Abstract
Global energy consumption and costs have increased exponentially in recent years, accelerating the search for viable, profitable, and sustainable alternatives. Renewable energy is currently one of the most suitable alternatives. The high variability of meteorological conditions (irradiance, ambient temperature, and wind speed) requires the development of complex and accurate management models for the optimal performance of photovoltaic systems. The simplification of photovoltaic models can be useful in the sizing of photovoltaic systems, but not for their management in real time. To solve this problem, we developed the I-Solar model, which considers all the elements that comprise the photovoltaic system, the meteorologic conditions, and the energy demand. We have validated it on a solar pumping system, but it can be applied to any other system. The I-Solar model was compared with a simplified model and a machine learning model calibrated in a high-power and complex photovoltaic pumping system located in Albacete, Spain. The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%. However, models based on machine learning are specific to the system evaluated, while the I-Solar model can be applied to any system.
Highlights
One of the biggest threats facing the world population is climate change, which is primarily caused by the emission of greenhouse gases (GHG) produced by the use of fossil fuels in industrial processes
The results show that the I-Solar model estimates the generated power with a relative error of 7.5%, while the relative error of machine learning models was 5.8%
Analysis of the Regression Models Based on Artificial Intelligence, AI-Solar Model To determine the type of machine learning that represents the best operation of the photovoltaic system, the main statistical results were obtained for each of the machine learning types described in the methodology (Table 5)
Summary
One of the biggest threats facing the world population is climate change, which is primarily caused by the emission of greenhouse gases (GHG) produced by the use of fossil fuels in industrial processes. The benefits of renewable energy (REn) are clearly visible, being a prerequisite to reach socioeconomically sustainable systems, and to address the challenges of climate change and the depletion of fossil fuels. These problems require active policies aiming at a rapid transition [1,2]. The improvement of photovoltaic modules and the search for highly efficient new materials [9] or module types [10] has led to an expansion, with high levels of investment in photovoltaic solar energy as an alternative to conventional energy sources. Agrivoltaics can open up an additional revenue stream, there is a high concern by farmers about land affection on the long term
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