PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254589" target="_blank" >RIV/61989100:27240/23:10254589 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10194796" target="_blank" >https://ieeexplore.ieee.org/document/10194796</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EEEIC/ICPSEurope57605.2023.10194796" target="_blank" >10.1109/EEEIC/ICPSEurope57605.2023.10194796</a>
Alternative languages
Result language
angličtina
Original language name
PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future
Original language description
The rapid increase in solar power generation requires accurate forecasting of photovoltaic (PV) energy production for effective grid integration and energy market participation. This paper presents state-of-the-art forecasting techniques and models, including statistical and time series models, machine learning models, deep learning models, probabilistic forecasting models, and data-driven feature engineering and selection. In addition, applications of PV energy production forecasting in grid integration and energy market operations such as load balancing, generation planning, transmission and distribution planning, energy storage optimisation, bidding strategies, and renewable energy certificate trading are presented. Furthermore, challenges persist in PV power generation forecasting are presented, including data quality and availability, model uncertainty, forecast horizon and time resolution, and integration of multiple data sources and models. Finally, this article focusses on providing insight into the current PV energy production forecasting landscape and guiding future research and development efforts to address these challenges, enhance forecasting capabilities, and facilitate the global transition to a more sustainable and low-carbon energy future.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC I I&CPS Europe) : conference Proceedings : 6- 9 June, 2023, Madrid, Spain
ISBN
979-8-3503-4743-2
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
"nečíslovano"
Publisher name
IEEE
Place of publication
Piscataway
Event location
Madrid
Event date
Jun 6, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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