Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region

Abstract

A 14-year-long data set containing daily values of meteorological variables was used to train three artificial neural networks (ANNs) for daily, weekly averaged and monthly averaged global solar radiation prediction for Fortaleza, in the Brazilian Northeast region. Local climate is semiarid coastal. Day of the year, maximum temperature, minimum temperature, irradiance, precipitation, cloudiness, extraterrestrial radiation, relative humidity, evaporation and wind speed were adopted as predictors. The ANNs were developed by an in-house code and trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Besides the lack of explicit predictors able to model El Niño and La Niña phenomena, which have strong influence on local weather, the accuracy of the predictions was considered excellent according to its values of normalized root-mean-square error (nRMSE) and good relative to mean absolute percentage error (MAPE) values. Both error metrics presented the smallest values for the monthly case study.

Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

“…Global solar irradiation measurements can be obtained using specific devices [3] which are limited to a small number of meteorological stations, probably due to their high cost and other inconveniences such as a need for regular calibration and maintenance [3,4]. Besides this, these data may not be accessible because the meteorological observatories that include measurement series of solar irradiation are still rarely distributed and these data present, sometimes, a problematic spatial interpolation in areas of intricate orography [5].…”

Modelling Using Machine Learning Models and Prediction of Monthly Global Irradiation

“…As far as possible, please keep the introduction comprehensible to scientists outside your particular field of research. References should be numbered in order of appearance and indicated by a numeral or numerals in square brackets-e.g., [1] or [2,3], or [4][5][6]. See the end of the document for further details on references.…”

“…To obtain global solar irradiation data remote measurements can be used using specific devices [3] which can be limited to a small number of meteorological stations [4], probably, due to their high cost and other inconveniences such as calibration and maintenance, [3,5]. Besides this, these data may not be accessible because the meteorological observatories that include measurement series of solar irradiation are still rarely distributed and these data present, sometimes, a problematic spatial interpolation in areas of intricate orography [6].…”

“…According to different authors, the shortage, the difficulties and the uncertainties of these measurements can be corrected with estimations calculated from other more abundant variables (climatological properties) such as cloudiness, among others [3,6].…”

“… Artificial neural networks have been used to predict the solar irradiation at different time windows (hourly, daily and monthly) from different meteorological variables (temperature, atmospheric pressure, among others) or even including geographical coordinates such as latitude, longitude and altitude [1]. This kind of models can be used to determine daily, weekly and monthly global solar radiation in Fortaleza (Brazilian Northeast region) using 14-year-long data set to train three different ANNs models [3]. ANNs can also be used to determine different parameters such as the global horizontal irradiation (from meteorological data), the global tilted irradiation (from the horizontal global irradiation and others) and to forecast the hourly direct normal and the global horizontal irradiation from one to six hours horizon [7].…”