文章基本信息

标题：A Comparative Evaluation of Artificial Neural Network and Sunshine Based models in prediction of Daily Global Solar Radiation of Lalibela, Ethiopia
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作者：Tegenu Argaw Woldegiyorgis ; Ashenafi Admasu ; Natei Ermias Benti 等
期刊名称：Cogent Engineering
电子版ISSN：2331-1916
出版年度：2022
卷号：9
期号：1
页码：1-13
DOI：10.1080/23311916.2021.1996871
语种：English
出版社：Taylor and Francis Ltd
摘要：Due to a lack of solar radiation measurement instruments in various regions across the world, particularly in Ethiopia, many empirical models have been developed to estimate global solar radiation. Reliable global solar radiation information is essential for the design and development of solar energy systems.Thus, the aim of this paper is to investigate the feasibility of using Artificial Neural Network (ANN) to predict mean daily global solar radiation (GSR) and to compare the performance of ANN and empirical models based on sunshine in estimating mean daily global solar irradiation on a horizontal surface.For the ANN model, network inputs were daily data for a number of days, average daily data (sunshine hours, maximum temperature, minimum temperature, wind speed, relative humidity, and pressure), and while daily averaged global solar radiation was the network output.After training, the model had R values of 0.891 for training, 0.932 for validation, 0.920 for testing, and 0.903 for all, with validation performance of 0.0797.The results of the ANN model were compared to the empirical equations using statistical error evaluation (MBEANN = 0.0005, RMSEANN = 0.3310, and R2ANN = 0.7998).The agreement between the values of NASA and estimated values reveals that the ANN exhibits good performance in studying daily GSR. The result also shows that the mean daily GSR is varying between 3.675KWh/m2 (August-1) and 6.935 KWh/m2 (April-19) in the study site.
关键词：Daily global solar radiation ;Meteorological parameters ;Artificial Neural Network ;Empirical model