Sustainable Solar Panel Efficiency Optimization with Chaos-Based XAI: An Autonomous Air Conditioning Cabinet-Based Approach - Akpinar, Ebru; Papatya, Fatma; Das, Mehmet; Yildirim, Suna; Alatas, Bilal; Catalkaya, Murat; Akay, Orhan E. (Mdpi, 2025)
This study introduces a climate chamber developed to evaluate the performance of photovoltaic (PV) and solar air heater (SAH) panels based on 12 months of climate data specific to the province of Antalya. In the test environment, the temperature can be controlled between -5 and +50 degrees C, relative humidity between 10% and 90%, irradiance between 0 and 1500 W/m2, and wind speed between 0 and 25 m/s. Experimental data revealed that PV panels achieved the lowest electricity production of 19.21 W in December and the highest of 73.47 W in June, while SAH panels reached an outlet temperature of 31.12 degrees C in July. As solar radiation increased, panel efficiency rose proportionally; however, an increase in relative humidity negatively impacted efficiency. The panel surface temperature increased from 16.86 degrees C in January to 39.33 degrees C in July. The original aspect of this study is the proposal and adaptation of chaos-integrated optimization-based explainable artificial intelligence (XAI) methods instead of classical regression-based models. These models have enabled the development of transparent, understandable, and interpretable rules based on environmental parameters, such as temperature, relative humidity, radiation, and airspeed, that affect panel performance. The methods used in this study make significant contributions to sustainable energy. In particular, the climate control test chamber developed to increase and optimize the efficiency of solar panels enables the investigation of the effects of environmental parameters on panel performance under realistic conditions, thereby facilitating the more effective use of renewable energy sources. Additionally, the use of chaos-integrated optimization-based explainable artificial intelligence (XAI) methods provides reliable, transparent, and understandable decision support models for the design and management of energy systems. This method promotes the adoption of renewable energy technologies, reduces dependence on fossil fuels, lowers carbon emissions, and supports long-term environmental sustainability.
Keywords: solar panel performance; sustainable energy; explainable artificial intelligence; climate control test chamber