A hybrid framework for forecasting power generation of multiple renewable energy sources
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU146541" target="_blank" >RIV/00216305:26210/23:PU146541 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1364032122009273" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1364032122009273</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.rser.2022.113046" target="_blank" >10.1016/j.rser.2022.113046</a>
Alternative languages
Result language
angličtina
Original language name
A hybrid framework for forecasting power generation of multiple renewable energy sources
Original language description
The accurate power generation forecast of multiple renewable energy sources is significant for the power scheduling of renewable energy systems. However, previous studies focused more on the prediction of a single energy source, ignoring the relationship among different energy sources, and failing to predict accurate power generation for all energy sources simultaneously. This paper proposes a hybrid framework for the power generation forecast of multiple renewable energy sources to overcome deficiencies. A Convolutional Neural Network (CNN) is developed to extract the local correlations among multiple energy sources, the Attention-based Long Short-Term Memory (A-LSTM) network is developed to capture the nonlinear time-series characteristics of weather conditions and individual energy, and the Auto-Regression model is applied to extract the linear time-series characteristics of each energy source. The accuracy and practicality of the proposed method are verified by taking a renewable energy system as an example. The results show that the hybrid framework is more accurate than other advanced models, such as artificial neural networks and decision trees. Mean absolute errors of the proposed method are reduced by 13.4%, 22.9%, and 27.1% for solar PV, solar thermal, and wind power compared with A-LSTM. The sensitivity analysis has been conducted to test the effectiveness of each component of the proposed hybrid framework to prove the significance of energy correlation patterns with higher accuracy and stability compared with the other two patterns.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
ISSN
1364-0321
e-ISSN
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Volume of the periodical
neuveden
Issue of the periodical within the volume
172
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
„“-„“
UT code for WoS article
000891641900004
EID of the result in the Scopus database
2-s2.0-85141986027