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

Bikhtiyar Ameen,Claire Jarvis,James Wheeler,Heiko Balzter

doi:10.3390/en12010148

Abstract

More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs.

Highlights

Several studies have estimated Global Horizontal Irradiance (GHI) from various methods, but a higher temporal resolution of GHI is likely necessary for several applications such as photovoltaic panel and concentrated solar power projects
M1–M10 on variable inputs for validation and test data were averaged for four automatic stations and five and tower stations and are training, validation and test data were averaged for four automatic stations five tower stations presented in Tablesin and respectively
This paper aimed to use a new input of Satellite-Derived Datasets (SDDs) together with clear sky (Cs), TOA and observed climate variables Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS) as new input combinations in ten ANN models to estimate GHI at the hourly time scale with a Levenberg–Marquardt training algorithm

Summary

Introduction

Several studies have estimated Global Horizontal Irradiance (GHI) from various methods, but a higher temporal resolution of GHI is likely necessary for several applications such as photovoltaic panel and concentrated solar power projects. The demand for GHI has increased for solar energy projects. This is owing to problems related to non-renewable energies, a lack of other energy sources, increasing the use of energy and potential availability of solar energy [1,2,3,4]. Several studies have tried to estimate GHI empirically from the early 20th century until now from other climate variables, namely, Sunshine. Machine learning approaches have been broadly used [15,16], which mostly include Artificial

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Conclusion