Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10248205" target="_blank" >RIV/61989100:27240/21:10248205 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.eejournal.ktu.lt/index.php/elt/article/view/28874" target="_blank" >https://www.eejournal.ktu.lt/index.php/elt/article/view/28874</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5755/j02.eie.28874" target="_blank" >10.5755/j02.eie.28874</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

Popis výsledku v původním jazyce

This article focuses on applying a deep learning approach to predict daily total solar energy for the next day by a neural network. Predicting future solar irradiance is an important topic in the renewable energy generation field to improve the performance and stability of the system. The forecast is used as a support parameter to control the operation duty-cycle, data collection or communication activities at energy-independent energy harvesting embedded devices. The prediction is based on previous hourly-measured atmospheric pressure values. For prediction, a back-propagation algorithm in combination with deep learning methods is used for multilayer network training. The ability of the proposed system to estimate the daily solar energy is compared to the support vector regression model and to the evolutionary-fuzzy prediction scheme presented in previous research studies. It is concluded that the presented neural network approach gave satisfying predictions in early spring, autumn, and winter. In a particular setting, the proposed solution provides better results than a model using the support vector regression method (e.g., the MAPE value of the proposed algorithm is 0.032 less than the MAPE value of support vector regression method). The time and computational complexity for neural network training is considerable, and therefore it was assumed to train the network on an external computer or a cloud, where only the network parameters have been obtained and transferred to the embedded devices.

Název v anglickém jazyce

Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

Popis výsledku anglicky

This article focuses on applying a deep learning approach to predict daily total solar energy for the next day by a neural network. Predicting future solar irradiance is an important topic in the renewable energy generation field to improve the performance and stability of the system. The forecast is used as a support parameter to control the operation duty-cycle, data collection or communication activities at energy-independent energy harvesting embedded devices. The prediction is based on previous hourly-measured atmospheric pressure values. For prediction, a back-propagation algorithm in combination with deep learning methods is used for multilayer network training. The ability of the proposed system to estimate the daily solar energy is compared to the support vector regression model and to the evolutionary-fuzzy prediction scheme presented in previous research studies. It is concluded that the presented neural network approach gave satisfying predictions in early spring, autumn, and winter. In a particular setting, the proposed solution provides better results than a model using the support vector regression method (e.g., the MAPE value of the proposed algorithm is 0.032 less than the MAPE value of support vector regression method). The time and computational complexity for neural network training is considerable, and therefore it was assumed to train the network on an external computer or a cloud, where only the network parameters have been obtained and transferred to the embedded devices.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Elektronika ir elektrotechnika

ISSN

1392-1215

e-ISSN

—

Svazek periodika

27

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

8

Strana od-do

18-25

Kód UT WoS článku

000713024300003

EID výsledku v databázi Scopus

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