One of the significant challenges affecting photovoltaic (PV) performance is the influence of environmental conditions. Cloud cover, rain, and high temperatures can reduce PV efficiency. Among these, dust impacts PV performance and leads to secondary effects, such as increased PV temperature.

This PhD thesis delves deeply into the impact of dust accumulation on photovoltaic (PV) modules, focusing on their thermal and optical performances. Firstly, the study identifies gaps in existing research and highlights the lack of consideration of how dust accumulation can impact PV temperature and the mathematical and specific correlation between dust accumulated weight and type on PV temperature. Additionally, the literature review reveals that a generalized module calculation is required to determine dust optical properties, which can help researchers and scholars identify the impact of dust on PV optical properties.

This study introduces a comprehensive approach that integrates a wide range of dust characteristics into the thermal and optical analysis of PV modules. This research unfolds through several phases, each delving into distinct yet interconnected aspects of how dust affects PV efficiency.

Regarding thermal effects, the investigation exposes the limitations of existing methodologies inadequately accounting for dust parameters. This revelation propels the research forward, employing advanced experimental setups and innovative mathematical models. These models, distinguished by their inclusion of absorption, emittance, and meteorological data, surpass traditional approaches in forecasting PV temperatures across varied dust conditions. The meticulous empirical validation against real-world scenarios underscores these novel formulations' heightened accuracy and practicality. Tested under real weather conditions, these models demonstrate a notable improvement in predicting PV module temperatures, achieving the lowest mean absolute error (MAE) of 1.4 compared to traditional methods, marking a significant advancement in the field.

In the subsequent phase, the research underscored the optical considerations that consider essential parameters to determine the PV temperature. The study has employed intelligent methodologies such as Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) to develop a novel model. Those models incorporate dust weight and the principal elements in the chemical composition of dust particles. This segment of the study, drawing from over 300 experimental observations and leveraging more than 600 global datasets, has yielded notable predictive precision which can predict the dusty PV surface the most important dust optical properties, which are emissivity, absorption, and transmittance where those parameters can play a crucial role to predict the PV performance and temperature. Additionally, the predictive novel has been compared with other modules that have been developed using different techniques to produce the transmittance of dusty PV using The Mean Squared Errors (MSEs). This developed module achieved the lowest MSE, where 1.8 is achieved for the ANN models and 8.44 for the MLR models, setting new benchmarks for predictive accuracy within dust's influence on PV modules.

The culmination of experimental insights and enhanced predictive accuracy constitutes the foundation of this thesis, providing a comprehensive perspective on the impact of dust on PV modules. This study addresses several critical research gaps, including the lack of specific models that integrate both the thermal and optical effects of dust accumulation, the absence of clear correlations between dust properties and PV temperature, and the need for generalized methods to quantify dust's optical properties (such as transmittance, absorption, and emissivity). These contributions, supported by empirical validation and advanced predictive modelling, significantly advance understanding and mitigating dust's effects on PV systems. The research's original contributions are validated through comparative analysis with existing literature, showcasing its superior predictive capabilities. This thesis significantly contributes to the field of PV technology by addressing crucial knowledge gaps and introducing improved methodological frameworks. It underscores the significance of a thorough comprehension of dust's profound influence on PV modules, advocating for a multidisciplinary approach to bolster the position of solar energy in the global energy market, particularly in regions grappling with dust-related challenges.