Abstract
Reliable and accurate photovoltaic (PV) output power projection is critical for power grid security, stability, and economic operation. However, because of the indirectness, unpredictability, and solar energy volatility, predicting precise and reliable photovoltaic output power is a complicated subject. The photovoltaic output power variable is evaluated in this study using a powerful machine learning approach called the support vector machine model based on gray-wolf optimization. A vast dataset of previously published papers was compiled for this purpose. Several studies were carried out to assess the suggested model. The statistical evaluation revealed that this model predicts absolute values with reasonable accuracy, including R 2 and RMSE values of 0.908 and 74.6584, respectively. The practical input data were also subjected to sensitivity analysis. The results of this analysis showed that the air temperature parameter has a greater effect on the target parameter than the solar irradiance intensity parameter (relevancy factor equal to 0.75 compared to 0.49, respectively). The leverage approach was also used to test the accuracy of actual data, and the findings revealed that the vast majority of data is accurate. This basic but accurate model may be quite effective in predicting target values and could be a viable substitute for laboratory data.
Highlights
Given the challenges such as climate change and the fossil energy crisis, renewable energy production has become much more vital [1,2,3]
The findings revealed that the suggested model worked well in terms of predicting photovoltaic output power
The results show that all defined inputs have a considerable impact on the photovoltaic output power values
Summary
Given the challenges such as climate change and the fossil energy crisis, renewable energy production has become much more vital [1,2,3]. The first type predicts photovoltaic output power using an AI model paired with an International Journal of Photoenergy optimization technique [20,21,22,23]. While the first type produced acceptable predictive performance, it is challenging to enhance the accuracy further. The reason for this is that the first type did not extract various characteristics of photovoltaic output power. Particle swarm optimization was used to enhance the variables of regression of support vector to increase predicting accuracy. The purpose of writing this article is to propose a model for estimating photovoltaic (PV) output power with higher accuracy compared to previous works. The GWO-SVM model was studied, and the performance of this model was estimated by examining the related statistical analyses
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