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Renewable energy sources generation forecasting in aggregated energy system level
dc.creator | Panapakidis I., Gousis G., Koltsaklis N., Dagoumas A. | en |
dc.date.accessioned | 2023-01-31T09:41:36Z | |
dc.date.available | 2023-01-31T09:41:36Z | |
dc.date.issued | 2021 | |
dc.identifier | 10.1109/EEEIC/ICPSEurope51590.2021.9584682 | |
dc.identifier.isbn | 9781665436120 | |
dc.identifier.uri | http://hdl.handle.net/11615/77473 | |
dc.description.abstract | Renewable Energy Sources (RES) generation forecasting is an approach to handle the stochasticity of RES. This concept is very crucial to transform RES plants into dispatchable and integrated them for contemporary energy markets. The majority of the literature focuses on individual plants. The data are collected in a site and used as inputs in the forecasting model. The present paper is centered on aggregated energy system level. The total capacities of Photovoltaics (PV) and Wind Turbine (WT) power of a country are regarded. A scenarios-based approach is followed in order to investigate how the number and types of inputs influence the forecasting performance. While most studies of the literature focus on individual systems, the paper contributes on the RES forecasting literature through the consideration of the total PV and WT generation capacity on aggregated power system level. © 2021 IEEE | en |
dc.language.iso | en | en |
dc.source | 21st IEEE International Conference on Environment and Electrical Engineering and 2021 5th IEEE Industrial and Commercial Power System Europe, EEEIC / I and CPS Europe 2021 - Proceedings | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126439436&doi=10.1109%2fEEEIC%2fICPSEurope51590.2021.9584682&partnerID=40&md5=dc6c200de0d45e11b739ae12e7e1e9d4 | |
dc.subject | Deep neural networks | en |
dc.subject | Natural resources | en |
dc.subject | Renewable energy resources | en |
dc.subject | Solar cells | en |
dc.subject | Turbogenerators | en |
dc.subject | Wind turbines | en |
dc.subject | Deep learning | en |
dc.subject | Neural-networks | en |
dc.subject | Photovoltaic generation forecasting | en |
dc.subject | Photovoltaics generations | en |
dc.subject | Power | en |
dc.subject | Power system | en |
dc.subject | Renewable energy source | en |
dc.subject | System levels | en |
dc.subject | Wind turbine generation forecasting | en |
dc.subject | Wind-turbine generation | en |
dc.subject | Forecasting | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Renewable energy sources generation forecasting in aggregated energy system level | en |
dc.type | conferenceItem | en |
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