Matches in SemOpenAlex for { <https://semopenalex.org/work/W2914410080> ?p ?o ?g. }
- W2914410080 endingPage "695" @default.
- W2914410080 startingPage "695" @default.
- W2914410080 abstract "India’s coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India’s coal consumption during 1995–2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018–2030." @default.
- W2914410080 created "2019-02-21" @default.
- W2914410080 creator A5025855204 @default.
- W2914410080 creator A5065278090 @default.
- W2914410080 creator A5084308830 @default.
- W2914410080 date "2019-01-29" @default.
- W2914410080 modified "2023-09-30" @default.
- W2914410080 title "Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models" @default.
- W2914410080 cites W1960563100 @default.
- W2914410080 cites W1982530894 @default.
- W2914410080 cites W1994108945 @default.
- W2914410080 cites W1996955033 @default.
- W2914410080 cites W1997200769 @default.
- W2914410080 cites W2002538901 @default.
- W2914410080 cites W2005521048 @default.
- W2914410080 cites W2019744914 @default.
- W2914410080 cites W2024638371 @default.
- W2914410080 cites W2040142217 @default.
- W2914410080 cites W2043009912 @default.
- W2914410080 cites W2078235759 @default.
- W2914410080 cites W2079151946 @default.
- W2914410080 cites W2081728853 @default.
- W2914410080 cites W2234824490 @default.
- W2914410080 cites W2235642366 @default.
- W2914410080 cites W2275543810 @default.
- W2914410080 cites W2296966686 @default.
- W2914410080 cites W2337811467 @default.
- W2914410080 cites W2344069239 @default.
- W2914410080 cites W2412249060 @default.
- W2914410080 cites W2516697969 @default.
- W2914410080 cites W2541631431 @default.
- W2914410080 cites W2553624782 @default.
- W2914410080 cites W2582676827 @default.
- W2914410080 cites W2610640432 @default.
- W2914410080 cites W2775155156 @default.
- W2914410080 cites W2875846317 @default.
- W2914410080 cites W2883388482 @default.
- W2914410080 cites W2885631883 @default.
- W2914410080 cites W2890954814 @default.
- W2914410080 cites W2898604056 @default.
- W2914410080 cites W2905579199 @default.
- W2914410080 cites W4229891116 @default.
- W2914410080 doi "https://doi.org/10.3390/su11030695" @default.
- W2914410080 hasPublicationYear "2019" @default.
- W2914410080 type Work @default.
- W2914410080 sameAs 2914410080 @default.
- W2914410080 citedByCount "13" @default.
- W2914410080 countsByYear W29144100802019 @default.
- W2914410080 countsByYear W29144100802020 @default.
- W2914410080 countsByYear W29144100802021 @default.
- W2914410080 countsByYear W29144100802022 @default.
- W2914410080 countsByYear W29144100802023 @default.
- W2914410080 crossrefType "journal-article" @default.
- W2914410080 hasAuthorship W2914410080A5025855204 @default.
- W2914410080 hasAuthorship W2914410080A5065278090 @default.
- W2914410080 hasAuthorship W2914410080A5084308830 @default.
- W2914410080 hasBestOaLocation W29144100801 @default.
- W2914410080 hasConcept C105795698 @default.
- W2914410080 hasConcept C119599485 @default.
- W2914410080 hasConcept C127413603 @default.
- W2914410080 hasConcept C144024400 @default.
- W2914410080 hasConcept C149782125 @default.
- W2914410080 hasConcept C151406439 @default.
- W2914410080 hasConcept C159877910 @default.
- W2914410080 hasConcept C162324750 @default.
- W2914410080 hasConcept C175706884 @default.
- W2914410080 hasConcept C18903297 @default.
- W2914410080 hasConcept C24338571 @default.
- W2914410080 hasConcept C2780165032 @default.
- W2914410080 hasConcept C30772137 @default.
- W2914410080 hasConcept C33923547 @default.
- W2914410080 hasConcept C36289849 @default.
- W2914410080 hasConcept C39432304 @default.
- W2914410080 hasConcept C47737302 @default.
- W2914410080 hasConcept C518851703 @default.
- W2914410080 hasConcept C548081761 @default.
- W2914410080 hasConcept C82257358 @default.
- W2914410080 hasConcept C86803240 @default.
- W2914410080 hasConceptScore W2914410080C105795698 @default.
- W2914410080 hasConceptScore W2914410080C119599485 @default.
- W2914410080 hasConceptScore W2914410080C127413603 @default.
- W2914410080 hasConceptScore W2914410080C144024400 @default.
- W2914410080 hasConceptScore W2914410080C149782125 @default.
- W2914410080 hasConceptScore W2914410080C151406439 @default.
- W2914410080 hasConceptScore W2914410080C159877910 @default.
- W2914410080 hasConceptScore W2914410080C162324750 @default.
- W2914410080 hasConceptScore W2914410080C175706884 @default.
- W2914410080 hasConceptScore W2914410080C18903297 @default.
- W2914410080 hasConceptScore W2914410080C24338571 @default.
- W2914410080 hasConceptScore W2914410080C2780165032 @default.
- W2914410080 hasConceptScore W2914410080C30772137 @default.
- W2914410080 hasConceptScore W2914410080C33923547 @default.
- W2914410080 hasConceptScore W2914410080C36289849 @default.
- W2914410080 hasConceptScore W2914410080C39432304 @default.
- W2914410080 hasConceptScore W2914410080C47737302 @default.
- W2914410080 hasConceptScore W2914410080C518851703 @default.
- W2914410080 hasConceptScore W2914410080C548081761 @default.
- W2914410080 hasConceptScore W2914410080C82257358 @default.