Book chapter 540 views
An AI-Based Support System for Microgrids Energy Management
Alejandro Puerta,
Santiago Horacio Hoyos,
Isis Bonet 
,
Fabio Caraffini
,
Fabio Caraffini
Applications of Evolutionary Computation, Pages: 507 - 518
Swansea University Author:
Fabio Caraffini
Full text not available from this repository: check for access using links below.
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...
| Published in: | Applications of Evolutionary Computation |
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| ISBN: | 9783031302282 9783031302299 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer Nature Switzerland
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63101 |
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| 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 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. |
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| College: |
Faculty of Science and Engineering |
| Start Page: |
507 |
| End Page: |
518 |