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Using machine learning techniques for the sizing energy sto..:
, In:
7th Offshore Energy & Storage Symposium (OSES 2023)
,
Mifsud, M. D.
;
Sant, T.
;
Farrugia, R. N.
- p. None , 2023
Link:
https://doi.org/10.1049/icp.2023.1560
RT T1 <cTitle>7th Offshore Energy & Storage Symposium (OSES 2023)</cTitle>: T1 <Title>Using machine learning techniques for the sizing energy storage systems coupled to offshore windfarms</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10267849&Exemplar=1&LAN=DE A1 Mifsud, M. D. A1 Sant, T. A1 Farrugia, R. N. YR 2023 K1 energy storage K1 learning (artificial intelligence) K1 load forecasting K1 offshore installations K1 power engineering computing K1 power generation planning K1 power grids K1 time series K1 wind power plants K1 energy demand forecasting K1 energy storage system sizing K1 energy yield K1 machine learning techniques K1 MCP techniques K1 measure-correlate-predict methodologies K1 offshore windfarm K1 onshore electricity grid K1 optimal ESS capacity K1 time series data sets K1 wind speed SP None LK http://dx.doi.org/https://doi.org/10.1049/icp.2023.1560 DO https://doi.org/10.1049/icp.2023.1560 SF ELIB - SuUB Bremen
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