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A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy
and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficie...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290234/ https://www.ncbi.nlm.nih.gov/pubmed/32546893 http://dx.doi.org/10.1016/j.energy.2020.118085 |
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author | Luo, Xilin Duan, Huiming He, Leiyuhang |
author_facet | Luo, Xilin Duan, Huiming He, Leiyuhang |
author_sort | Luo, Xilin |
collection | PubMed |
description | and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficients to optimize the whitening equation, and establishes a grey prediction model (CCRGM(1,1)) based on the Riccati equation. This model organically combines the characteristics of the grey model, and flexibly improves the modelling precision. Furthermore, the nonlinear term is optimized by the simulated annealing algorithm. To illustrate the validation of the new model, two kinds of clean energy consumption in the actual area are selected as the research objects. Compared with six other grey prediction models, CCRGM(1,1) model has the highest accuracy in simulation and prediction. Finally, this model is used to predict the nuclear and hydroelectricity energy consumption in North America from 2019 to 2028. The results predict that nuclear energy consumption will keep rising in the next decade, while hydroelectricity energy consumption will rise to a peak and subsequently fall back, which offers important information for the governments of North America to formulate energy measures. |
format | Online Article Text |
id | pubmed-7290234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72902342020-06-12 A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy Luo, Xilin Duan, Huiming He, Leiyuhang Energy (Oxf) Article and accurate prediction of clean energy can supply an important reference for governments to formulate social and economic development policies. This paper begins with the logistic equation which is the whitening equation of the Verhulst model, introduces the Riccati equation with constant coefficients to optimize the whitening equation, and establishes a grey prediction model (CCRGM(1,1)) based on the Riccati equation. This model organically combines the characteristics of the grey model, and flexibly improves the modelling precision. Furthermore, the nonlinear term is optimized by the simulated annealing algorithm. To illustrate the validation of the new model, two kinds of clean energy consumption in the actual area are selected as the research objects. Compared with six other grey prediction models, CCRGM(1,1) model has the highest accuracy in simulation and prediction. Finally, this model is used to predict the nuclear and hydroelectricity energy consumption in North America from 2019 to 2028. The results predict that nuclear energy consumption will keep rising in the next decade, while hydroelectricity energy consumption will rise to a peak and subsequently fall back, which offers important information for the governments of North America to formulate energy measures. Elsevier Ltd. 2020-08-15 2020-06-12 /pmc/articles/PMC7290234/ /pubmed/32546893 http://dx.doi.org/10.1016/j.energy.2020.118085 Text en © 2020 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Luo, Xilin Duan, Huiming He, Leiyuhang A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title | A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title_full | A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title_fullStr | A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title_full_unstemmed | A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title_short | A Novel Riccati Equation Grey Model And Its Application In Forecasting Clean Energy |
title_sort | novel riccati equation grey model and its application in forecasting clean energy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7290234/ https://www.ncbi.nlm.nih.gov/pubmed/32546893 http://dx.doi.org/10.1016/j.energy.2020.118085 |
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