Modelo para evaluar el impacto que tienen las variables climatológicas y de calidad del aire, en la eficiencia de los paneles solares

Abstract

In a global context that drives an imperative energy transition, where solar power emerges as a primary alternative due to its ease of installation and distributed generation capabilities, a critical need arises to understand and mitigate the factors affecting its efficiency. This research precisely addresses the impact of climatological and air quality variables on the performance of solar panels, proposing a machine learning model capable of forecasting their efficiency. The central problem lies in the vulnerability of photovoltaic generation to exogenous factors like irradiance, temperature, pressure, humidity, and, crucially, dirt and environmental pollution. Omitting these studies can lead to the installation of seemingly viable systems that, over time, suffer a rapid degradation of efficiency, generating economic losses and disconnection in areas seeking energy autonomy. To counter this, a model is proposed that uses climatological and air pollution data from recognized APIs to study efficiency behavior in a pre-selected location in Colombia. The problem's formulation focused on how to evaluate the impact of these variables, and its systematization explored the functional and structural description of the evaluation and visualization model, as well as the validation of its predictive capacity. To address this challenge, climatological, air quality, and irradiance variables were collected, stored, and processed. Various machine learning algorithms were evaluated to predict instantaneous efficiency in seven geographical locations in Colombia with varied climatological characteristics, selecting the best-performing ones based on the number of correct predictions the algorithm is capable of making (R2). One of the most relevant conclusions is that the inclusion of air pollution variables in the prediction models significantly improved predictive performance, by 3% for the standard evaluation metric used. The feature importance analysis of the selected Random Forest model revealed that irradiance (GHI, GDI, DNI), climatological variables (temperature, humidity, wind direction and speed), and air quality variables (NO2, PM2.5, PM10, CO, and O3) were the most influential. Additionally, a negative association was found between instantaneous efficiency and higher air pollution conditions, with NO2, CO, SO2, PM10 and PM2.5 showing the greatest influence, while O3 exhibited a positive association, regardless of the overall level of pollution. These findings underscore the practical importance of incorporating air quality variables in the planning and operation of photovoltaic systems, since a more accurate prediction of instantaneous efficiency allows for a better estimation of performance and optimizes operation and maintenance strategies, contributing to a more effective use of solar potential and reinforcing the viability of clean energy.