Implementation of artificial intelligence for prediction performance of solar thermal system

Autor:	Mohd Danish Irfan Mohd Sufian, Nur Ashida Salim, Hasmaini Mohamad, Zuhaila Mat Yasin
Rok vydání:	2022
Předmět:	Artificial neural network Artificial intelligence Energy Engineering and Power Technology Electrical and Electronic Engineering Solar thermal systems Levenberg-Marquardt
Zdroj:	International Journal of Power Electronics and Drive Systems (IJPEDS). 13:1751
ISSN:	2722-256X 2088-8694
DOI:	10.11591/ijpeds.v13.i3.pp1751-1760
Popis:	A related input parameter is used in this case study to forecast solar thermal systems (STS) capabilities and to compare which artificial neural network (ANN) algorithms and other artificial intelligence (AI) methods have the most reliable predictor for STS performance. In order to gauge the performance of the STS, this research aims to implement AI for predicting STS performance by comparing the ANN technique with other methods. Three different training algorithms which are Levenberg-Marquardt (LM), scaled conjugate gradient (SCG) and Bayesian regularization (BR) are considered in this research. This research will identify acceptable parameters and the best AI technique to use in predicting the STS performance. Previous research on STS demonstrates that the efficiency of STS has been estimated using different input parameters. The results show that the prediction of the LM training algorithm is the best for STS performance.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a19b3a1ef5e899be9453db7b7ee9f593 https://doi.org/10.11591/ijpeds.v13.i3.pp1751-1760 Zobrazit plný text záznamu