PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

PV Advancements & Challenges: Forecasting Techniques, Real Applications, and Grid Integration for a Sustainable Energy Future

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC I I&amp;CPS Europe) : conference Proceedings : 6- 9 June, 2023, Madrid, Spain

ISBN

979-8-3503-4743-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

"nečíslovano"

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Madrid

Datum konání akce

6. 6. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—