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Reinforcement learning for control of flexibility providers..:

Brida V. Mbuwir ; Davy Geysen ; Fred Spiessens.
https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0196. , 2019

Link: https://doi.org/10.1049/iet-stg.2019.0196

RT Journal
T1 <Title>Reinforcement learning for control of flexibility providers in a residential microgrid</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:96db75402bb44775a283469a9479d619&Exemplar=1&LAN=DE
				
A1 Brida V. Mbuwir
A1 Davy Geysen
A1 Fred Spiessens
A1 Geert Deconinck
PB Wiley
YR 2019
K1 learning (artificial intelligence)
K1 multi-agent systems
K1 power consumption
K1 distributed power generation
K1 photovoltaic power systems
K1 power engineering computing
K1 iterative methods
K1 power generation scheduling
K1 heat pumps
K1 stochastic processes
K1 control engineering computing
K1 power generation control
K1 residential microgrid
K1 smart grid paradigm
K1 smart meters
K1 machine learning
K1 model-free reinforcement learning techniques
K1 single-agent stochastic microgrid settings
K1 rule-based controller
K1 model-based optimal controller
K1 electricity consumption patterns
K1 power system planning
K1 rl techniques
K1 policy iteration
K1 pi
K1 fitted q-iteration
K1 fqi
K1 heat pump
K1 multiagent collaborative microgrid settings
K1 photovoltaic production
K1 Electrical engineering. Electronics. Nuclear engineering
K1 TK1-9971
JF https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0196
LK http://dx.doi.org/https://doi.org/10.1049/iet-stg.2019.0196
DO https://doi.org/10.1049/iet-stg.2019.0196
SF ELIB - SuUB Bremen

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