This paper assesses the features/characteristics of a photovoltaic system’s data, investigates the relationship between the soiling and solar panel performance, and leverages real-world data obtained from a solar site in Shams Solar Facility located at the German University of Technology in Oman. Through an experimental approach, different parameters were scrutinized to unravel the dynamics at play. Due to the lack of studies on how to assess the features of a PV System’s data, and in order to model the PV System’s data, extensive analyses were conducted based on a big dataset containing 36,851 observations of each parameter (environmental factors) of the study. In addition, diverse environmental factors, operational conditions, and the collected data were analyzed by various mathematical/statistical measures, and inferential statistical measures were applied to obtain accurate and significant results that explain the level of each parameter (environmental factors), and are developed to examine the features/characteristics and performance of PV Systems and reveal the influence of soiling accumulation on the energy output. The research findings do not only deepen the understanding of the features of PV Systems data and the impact of soiling on solar panels, but also underscore the significance of considering geographical and climatic variations. This research contributes significantly to advancing knowledge within the realm of solar energy systems and provides actionable insights for optimizing the performance and reliability of PV installations in real-world settings. The discussion, conclusions, limitations, and future directions have been discussed.

Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct current, power, and voltage—of solar panels using advanced robust regression methods: Ridge Regression, Least Absolute Deviation, and M-Estimation. Our results demonstrate that irradiance is the dominant driver of performance, with Ridge Regression coefficients reaching up to 1193 for power. The M-estimation model achieved high predictive accuracy, with R² Scores of 0.989 for current (Mean Squared Error = 0.0399) and 0.991 for power (Mean Squared Error ≈ 10,445), indicating strong model reliability. voltage prediction was more challenging but still robust (R² = 0.836, Mean Squared Error = 49.63). Negative impacts from ambient temperature and humidity were consistently observed across models, while wind speed exhibited a beneficial effect by enhancing cooling and thus improving current and power outputs. Soiling was also identified as a critical factor, significantly reducing power and voltage generation. These findings provide quantifiable evidence of how environmental variables shape solar photovoltaic performance and underscore the importance of environmental monitoring and maintenance strategies to optimize energy yield in operational solar power systems.

This paper focuses on the modeling of the performance of photovoltaic systems based on advanced techniques. This research leverages real-world data from the Shams Solar Facility at the German University of Technology in Oman to explore the application of Linear, Lasso, Ridge, and Elastic Net Regressions to predict and optimize the performance of photovoltaic systems. A comprehensive dataset of 36,851 observations of environmental and operational conditions forms the basis of the analysis. The research identifies the strengths and limitations of these modeling techniques for an accurate forecast of energy output under various scenarios. The comparative analysis highlights the precision and reliability of each regression method and offers actionable insights into their practical implementation. The findings highlight the importance of more sophisticated modeling approaches in increasing the knowledge of photovoltaic system dynamics and optimizing their performance. This research facilitates the advancement in solar energy systems and provides critical recommendations for the improvement in efficiency and reliability of photovoltaic installations under different geographic and climatic settings.

The global shift toward sustainable transport electric vehicles (EVs) is at the core of decarbonization efforts. While advanced economies have achieved their rapid adoption through strong policies and incentives, emerging markets face structural and behavioral barriers. This study investigates the paradox in Morocco, whereby a significant automotive capacity contrasts with a minimal domestic BEV market share of 0.6%, despite 143% growth from a small base, using a four-dimensional framework encompassing financial, infrastructural and energy, policy and institutional, and behavioral–social factors. The research integrates a literature review, a survey (n = 522), and secondary data on charging infrastructure and EV sales. Findings reveal a strong value–action gap: 69% of respondents acknowledged EVs’ environmental benefits yet only 1.1% owned one and 42% had considered buying. The high upfront costs of EVs influenced over 70% of participants, and a significant association was confirmed between charging availability and purchase intent (χ² = 34.80, p < 0.05). Urban-centric charging, fragmented governance, and skepticism persist as barriers. The study concludes that industrial strength alone cannot ensure adoption without targeted incentives, equitable infrastructure, and cultural shifts in ownership perception, offering key insights for policymakers in emerging economies pursuing sustainable mobility.

Purpose: The purpose of this study is to build an integrated framework that provides a methodical approach to managing the knowledge produced by Building Information Modelling (BIM) procedures. Theoretical framework: BIM has revolutionized the approach to building design and construction projects; nevertheless, managing the vast amounts of heterogeneous data produced by the various BIM processes is a very difficult task. The need of knowledge management (KM) for successful BIM implementation is becoming widely acknowledged as it will allow organisational stakeholders to make strategic decisions, reduce errors, and improve results. Methodology: Both primary and secondary data were collected in this research. This data was utilized to develop an initial version of the KM-based BIM framework, which underwent a thorough two-phase expert evaluation process via interviews with industry experts to enhance the framework's applicability and relevance. Findings: The significant factors influencing KM in the context of BIM were identified. Moreover, through the expert evaluation process, it was determined that the proposed KM-based BIM framework provided valuable assistance in addressing these factors and in managing BIM organisational knowledge. Research implications: The research has implications for the field of building design and construction projects. By promoting the adoption of a KM-based BIM framework, it seeks to address the challenges associated with managing heterogeneous data and information silos. The framework has the potential to improve the decision making process in the context of BIM. Originality/value: This is the first research providing a methodical approach to managing BIM-related knowledge through KM, and that provides an expert-validated framework for improved decision making.

Building Information Modelling (BIM) has gained widespread popularity over the past decade, mainly in the Architecture, Engineering, and Construction (AEC) industry. The adoption of BIM in developed countries is already regulated as opposed to developing and underdeveloped countries. This study aims at filling the knowledge gap as to the adoption and implementation of BIM practices in small and medium enterprises (SMEs), with a focus on the Moroccan AEC industry. The main goals of this study are 1. to determine the BIM awareness level and adoption rate, 2. to assess the familiarity level, 3. to establish the benefits, challenges, and prospects of BIM. The results of this study have shown that BIM awareness is high, but its adoption and implementation are not, though promising as it is exponentially gaining the attention of AEC practitioners.