Prediction of Solar Irradiance over the Arabian Peninsula: Satellite Data, Radiative Transfer Model, and Machine Learning Integration Approach

Sahar Alwadei,Harry D Kambezidis,Ashraf Farahat,Moataz Ahmed

doi:10.3390/app12020717

Abstract

Data from a moderate resolution imaging spectroradiometer instrument onboard the Terra satellite along with a radiative transfer model and a machine learning technique were integrated to predict direct solar irradiance on a horizontal surface over the Arabian Peninsula (AP). In preparation for building appropriate residual network (ResNet) prediction models, we conducted some exploratory data analysis (EDA) and came to some conclusions. We noted that aerosols in the atmosphere correlate with solar irradiance in the eastern region of the AP, especially near the coastlines of the Arabian Gulf and the Sea of Oman. We also found low solar irradiance during March 2016 and March 2017 in the central (~20% less) and eastern regions (~15% less) of the AP, which could be attributed to the high frequency of dust events during those months. Compared to other locations in the AP, high solar irradiance was recorded in the Rub Al Khali desert during winter and spring. The effect of major dust outbreaks over the AP during March 2009 and March 2012 was also noted. The EDA indicated a correlation between high aerosol loading and a decrease in solar irradiance. The analysis showed that the Rub Al Khali desert is one of the best locations in the AP to harvest solar radiation. The analysis also showed the ResNet prediction model achieves high test accuracy scores, indicated by a mean absolute error of ~0.02, a mean squared error of ~0.005, and an R2 of 0.99.

Highlights

Renewable energy technologies, including photovoltaic (PV) technology, could play a significant role in supplying rising global energy demand with low environmental impacts [1]
This property of ML facilitates its use in many fields such as the one considered within this research, in which it will be utilized to predict solar irradiance considering earlier observed data of solar irradiance as time series
Based on the aerosol loading in the atmosphere as an input to the algorithm of Figure 3, we use the library of the radiative transfer model (RTM) model to estimate direct normal irradiance (DNI) on a horizontal surface

Summary

Introduction

Renewable energy technologies, including photovoltaic (PV) technology, could play a significant role in supplying rising global energy demand with low environmental impacts [1]. The impact o sols on solar radiation is even more significant over climatically sensitive regions li AP, which hosts one of the largest deserts in the world (i.e., Al Rub’ al Khali). More efforts are required to predict solar irradiance following the effects of natural and anthropogenic aerosols in different regions. The prediction of solar irradiance lends itself to a form of time-series analysis. Time-series classification categorizes time series in predefined classes, while forecasting the future values of time series reflects the closest recent values The latter category, forecasting, represents the task considered here to predict solar irradiance. Tracing the different techniques used to solve prediction problems, the machine-learning (ML) techniques have provided the field with promising results These results motivated us to investigate the applicability of such techniques to predict solar irradiance over the AP. Note that the work presented in this research effort represents an application of an existing ML model, and does not aim to improve the model or enhance its performance

Theoretical Background

ResNet Model Setup

Dataset

Results and Discussion

ResNets Prediction Analysis

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