Machine Learning Analysis of Solar Photovoltaic Panel Efficiency and Exergy in Real-World Climate Conditions

Machine Learning-Based Evaluation of Solar Photovoltaic Panel Exergy and Efficiency Under Real Climate Conditions

Abstract: This study aims to provide a thorough analysis of the performance of solar panels regarding exergy and energy efficiency within the climatic context of the Utrecht region in the Netherlands. The research examines how solar panels perform in terms of energy and exergy efficiency. Key factors investigated include solar radiation, module internal temperature, air temperature, maximum power, and solar energy efficiency. Environmental factors significantly influence panel performance; while increased temperature typically reduces efficiency, higher solar radiation correlates with enhanced energy and exergy output. These insights can help improve solar energy utilization in temperate oceanic climates like that of Utrecht. The effectiveness of the linear regression model in machine learning was confirmed through performance metrics such as R², RMSE, MAE, and MAPE, with an R² value of 0.94889 for the utilized parameters. This research serves as a valuable resource for policymakers, researchers, and industry stakeholders interested in optimizing solar energy use amidst changing climatic conditions.

1. Introduction
As technology advances, a significant portion of the increasing global energy demand continues to be met by fossil fuels. However, these resources are depleting and pose severe environmental challenges, making the shift towards clean and renewable energy sources crucial. The Paris Climate Agreement, ratified by many countries, including Turkey in 2015, encourages the transition to more sustainable energy sources. Energy, historically fundamental to human society, must be provided in a cost-effective and environmentally sustainable manner for the well-being of future generations.

With the depletion of energy resources, sustainability is emerging as a central issue. Energy encompasses various forms, including electrical, chemical, mechanical, thermal, and nuclear energy. The first law of thermodynamics focuses on energy conservation, while the second law emphasizes the quality of energy, noting that energy diminishes in both quantity and quality during transformation—a concept known as exergy. Exergy analysis synthesizes these principles, offering a comprehensive assessment of system performance by revealing exergy loss in components. This analysis is vital for evaluating the effective use of solar energy systems.

Despite the value of solar radiation maps, they do not provide a complete assessment of electrical energy output from photovoltaic power plants, as other environmental factors, such as ambient temperature and wind speed, also significantly impact performance. Exergy methods can address this limitation effectively. Thermoeconomics, which combines exergy analysis with economic theory, has been widely applied to optimize energy systems.

Previous studies have explored the energy and exergy efficiencies of various solar panel systems, indicating that exergy efficiency fluctuates with solar radiation intensity and module temperature. Understanding these relationships is crucial for maximizing solar energy utilization.

2. Materials and Methods
This research investigates exergy solar, exergy electricity, and exergy efficiency in relation to meteorological conditions in Utrecht, employing minute-by-minute data from the Utrecht University Photovoltaic Outdoor Test (UPOT) facility. Key variables assessed include solar radiation, air temperature, module temperature, maximum power, and efficiency.

The study begins with definitions of the variables and the data collection process, followed by an explanation of the linear regression model used. Data were analyzed using Python 3.9.1 and the Scikit-learn machine learning framework.

2.1. Study Area
The Utrecht region experiences mild summers and cold, windy, overcast winters, with temperatures ranging from 0 °C to 22 °C. Cloud cover varies seasonally, with July typically being the clearest month. The region sees the most rainfall in December, and wind speeds peak from October to April. The average altitude is 4 m above sea level, with a maximum elevation variation of 25 m.

2.2. Parameters Influencing PV Solar Power Plant Efficiency
The efficiency of a PV solar power plant is defined as the ratio of total power generated to total solar radiation received, influenced by both external and internal factors. Key parameters include:

• Solar Irradiation (W/m²): The intensity of sunlight received, which enhances electricity production.
• Air Temperature (°C): Higher temperatures often reduce performance.
• Module Maximum Power (kW): The peak electrical output.
• Module Temperature (°C): Elevated temperatures may diminish output.

In June 2023, a fixed, grid-connected PV solar power plant was installed at the UPOT facility, with solar modules oriented at a 37° angle to the south. Experimental measurements of electrical parameters were recorded, and data collection occurred every 5 minutes.

2.3. Multiple Linear Regression
Multiple linear regression is a statistical method used to illustrate relationships between a dependent variable and one or more independent variables. The model aims to determine the best-fit line that defines these relationships. Key performance metrics include R² (coefficient of determination), mean absolute error (MAE), and mean squared error (MSE).

2.4. Exergy Efficiency Calculation in Solar Panels
Exergy efficiency measures how effectively solar energy is converted into electrical energy, accounting for both solar energy input and the efficiency of its use. The exergy of sunlight is calculated using specific formulas that consider ambient temperature and solar radiation.

3. Results and Discussion
The results indicate that various environmental factors significantly affect solar panel efficiency. The study highlights that as ambient temperature increases, energy efficiency decreases, while higher solar radiation correlates with improved efficiency. The data underscores the importance of managing temperature and maximizing solar exposure to enhance solar energy systems’ performance.

4. Conclusions
This research provides an in-depth analysis of solar panel exergy and efficiency under real climatic conditions in Utrecht. The findings demonstrate comparable energy and exergy efficiencies and emphasize the significant impact of environmental factors on panel performance. This study serves as a vital resource for optimizing solar energy use in similar climatic regions, offering insights for both the scientific community and practitioners in the field.