Zobrazeno 1 - 7
of 7
pro vyhledávání: '"EL Mahjoub Benghoulam"'
Publikováno v:
Heliyon, Vol 9, Iss 11, Pp e21299- (2023)
This paper investigates the effectiveness of an indirect solar dryer (ISD) specifically designed for the geographical and climatic conditions of Meknes (Morocco). The constructed ISD system incorporates a solar air collector (SAC) inclined at 34° to
Externí odkaz:
https://doaj.org/article/bba346843c9146a69a624cd17c71543d
Publikováno v:
International Journal of Renewable Energy Development, Vol 11, Iss 1, Pp 309-323 (2022)
Prediction of daily global solar radiation with simple and highly accurate models would be beneficial for solar energy conversion systems. In this paper, we proposed a hybrid machine learning methodology integrating two feature selection methods and
Externí odkaz:
https://doaj.org/article/1f063ccafaa7419eb3e3e5f26fc0c0b5
Publikováno v:
Energies, Vol 14, Iss 21, p 7367 (2021)
Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation tech
Externí odkaz:
https://doaj.org/article/b747539a9fcd4b4291320360fb351d14
Publikováno v:
International Journal of Renewable Energy Development, Vol 11, Iss 1, Pp 309-323 (2022)
Prediction of daily global solar radiation with simple and highly accurate models would be beneficial for solar energy conversion systems. In this paper, we proposed a hybrid machine learning methodology integrating two feature selection methods and
Publikováno v:
SSRN Electronic Journal.
Publikováno v:
SSRN Electronic Journal.
Prediction of daily global solar radiation (H) with simple and high accurate models would be beneficial for solar energy conversion systems. In this paper, we proposed a hybrid machine learning methodology integrating two feature selection methods an
Publikováno v:
Energies
Volume 14
Issue 21
Energies, Vol 14, Iss 7367, p 7367 (2021)
Volume 14
Issue 21
Energies, Vol 14, Iss 7367, p 7367 (2021)
Machine learning (ML) models are commonly used in solar modeling due to their high predictive accuracy. However, the predictions of these models are difficult to explain and trust. This paper aims to demonstrate the utility of two interpretation tech