.apm.2021.03.047","article-title":"A novel Hausdorff fractional NGMC(p,n) grey prediction model with Grey Wolf Optimizer and its applications in forecasting energy production and conversion of China","volume":"97","year":"2021","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref037","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.isatra.2020.07.017","article-title":"On unified framework for discrete-time grey models: extensions and applications","volume":"107","year":"2020","journal-title":"ISA Transactions"},{"key":"key2023012406044391900_ref038","doi-asserted-by":"publisher","DOI":"10.1016\/j.cnsns.2019.105076","article-title":"Understanding cumulative sum operator in grey prediction model with integral matching","volume":"82","year":"2020","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"issue":"7","key":"key2023012406044391900_ref039","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1016\/j.cnsns.2012.11.017","article-title":"Grey system model with the fractional order accumulation","volume":"18","year":"2013","journal-title":"Communications in Nonlinear Science and Numerical Simulation"},{"key":"key2023012406044391900_ref040","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.energy.2018.10.076","article-title":"A new hybrid model to predict the electrical load in five states of Australia","volume":"166","year":"2019","journal-title":"Energy"},{"key":"key2023012406044391900_ref041","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120714","article-title":"Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: a case of Hubei in China","volume":"229","year":"2021","journal-title":"Energy"},{"key":"key2023012406044391900_ref042","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.apm.2017.07.010","article-title":"An improved seasonal rolling grey forecasting model using a cycle truncation accumulated generating operation for traffic flow","volume":"51","year":"2017","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref043","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106538","article-title":"Parameter optimization for nonlinear grey Bernoulli model on biomass energy consumption prediction","volume":"95","year":"2020","journal-title":"Applied Soft Computing"},{"key":"key2023012406044391900_ref044","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/j.apm.2020.06.020","article-title":"A novel car-following inertia gray model and its application in forecasting short-term traffic flow","volume":"87","year":"2020","journal-title":"Applied Mathematical Modelling"},{"issue":"1","key":"key2023012406044391900_ref045","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3846\/tede.2020.13742","article-title":"Evaluation of the coordination between China's technology and economy using a grey multivariate coupling model","volume":"27","year":"2021","journal-title":"Technological and Economic Development of Economy"},{"issue":"9","key":"key2023012406044391900_ref046","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1016\/j.apm.2019.09.013","article-title":"A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions","volume":"77","year":"2020","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref047","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.knosys.2018.08.027","article-title":"Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines","volume":"163","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"key2023012406044391900_ref048","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1016\/j.gloei.2021.01.003","article-title":"Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network","volume":"3","year":"2020","journal-title":"Global Energy Interconnection"},{"issue":"2","key":"key2023012406044391900_ref049","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.apm.2019.05.044","article-title":"A new multivariable grey prediction model with structure compatibility","volume":"75","year":"2019","journal-title":"Applied Mathematical Modelling"},{"key":"key2023012406044391900_ref050","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1016\/j.apenergy.2015.07.059","article-title":"Comparison of numerical weather prediction based deterministic and probabilistic wind resource assessment methods","volume":"156","year":"2015","journal-title":"Applied Energy"},{"key":"key2023012406044391900_ref053","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.isatra.2020.12.024","article-title":"A grey seasonal least square support vector regression model for time series forecasting","volume":"114","year":"2021","journal-title":"ISA Trans"},{"key":"key2023012406044391900_ref051","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107363","article-title":"A novel grey prediction model for seasonal time series","volume":"229","year":"2021","journal-title":"Knowledge-Based Systems"}],"container-title":["Grey Systems: Theory and Application"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/GS-10-2021-0159\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/GS-10-2021-0159\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T00:46:19Z","timestamp":1753404379000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/gs\/article\/13\/1\/141-171\/207015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,5]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,7,5]]},"published-print":{"date-parts":[[2023,1,25]]}},"alternative-id":["10.1108\/GS-10-2021-0159"],"URL":"https:\/\/doi.org\/10.1108\/gs-10-2021-0159","relation":{},"ISSN":["2043-9377"],"issn-type":[{"value":"2043-9377","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7,5]]}}}