| dc.contributor.author | - | |
| dc.date.accessioned | 2025-10-02T04:22:07Z | |
| dc.date.available | 2025-10-02T04:22:07Z | |
| dc.date.issued | 2020-09-23 08:06:08+00:00 | |
| dc.identifier.issn | - | |
| dc.identifier.uri | 2666-5468 | |
| dc.identifier.uri | http://digilib.fisipol.ugm.ac.id/repo/handle/15717717/34636 | |
| dc.description.abstract | - | |
| dc.format | - | |
| dc.language.iso | EN | |
| dc.publisher | Elsevier | |
| dc.relation.uri | ['https://www.journals.elsevier.com/energy-and-ai', 'https://www.elsevier.com/authors/open-access/choice#waivers', 'https://www.elsevier.com/journals/energy-and-ai/2666-5468/guide-for-authors'] | |
| dc.rights | ['CC BY', 'CC BY-NC-ND', 'CC BY-NC'] | |
| dc.subject | ['energy', 'ai', 'renewable energy', 'machine learning', 'energy system', 'energy materials', 'Electrical engineering. Electronics. Nuclear engineering', 'TK1-9971', 'Computer software', 'QA76.75-76.765'] | |
| dc.title | Energy and AI | |
| dc.type | Article | |
| dc.identifier.oai | oai:doaj.org/journal:a5c539d91d5d42f8bd0784b68961d544 | |
| dc.journal.info | - | |
