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Integration of AI, ...
Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management
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- Nammouchi, Amal (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Aupke, Phil (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Kassler, Andreas, 1968- (författare)
- Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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- Theocharis, Andreas (författare)
- Karlstads universitet,Institutionen för ingenjörsvetenskap och fysik (from 2013)
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- Raffa, Viviana (författare)
- University of Bologna, ITA
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- Di Felice, Marco (författare)
- University of Bologna, ITA
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(creator_code:org_t)
- IEEE, 2021
- 2021
- Engelska.
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Ingår i: 2021 21St Ieee International Conference On Environment And Electrical Engineering And 2021 5Th Ieee Industrial And Commercial Power Systems Europe (Eeeic/I&Cps Europe). - : IEEE. - 9781665436137
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Towards zero CO2 emissions society, large shares of renewable energy sources and storage systems are integrated into microgrids as part of the electrical grids for energy exchange aiming to effectively reduce the stress from the transmission grid. However, energy management within and across microgrids is complicated due to many uncertainties such as imprecise knowledge on energy production and demand, which makes energy optimization challenging. In this paper, we present an open architecture that uses machine learning algorithms at the edge to predict energy consumption and production for energy management in smart microgrids. Such predictions are aggregated across different prosumers at a centralized marketplace in the Cloud using Kafka Streams and OpenSource IoT platforms. Using pluggable optimization algorithms, different microgrids can implement different strategies for real-time optimal energy schedules. The proposed architecture is evaluated in terms of scalability and accuracy of predictions. Our heuristics can effectively optimize medium-sized microgrids.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- artificial intelligence
- internet of things
- edge/cloud computing
- machine learning
- microgrids
- smart grid
- renewable energy
- energy management systems
- Computer Science
- Datavetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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