Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

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 i...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Tereza Paterova, Michal Prauzek
Formato:	article
Lenguaje:	EN
Publicado:	Kaunas University of Technology 2021
Materias:	energy management environmental monitoring neural networks prediction algorithms Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/aabe651d241346d480817b833a026446
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:aabe651d241346d480817b833a026446
record_format	dspace
spelling	oai:doaj.org-article:aabe651d241346d480817b833a0264462021-11-04T14:14:15ZEstimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning1392-12152029-573110.5755/j02.eie.28874https://doaj.org/article/aabe651d241346d480817b833a0264462021-10-01T00:00:00Zhttps://eejournal.ktu.lt/index.php/elt/article/view/28874https://doaj.org/toc/1392-1215https://doaj.org/toc/2029-5731This 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.Tereza PaterovaMichal PrauzekKaunas University of Technologyarticleenergy managementenvironmental monitoringneural networksprediction algorithmsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElektronika ir Elektrotechnika, Vol 27, Iss 5, Pp 18-25 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	energy management environmental monitoring neural networks prediction algorithms Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	energy management environmental monitoring neural networks prediction algorithms Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tereza Paterova Michal Prauzek Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
description	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.
format	article
author	Tereza Paterova Michal Prauzek
author_facet	Tereza Paterova Michal Prauzek
author_sort	Tereza Paterova
title	Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
title_short	Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
title_full	Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
title_fullStr	Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
title_full_unstemmed	Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning
title_sort	estimating harvestable solar energy from atmospheric pressure using deep learning
publisher	Kaunas University of Technology
publishDate	2021
url	https://doaj.org/article/aabe651d241346d480817b833a026446
work_keys_str_mv	AT terezapaterova estimatingharvestablesolarenergyfromatmosphericpressureusingdeeplearning AT michalprauzek estimatingharvestablesolarenergyfromatmosphericpressureusingdeeplearning
_version_	1718444859977629696

Estimating Harvestable Solar Energy from Atmospheric Pressure Using Deep Learning

Ejemplares similares