Calibration and Validation of Global Horizontal Irradiance Clear Sky Models against McClear Clear Sky Model in Morocco

Abstract

This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot–Bourges–Campana–Gicquel (ABCG), Berger–Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the Bird clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models’ inputs are not available.

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

“…Notably, the ARIMA method often outperformed complex structural methods, especially in short‐term, one‐step forecasting for univariate datasets. [ 33 ]…”

“…Notably, the ARIMA method often outperformed complex structural methods, especially in short-term, one-step forecasting for univariate datasets. [33] F I G U R E 8 ARIMA model with predictions for NO 2 , 1-h granularity, based on grid search and ACF, PACF lags consideration.…”

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

“…Average monthly temperatures range from 9.4 • C (December, January) to 26 • C (July, August). The wettest months are from October to April, and therefore the driest are from June to August [17]. Recently, the National Centre of Meteorology and the Moroccan Agency for Energy Efficiency (AMEE) worked together to form a replacement climatic zoning map for Morocco [45,46].…”

“…Empirical Relationships: These models are supported established empirical equations and relationships. Examples include the works of [14][15][16][17][18][19]. Artificial Intelligence (ANN)-Based Models: These models utilize ANN and machine learning (ML) techniques to estimate radiation.…”