A comparison study based on artificial neural network for assessing PV/T solar energy production. (March 2019)

• Record Type: Journal Article
• Authors: Yousif, Jabar H.; Kazem, Hussein A.; Alattar, Nebras N.; Elhassan, Imadeldin I.
• Is Part Of: Case studies in thermal engineering. Volume 13(2019)
• Publication Date: 2019-03
• Subjects: Solar energy -- Hybrid PV/T -- Energy prediction -- ANN -- Data comparison; Heat engineering -- Case studies -- Periodicals; 621.40205
• DOI: 10.1016/j.csite.2019.100407
• Languages: English
• ISSNs: 2214-157X

Abstract

Abstract: This paper aims to employ and perform a comparison study of PV/T energy data prediction systems using different ANNs techniques. Several studies focus on photovoltaic thermal (PV/T) collectors started during the 1970s till now, which aims to increase the photovoltaic efficiency and produce a hybrid system for electricity and heat production. Locations that have good meteorological stations for recording solar radiations have been studied to predict solar energy based on using artificial neural networks (ANNs). Published studies in data sets for the years 2008–2017 were collected from individual countries and evaluated using suitable evaluation factors like MSE, MAPE, R 2, RSME, MBE, and MPE. Furthermore, the best models used to predict the data of global solar radiation for locations with different latitudes and climates are discussed and analysed. This study is a guide for the reader and useful for engineers, and researchers interested in ANNs applied for solar PV/T systems data generation. Highlights: 10 years intensive review of PV/T Solar Energy Prediction. Artificial Neural Network used for Solar energy prediction. Best predicting models for different locations are discussed and analysed.