Neural Network for Estimating Daily Global Solar Radiation Using Temperature, Humidity and Pressure as Unique Climatic Input Variables

Abstract

Solar radiation is one of the most important parameters for applications, development and research related to renewable energy. However, solar radiation measurements are not a simple task for several reasons. In the cases where data are not available, it is very common the use of computational models to estimate the missing data, which are based mainly on the search for relationships between weather variables, such as temperature, humidity, precipitation, cloudiness, sunshine hours, etc. But, many of these are subjective and difficult to measure, and thus they are not always available. In this paper, we propose a method for estimating daily global solar radiation, combining empirical models and artificial neural networks. The model uses temperature, relative humidity and atmospheric pressure as the only climatic input variables. Also, this method is compared with linear regression to verify that the data have nonlinear components. The models are adjusted and validated using data from five meteorological stations in the province of Tucumán, Argentina. Results show that neural networks have better accuracy than empirical models and linear regression, obtaining on average, an error of 2.83 [MJ/m 2 ] in the validation dataset.

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

“…The low recorded bias in most of the models is related to the good estimation of GHI by ANN models as mentioned in several studies [12,17,62,64]. We presented the overall bias among stations, which led to a decrease in the bias because of positive bias in some stations and negative bias in others in the same model, whereas the bias in all individual stations was lower than 2% except one case of 2.2% (Tables A1-A9, Figure A1).…”

“…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].…”

“…For instance, geographical and meteorological parameters at different time scales as various inputs have been used with ANN models for a variety of climate regions and countries. For example, two cities in Oman [51]; eight cities [52] and nine cities [53] in China; 195 cities in Nigeria [54]; 27 stations [55], seven cities [56] and 30 cities [57] in Turkey; five stations in Argentina [12]; six cities in the Yucatan peninsula, Mexico [18]; five cities in Italy [58]; four cities in the USA; and two cities [59], 10 cities [60] and 12 cities in Iran [61]; and Cairo city in Egypt [17]. Generally, the results of those models in the literature show good agreement with ground data.…”

Estimación De Radiación Solar Horaria Utilizando Modelos Empíricos Y Redes Neuronales Artificiales.

“…La radiación solar estimada de esta forma pasa a formar parte del vector de entrada de la red neuronal, junto a mediciones de otras variables climáticas tomadas localmente que pueden o no haber sido usadas por el modelo empírico, dando como resultado las estimaciones finales de radiación solar. En trabajos anteriores (Jimenez et al, 2016;Jimenez, Will, Rodriguez, & Lamelas, 2014;Will et al, 2013) se comprobó que la radiación solar observada diaria presenta una relación lineal con otras variables climáticas, principalmente con temperatura, humedad, heliofanía y nubosidad, entre otras. Sin embargo, al no contar con todas estas variables usualmente utilizadas en la bibliografía, y al tratarse de estimaciones horarias de radiación solar, la calidad de las estimaciones obtenidas con modelos lineales puede verse reducida.…”

“…En un trabajo previo (Jimenez, Barrionuevo, Will, & Rodriguez, 2016) se propuso una metodología para estimar radiación solar global diaria basada en el uso de un modelo empírico en combinación con redes neuronales artificiales, logrando buenos resultados con bajos niveles de error. El método utiliza parámetros geográficos y variables climáticas de fácil acceso en cualquier región.…”