FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4295777023> ?p ?o ?g. }

• W4295777023 endingPage "125439" @default.
• W4295777023 startingPage "125439" @default.
• W4295777023 abstract "In this paper, GM(1,Nr) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm; (2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables; and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,Nr) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions." @default.
• W4295777023 created "2022-09-15" @default.
• W4295777023 creator A5014780086 @default.
• W4295777023 creator A5029428058 @default.
• W4295777023 creator A5041493924 @default.
• W4295777023 date "2023-01-01" @default.
• W4295777023 modified "2023-10-02" @default.
• W4295777023 title "Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China" @default.
• W4295777023 cites W1023077103 @default.
• W4295777023 cites W1992885444 @default.
• W4295777023 cites W2032777344 @default.
• W4295777023 cites W2051502925 @default.
• W4295777023 cites W2075165019 @default.
• W4295777023 cites W2144335970 @default.
• W4295777023 cites W2168951499 @default.
• W4295777023 cites W2315656307 @default.
• W4295777023 cites W2342680502 @default.
• W4295777023 cites W2473026454 @default.
• W4295777023 cites W2473105253 @default.
• W4295777023 cites W2543508620 @default.
• W4295777023 cites W2566102230 @default.
• W4295777023 cites W2731886114 @default.
• W4295777023 cites W2742652546 @default.
• W4295777023 cites W2750292148 @default.
• W4295777023 cites W2762777252 @default.
• W4295777023 cites W2794794675 @default.
• W4295777023 cites W2797106044 @default.
• W4295777023 cites W2804202111 @default.
• W4295777023 cites W2887216976 @default.
• W4295777023 cites W2915600035 @default.
• W4295777023 cites W2949305709 @default.
• W4295777023 cites W2953345642 @default.
• W4295777023 cites W2980100207 @default.
• W4295777023 cites W2999837703 @default.
• W4295777023 cites W3005230747 @default.
• W4295777023 cites W3005837074 @default.
• W4295777023 cites W3007472825 @default.
• W4295777023 cites W3013476570 @default.
• W4295777023 cites W3016754312 @default.
• W4295777023 cites W3024619383 @default.
• W4295777023 cites W3092411501 @default.
• W4295777023 cites W3095694836 @default.
• W4295777023 cites W3117298323 @default.
• W4295777023 cites W3124534284 @default.
• W4295777023 cites W3126904039 @default.
• W4295777023 cites W3131664590 @default.
• W4295777023 cites W3137179618 @default.
• W4295777023 cites W3157457070 @default.
• W4295777023 cites W3158398531 @default.
• W4295777023 cites W3175291933 @default.
• W4295777023 cites W3178074155 @default.
• W4295777023 cites W3191813552 @default.
• W4295777023 cites W3198618412 @default.
• W4295777023 cites W3200747163 @default.
• W4295777023 cites W4213428707 @default.
• W4295777023 cites W4220736032 @default.
• W4295777023 cites W4226302151 @default.
• W4295777023 doi "https://doi.org/10.1016/j.energy.2022.125439" @default.
• W4295777023 hasPublicationYear "2023" @default.
• W4295777023 type Work @default.
• W4295777023 citedByCount "7" @default.
• W4295777023 countsByYear W42957770232023 @default.
• W4295777023 crossrefType "journal-article" @default.
• W4295777023 hasAuthorship W4295777023A5014780086 @default.
• W4295777023 hasAuthorship W4295777023A5029428058 @default.
• W4295777023 hasAuthorship W4295777023A5041493924 @default.
• W4295777023 hasConcept C105795698 @default.
• W4295777023 hasConcept C119599485 @default.
• W4295777023 hasConcept C119857082 @default.
• W4295777023 hasConcept C127413603 @default.
• W4295777023 hasConcept C134560507 @default.
• W4295777023 hasConcept C144024400 @default.
• W4295777023 hasConcept C149782125 @default.
• W4295777023 hasConcept C154945302 @default.
• W4295777023 hasConcept C162324750 @default.
• W4295777023 hasConcept C166957645 @default.
• W4295777023 hasConcept C189430467 @default.
• W4295777023 hasConcept C191935318 @default.
• W4295777023 hasConcept C205649164 @default.
• W4295777023 hasConcept C206658404 @default.
• W4295777023 hasConcept C2778112365 @default.
• W4295777023 hasConcept C30772137 @default.
• W4295777023 hasConcept C33923547 @default.
• W4295777023 hasConcept C36289849 @default.
• W4295777023 hasConcept C41008148 @default.
• W4295777023 hasConcept C42475967 @default.
• W4295777023 hasConcept C54355233 @default.
• W4295777023 hasConcept C85617194 @default.
• W4295777023 hasConcept C86803240 @default.
• W4295777023 hasConceptScore W4295777023C105795698 @default.
• W4295777023 hasConceptScore W4295777023C119599485 @default.
• W4295777023 hasConceptScore W4295777023C119857082 @default.
• W4295777023 hasConceptScore W4295777023C127413603 @default.
• W4295777023 hasConceptScore W4295777023C134560507 @default.
• W4295777023 hasConceptScore W4295777023C144024400 @default.
• W4295777023 hasConceptScore W4295777023C149782125 @default.
• W4295777023 hasConceptScore W4295777023C154945302 @default.
• W4295777023 hasConceptScore W4295777023C162324750 @default.

• next