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Artificial Intelligence Techniques for the Smart Grid

Artificial Intelligence Techniques for the Smart Grid ADVANCES IN BUILDING ENERGY RESEARCH, 2018 VOL. 12, NO. 1, 1–2 https://doi.org/10.1080/17512549.2017.1410362 EDITORIAL In recent years, there is a rush in Artificial Intelligence (AI) research to produce practical solutions for the Smart Grid, the anticipated new generation of energy (primarily electricity) networks that will be able to make efficient use of renewable energy sources, support real time and effi- cient demand response, as well as the large-scale deployment of electric vehicles (EVs). AI tech- niques and methodologies can be instrumental in addressing sustainability problems, for example, to increase the efficiency and effectiveness of the way we manage and allocate our natural and societal resources. The drive to use AI for the Smart Grid has in turn led to novel questions and challenges for AI research, and to the realization that only the cross-ferti- lization of ideas and mixing of various techniques originating in different (sub-)fields can lead to the Holy Grail of an electricity Grid that takes full advantage of AI technologies to deliver power that is at the same time ‘green’, stable, affordable and accessible to all. This Special Issue brings together research questions and approaches originating in different (sub-)fields, such as multiagent systems, machine learning, optimization and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Building Energy Research Taylor & Francis

Artificial Intelligence Techniques for the Smart Grid

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Artificial Intelligence Techniques for the Smart Grid

Abstract

ADVANCES IN BUILDING ENERGY RESEARCH, 2018 VOL. 12, NO. 1, 1–2 https://doi.org/10.1080/17512549.2017.1410362 EDITORIAL In recent years, there is a rush in Artificial Intelligence (AI) research to produce practical solutions for the Smart Grid, the anticipated new generation of energy (primarily electricity) networks that will be able to make efficient use of renewable energy sources, support real time and effi- cient demand response, as well as the large-scale deployment of electric...
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1756-2201
eISSN
1751-2549
DOI
10.1080/17512549.2017.1410362
Publisher site
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Abstract

ADVANCES IN BUILDING ENERGY RESEARCH, 2018 VOL. 12, NO. 1, 1–2 https://doi.org/10.1080/17512549.2017.1410362 EDITORIAL In recent years, there is a rush in Artificial Intelligence (AI) research to produce practical solutions for the Smart Grid, the anticipated new generation of energy (primarily electricity) networks that will be able to make efficient use of renewable energy sources, support real time and effi- cient demand response, as well as the large-scale deployment of electric vehicles (EVs). AI tech- niques and methodologies can be instrumental in addressing sustainability problems, for example, to increase the efficiency and effectiveness of the way we manage and allocate our natural and societal resources. The drive to use AI for the Smart Grid has in turn led to novel questions and challenges for AI research, and to the realization that only the cross-ferti- lization of ideas and mixing of various techniques originating in different (sub-)fields can lead to the Holy Grail of an electricity Grid that takes full advantage of AI technologies to deliver power that is at the same time ‘green’, stable, affordable and accessible to all. This Special Issue brings together research questions and approaches originating in different (sub-)fields, such as multiagent systems, machine learning, optimization and

Journal

Advances in Building Energy Research – Taylor & Francis

Published: Jan 2, 2018

References

  • Predicting agent performance in large-scale electricity demand shifting
  • Applying extended Kalman filters to adaptive thermal modelling in homes
  • Customized time-of-use pricing for small-scale consumers using multi-objective particle swarm optimization
  • An innovative soft computing system for smart energy grids cybersecurity
  • Introduction of plug-in hybrid electric vehicles in an isolated island system
  • Modelling of household electricity consumption with the aid of computational intelligence methods
  • Energy management in solar microgrid via reinforcement learning using fuzzy reward