Prediction of monthly average global solar radiation based on statistical distribution of clearness index

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

“…However, the soft computing methods broadly employed in numerous scientific fields for modeling, prediction and optimization such as automatic relevance determination (ARD) methodology, the niching genetic algorithms, and the adaptive neuro-fuzzy inference system (ANFIS) technique can be adopted for estimating global solar radiation in Nigeria. For example, Ayedele and Ogunjunjuyigbe [38] applied auto regressive moving average method and recorded more accurate performance compared to empirical models Angstrom-Pescott [16][17]; Garcia [54]; Hargreaves and Samani [53] for Ibadan. Sanusi et al [69] used ANN models to estimate global solar radiation in Sokoto, Nigeria and observed that their model performed better statistically when compared to existing empirical models in literature Angstrom [16]; Hargreaves and Samani [53].…”

“…Besharat et al [5] classified various global solar radiation models into four categories (sunshine-based, cloud-based, temperature-based models and other meteorological parameterbased models). Peers and researchers have to revealed that sunshine-based models are often employed probably because of its global availability at most weather stations, temperature-based models can be a conveniently used if calibrated for a particular locality, cloud cover and soil temperature-based models can be used as an alternative but are sensible to human biasing, monthly-based models can be applied comfortable because of the constant movement of the earth on its axis thereby culminating into constant variations from one month to another, extraterrestrial solar radiationbased models can be adopted with ease since it has major influence in determining the amount of irradiation that falls in a given location, and hybrid parameter-based models are reported to estimate the global solar radiation on a horizontal surface with a high precision, but most of their input parameters are not readily available at most locations of interest [5], [22], [38], [44], [46], [55], [56], [57].…”

“…Okonkwo and Nwokoye [31] derived the following MB model for Minna as: (19) Ogolo [32] suggested the following MB model for Nigeria as: (20) Nwokoye and Okonkwo [33] reported the following MB model for Bida as: (21) Kaltiya et al [34] obtained the following MB model for FebruaryJuly In Makurdi as: (22) Sheriff et al [35] registered the following MB model for Maiduguri as: (23) Ike [36] derived the following MB model for Akure as: (24) Gana et al [37] calibrated the following MB model for Sokoto (25) Ayodele and Ogunjuyigbe [38] developed the following DB model for Ibadan:…”

“…This model is applied to predict global solar radiation when the meteorological condition will be considered. Ayedele and Ogunjunjuyigbe [38] employed a probability function to represent the distribution of clearness index in a year. The coefficients were determined by applying 7 years daily global solar radiation data.…”

Estimation of Hourly, Daily and Monthly Global Solar Radiation on Inclined Surfaces: Models Re-Visited

“…The clearness index (M t ) is a measure of the atmospheric effects in an isolated place [18]. It is a random parameter that varies according to time of the year, season, climatic conditions, and geographical situation of a place [19].…”