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An AI-Based Support System for Microgrids Energy Management

Alejandro Puerta, Santiago Horacio Hoyos, Isis Bonet Orcid Logo, Fabio Caraffini Orcid Logo

Applications of Evolutionary Computation, Pages: 507 - 518

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.1007/978-3-031-30229-9_33

Abstract

Decarbonisationoftheeconomyisthekeytoreducinggreenhouse- effect gas emissions and climate change. One of the ways decarbonisation of economy is electrification of economic sectors. In this case, the imple- mentation of micro-grids in different economic sectors such as households, industry, and comme...

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Published in: Applications of Evolutionary Computation
ISBN: 9783031302282 9783031302299
ISSN: 0302-9743 1611-3349
Published: Cham Springer Nature Switzerland 2023
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa63101
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first_indexed 2023-04-09T14:29:39Z
last_indexed 2023-04-10T03:21:47Z
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spelling 2023-04-09T15:41:47.2545044 v2 63101 2023-04-09 An AI-Based Support System for Microgrids Energy Management d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2023-04-09 SCS Decarbonisationoftheeconomyisthekeytoreducinggreenhouse- effect gas emissions and climate change. One of the ways decarbonisation of economy is electrification of economic sectors. In this case, the imple- mentation of micro-grids in different economic sectors such as households, industry, and commerce is a great mechanism that allows the integration of renewable energies into the electrical power system and to contribute with accelerated energy transition for decarbonisation. However, micro- grids include self-generation through renewable energy and distributed generation, as well as energy efficiency in the consumer. Micro-grids have energetic, economic, and environmental benefits for the user and the power system, but for the security of the energy supply it is necessary to balance the offer and demand of electricity at all times, which in this case must be estimated for the market of the next day. The problem here is how to estimate generation and consume for the next day when the determinant of offer and demand are variable. This paper proposes algorithms of forecasting based on machine learning with high accuracy in a decision support system of management of energy for a micro-grid. Book chapter Applications of Evolutionary Computation 507 518 Springer Nature Switzerland Cham 9783031302282 9783031302299 0302-9743 1611-3349 1 1 2023 2023-01-01 10.1007/978-3-031-30229-9_33 http://dx.doi.org/10.1007/978-3-031-30229-9_33 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required 2023-04-09T15:41:47.2545044 2023-04-09T10:37:33.9338808 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Alejandro Puerta 1 Santiago Horacio Hoyos 2 Isis Bonet 0000-0002-3031-2334 3 Fabio Caraffini 0000-0001-9199-7368 4
title An AI-Based Support System for Microgrids Energy Management
spellingShingle An AI-Based Support System for Microgrids Energy Management
Fabio Caraffini
title_short An AI-Based Support System for Microgrids Energy Management
title_full An AI-Based Support System for Microgrids Energy Management
title_fullStr An AI-Based Support System for Microgrids Energy Management
title_full_unstemmed An AI-Based Support System for Microgrids Energy Management
title_sort An AI-Based Support System for Microgrids Energy Management
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Alejandro Puerta
Santiago Horacio Hoyos
Isis Bonet
Fabio Caraffini
format Book chapter
container_title Applications of Evolutionary Computation
container_start_page 507
publishDate 2023
institution Swansea University
isbn 9783031302282
9783031302299
issn 0302-9743
1611-3349
doi_str_mv 10.1007/978-3-031-30229-9_33
publisher Springer Nature Switzerland
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.1007/978-3-031-30229-9_33
document_store_str 0
active_str 0
description Decarbonisationoftheeconomyisthekeytoreducinggreenhouse- effect gas emissions and climate change. One of the ways decarbonisation of economy is electrification of economic sectors. In this case, the imple- mentation of micro-grids in different economic sectors such as households, industry, and commerce is a great mechanism that allows the integration of renewable energies into the electrical power system and to contribute with accelerated energy transition for decarbonisation. However, micro- grids include self-generation through renewable energy and distributed generation, as well as energy efficiency in the consumer. Micro-grids have energetic, economic, and environmental benefits for the user and the power system, but for the security of the energy supply it is necessary to balance the offer and demand of electricity at all times, which in this case must be estimated for the market of the next day. The problem here is how to estimate generation and consume for the next day when the determinant of offer and demand are variable. This paper proposes algorithms of forecasting based on machine learning with high accuracy in a decision support system of management of energy for a micro-grid.
published_date 2023-01-01T04:23:36Z
_version_ 1763663955002130432
score 11.012678

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