A hybrid framework for forecasting power generation of multiple renewable energy sources

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

A hybrid framework for forecasting power generation of multiple renewable energy sources

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

A hybrid framework for forecasting power generation of multiple renewable energy sources

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

RENEWABLE & SUSTAINABLE ENERGY REVIEWS

ISSN

1364-0321

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

172

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

„“-„“

Kód UT WoS článku

000891641900004

EID výsledku v databázi Scopus

2-s2.0-85141986027