Publication
Macro modeling of electricity price towards SDG7
| dc.contributor.author | Martins, Florinda | |
| dc.contributor.author | Felgueiras, Carlos | |
| dc.contributor.author | Caetano, Nídia | |
| dc.date.accessioned | 2023-01-26T15:51:46Z | |
| dc.date.available | 2023-01-26T15:51:46Z | |
| dc.date.issued | 2022-05 | |
| dc.description.abstract | Energy challenges are crucial issues to achieve Sustainable Development and its goals. Energy availability and affordability are pillars for ending poverty, giving access to commodities as well as water, etc. Modern lives rely on appliances and gadgets based on electric energy being its price a key issue making it worth to analyze and promote simple models able to predict electric energy prices to support in decision-making processes and in management. This work studied the correlation of electricity price with variables such as the electricity mix, GDP (gross domestic product), energy productivity, electricity consumption per capita, fossil fuel reserves, and diesel price, using Spearman correlation. To the significant correlations found it was then applied the Kruskal–Wallis test and the variables that presented statistically significant differences were then considered to model electricity price based on these macro variables. Our findings revealed that the best models were a logarithmic and a linear model of energy productivity to predict electricity price, which is fundamental to achieve Sustainable Development Goals (SDG), specifically SDG7. In the validation process, these models presented an average deviation of 10.3% and 11.7%, respectively, which is reasonable considering the simplicity of the models developed. | pt_PT |
| dc.description.sponsorship | This work was financially supported by Base Funding – UIDB/04730/2020 of Center for Innovation in Engineering and Industrial Technology, CIETI – funded by national funds through the FCT/MCTES (PIDDAC), Portugal. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.doi | 10.1016/j.egyr.2022.04.055 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.22/21917 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Elsevier | pt_PT |
| dc.relation | Center for Innovation in Industrial Engineering and Technology | |
| dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2352484722008204?via%3Dihub | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | pt_PT |
| dc.subject | Electricity price | pt_PT |
| dc.subject | Energy | pt_PT |
| dc.subject | Regression models | pt_PT |
| dc.subject | Sustainable development goals | pt_PT |
| dc.title | Macro modeling of electricity price towards SDG7 | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Center for Innovation in Industrial Engineering and Technology | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04730%2F2020/PT | |
| oaire.citation.conferencePlace | The 8th International Conference on Energy and Environment Research ICEER 2021, 13–17 September | pt_PT |
| oaire.citation.endPage | 622 | pt_PT |
| oaire.citation.startPage | 614 | pt_PT |
| oaire.citation.title | Energy Reports | pt_PT |
| oaire.citation.volume | 8 | pt_PT |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| person.familyName | Martins | |
| person.familyName | Felgueiras | |
| person.familyName | Caetano | |
| person.givenName | F. | |
| person.givenName | Carlos | |
| person.givenName | Nídia | |
| person.identifier | R-000-DJC | |
| person.identifier.ciencia-id | F112-AE32-9279 | |
| person.identifier.ciencia-id | 1F1D-73E2-BFBF | |
| person.identifier.orcid | 0000-0002-8866-1799 | |
| person.identifier.orcid | 0000-0002-4202-5551 | |
| person.identifier.orcid | 0000-0002-2185-6401 | |
| person.identifier.rid | I-3934-2012 | |
| person.identifier.scopus-author-id | 55901684900 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
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