A critical review of theoretical models for estimating global so-lar radiation between 2012-2016 in Nigeria

Abstract

A routinely research of solar radiation is of vital requirement for surveys in agronomy, hydrology, ecology and sizing of the photovoltaic or thermal solar systems, solar architecture, molten salt power plant and supplying energy to natural processes like photosynthesis and estimates of their performances. However, measurement of global solar radiation is not available in most locations across in Nigeria. During the past 5 years in order to estimate global solar radiation on the horizontal surface on both daily and monthly mean daily basis, numerous empirical models have been developed for several locations in Nigeria. As a result, various input parameters have been utilized and different functional forms used. In this study aims at comparing, classifying and reviewing the empirical and soft computing models applied for estimating global solar radiation. The empirical models so far utilized were classified into eight main categories and presented based on the input parameters employed. The models were further reclassified into several main sub-classes and finally represented according to their developing year. On the whole, 145 empirical models and 42 functional forms, 8 artificial neural network models, 1 adaptive neural fuzzy inference system approach, and 1 Autoregressive Moving Average methods were recorded in literature for estimating global solar radiation in Nigeria. This review would provide solar-energy researchers in terms of identifying the input parameters and functional forms widely employed up until now as well as recognizing their importance for estimating global solar radiation using soft computing empirical models in several locations in Nigeria.

Assessing the Impact of Soiling, Tilt Angle, and Solar Radiation on the Performance of Solar PV Systems

“…These results demonstrate the importance of taking location-specific characteristics [37], such as local climate [38] and cloud cover [39], into account when deciding on the optimal tilt angle and orientation for a PV system in order to maximize its efficiency and ensure that it continues to yield the maximum possible energy output over time. The results of this research indicate that the optimal tilt angle and orientation of a PV system should be determined based on the combination of multiple factors such as global horizontal irradiance [40], latitude [41], maximum incident solar radiation [42], and cloud cover [43].…”

Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks

“…Stations with long historical measurements of GHI are limited because of the cost of installation and maintenance, and issues related to the pyranometers [5]. Therefore, several studies have tried to estimate GHI empirically from the early 20th century until now from other climate variables, namely, Sunshine Duration (SD), Air Temperature (AT), cloud cover, and other variables, using the top-of-atmosphere irradiance on the horizontal surface (TOA) [6][7][8][9][10][11] and with linear regression models [12][13][14]. Recently, machine learning approaches have also been broadly used [15,16], which mostly include Artificial Neural Networks (ANNs), which will be discussed in a later section, Support Vector Machines, Random Forest [5,17,18] and some other machine learning models [19,20].…”