The Artificial Intelligence Use in Solar Panels Proposal Aim and Focus of the Study

The use of solar PV panels as sources of renewable energy has been gaining traction in the recent decades. As a result, there has been increased competitiveness in the installation of PV panels. Meanwhile, the growing usage of artificial intelligence continues to enhance improved performance predictions through computational power and higher data availability. The need to predict solar PV energy output remains very essential to many players in the renewable energy industry. Artificial intelligence can be leveraged to achieve this end, particularly with regard to weather input parameters such as humidity and dust rate. This study proposal aims to present an approach that can be used to predict the output of a solar PV panel based on weather input parameters through the use of artificial intelligence. Different models of artificial intelligence with the features of humidity and dust rate will be created. The datasets will be collected in different areas within the United Arab Emirates (UAE).

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Research Context

The continued growth of the economy has resulted in an increase in the demand for energy across the globe. However, there are fears that the finite energy reserves are rapidly getting depleted. Besides, the Dudley [1] observes that overreliance on fossil fuel as the primary energy source is taking a catastrophic toll on the global environment, thanks to global warming and climate change. Solar energy plants are thus emerging as the most appropriate renewable energy alternatives that will help reverse these trends. In recent years, there has been an increased uptake and installation of solar PV panels around the globe. In 2019 alone, 117 gigawatts were generated from solar PV power as demostarted by Alomari [2]. Artificial intelligence is now a common phenomenon in predicting and classifying solar PV panels output against the input variables. This is because of its ability to process nonlinear and complex problems reliably.

Fuzzy logic, K-nearest algorithm, artificial neural network (ANN), decision tree-based technique, and the support vector machine are the most common AI techniques in improving the performance of photovoltaic forecasting models. Gligor et al. [3] define artificial intelligence as a technology that has the potential to make quicker, better, and more practical forecasts as compared to traditional methods. In the opinion of Son et al. [4], when predicting the solar PV panels’ output, it is important to consider the prevailing environmental conditions, such as weather, humidity, and air pollution.

Different prediction models have been advanced using weather features to estimate the solar panels’ power output. For instance, according to Nageem and Jayabarat (5), a multi-input support vector regression (SVR) model can be used to forecast the output of a solar panel connected to a grid. In arriving at this conclusion, the authors considered such weather features as temperature, the speed of wind, humidity, and pressure. From their experimental analysis, Son et al. [4] observe that the SVR model produced more effective and accurate results as compared to the analytical model. The authors also used artificial neural networks (ANN) to predict solar panels’ power output on such weather features as irradiance and temperature. A five-year dataset demonstrated that such machine learning models as K-Nearest Neighbors, Random Regression, Gradient Boosting Regressor, and Linear Regression, have the potential to produce stronger performances.

Solar panels’ power output can also be predicted by the use of pollution features, particularly the atmospheric dust rate. While studying the impacts of particulate matter on South Korea’s solar output, the authors used the concentrations of PM2.5 and PM10 for the 2015 to 2017 dataset. The authors clarified that the PMs normally decrease the output of solar power by more than 10% [6]. From their results, they recommended that the PMs have negative effects on solar panels, and this should be taken into consideration in policymaking that targets South Korea’s solar panels. In addition, the authors calculated the production of solar energy due to particulate and dust air pollution by merging global modeling and field measurements to evaluate the effect of PM and dust on the generation of solar electricity. The results show that the production of solar panels was decreased by between 17 to 25% as a result of the PM on the solar panels’ surface, as reported by Bergin et al. [6]. From these previous studies, it is apparent that weather features adversely affect the production of power using solar PV