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A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application
In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420576/ https://www.ncbi.nlm.nih.gov/pubmed/36045994 http://dx.doi.org/10.1155/2022/1722848 |
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author | Lv, Xian-Long Tang, Shikai Su, Jia |
author_facet | Lv, Xian-Long Tang, Shikai Su, Jia |
author_sort | Lv, Xian-Long |
collection | PubMed |
description | In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the “14th Five-Year” energy plan. |
format | Online Article Text |
id | pubmed-9420576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94205762022-08-30 A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application Lv, Xian-Long Tang, Shikai Su, Jia Comput Intell Neurosci Research Article In order to actively respond to the “14th Five-Year Plan,” the PGA algorithm is used to develop a new energy planning strategy in this paper. The project can make full use of my country's abundant renewable energy resources, encourage energy conservation and reduction of emissions, improve the energy structure's low-carbon level, support the development of smart green energy, and achieve ecological civilization construction. This solution can show users how much greenhouse gas emissions can be reduced through some environmental changes, as well as the basic issues of meeting the future energy needs. It can display the benefits, costs, and emissions data under different scenarios in the future and use the scenario demonstration method to show energy planning to make energy data more vivid. It allows people, technicians, and decision makers to understand what will happen to China's carbon emissions over time in the next 15 years. This paper innovatively combines a particle swarm optimization algorithm with a genetic algorithm and designs a PGA algorithm for path optimization. In terms of carbon emission reduction, comparative trials demonstrate that the PGA algorithm's path optimization is 58.06 percent greater than the genetic algorithm; In terms of cost, the PGA algorithm's path optimization is 15.72% less expensive than the genetic algorithm's. This article provides a reference path for selecting the best results for future energy planning schemes and provides a new strategy for the “14th Five-Year” energy plan. Hindawi 2022-08-21 /pmc/articles/PMC9420576/ /pubmed/36045994 http://dx.doi.org/10.1155/2022/1722848 Text en Copyright © 2022 Xian-Long Lv et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Lv, Xian-Long Tang, Shikai Su, Jia A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title | A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title_full | A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title_fullStr | A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title_full_unstemmed | A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title_short | A Novel Energy Planning Scheme Based on PGA Algorithm and Its Application |
title_sort | novel energy planning scheme based on pga algorithm and its application |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9420576/ https://www.ncbi.nlm.nih.gov/pubmed/36045994 http://dx.doi.org/10.1155/2022/1722848 |
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