Yazar "Alkharusi, Tarik" seçeneğine göre listele

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    Alternative work arrangements: Individual, organizational and environmental outcomes
    (Cell Press, 2023) Yildizhan, Hasan; Hosouli, Sahand; Yilmaz, Sidika Ece; Gomes, Joao; Pandey, Chandan; Alkharusi, Tarik
    Flexible working models are widely used around the world. Furthermore, several countries are currently transitioning to a 4-day workweek. These working models have significant effects on organizational behavior and the environment. The study investigates the employees' attitudes and behaviors toward flexible working and 4-day workweek and the impact on the environment. The semi-structured interview method was used in the study to determine employee attitudes and behaviors; the carbon footprint calculation method was used to determine the environmental impact of a 4-day workweek. According to the study's findings, it has been discovered that there would be a positive impact on socialization, happiness, stress factor, motivation, personal time, mental health, comfort, work-life balance, time-saving, willingness, positive working environment, personal time, and physical health. Furthermore, a 4-day workweek reduced commuting emissions by 20%, resulting in a 6,07 kg tCO(2)e reduction per person. As a result, the study attempted to draw attention holistically to the positive effects of the flexible working model and 4-day workweek. The study is intended to serve as a tool for decision-makers and human resource managers.
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    Examining effects of air pollution on photovoltaic systems via interpretable random forest model
    (Pergamon-Elsevier Science Ltd, 2024) Dudas, Adam; Udristioiu, Mihaela Tinca; Alkharusi, Tarik; Yildizhan, Hasan; Sampath, Satheesh Kumar
    Renewable energy plays a vital role in power generation and solar photovoltaic systems due to resource availability throughout the year. This work aims to investigate the impact of air pollutants and meteorological parameters on the performance of the photovoltaic systems locally, taking into consideration the advantages of the photovoltaic power potential of the SW part of Romania, where Craiova is located (average solar radiation intensity >1350 kWh/m(2)/year). This study is based on a one-year dataset provided by a sensor that monitors particulate matter concentrations, volatile organic compounds, dioxide of carbon, ozone, noise, formaldehyde and three climate parameters (temperature, pressure, and relative humidity). The research methodology applies an innovative interpretable random forest model emphasising the implications of air pollution for photovoltaic systems. The proposed machine learning model was trained to predict the particulate matter level in air based on the basic environmental variable measurements. The study presents six random forest models of varying complexity, which reach the accuracy of classification for the selected problem up to 99 %, and applies the Shapley Additive Explanations technique to interpret the decision-making model. The observation regarding the highest concentration of particulate matter occurring during cold months, which typically do not align with peak solar irradiance, underscores the importance of considering various environmental factors in solar energy planning. With its practical implications, this insight offers decision-makers valuable information about the feasibility of optimising solar energy generation despite seasonal variations in air pollution levels, directly addressing their needs and concerns.
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    Öğe
    Examining effects of air pollution on photovoltaic systems via interpretable random forest model
    (Pergamon-Elsevier Science Ltd, 2024) Dudas, Adam; Udristioiu, Mihaela Tinca; Alkharusi, Tarik; Yildizhan, Hasan; Sampath, Satheesh Kumar
    Renewable energy plays a vital role in power generation and solar photovoltaic systems due to resource availability throughout the year. This work aims to investigate the impact of air pollutants and meteorological parameters on the performance of the photovoltaic systems locally, taking into consideration the advantages of the photovoltaic power potential of the SW part of Romania, where Craiova is located (average solar radiation intensity >1350 kWh/m(2)/year). This study is based on a one-year dataset provided by a sensor that monitors particulate matter concentrations, volatile organic compounds, dioxide of carbon, ozone, noise, formaldehyde and three climate parameters (temperature, pressure, and relative humidity). The research methodology applies an innovative interpretable random forest model emphasising the implications of air pollution for photovoltaic systems. The proposed machine learning model was trained to predict the particulate matter level in air based on the basic environmental variable measurements. The study presents six random forest models of varying complexity, which reach the accuracy of classification for the selected problem up to 99 %, and applies the Shapley Additive Explanations technique to interpret the decision-making model. The observation regarding the highest concentration of particulate matter occurring during cold months, which typically do not align with peak solar irradiance, underscores the importance of considering various environmental factors in solar energy planning. With its practical implications, this insight offers decision-makers valuable information about the feasibility of optimising solar energy generation despite seasonal variations in air pollution levels, directly addressing their needs and concerns.
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    Öğe
    Experimental investigation of nonuniform PV soiling
    (Pergamon-Elsevier Science Ltd, 2024) Alkharusi, Tarik; Alzahrani, Mussad M.; Pandey, Chandan; Yildizhan, Hasan; Markides, Christos N.
    Photovoltaic (PV) module soiling, i.e., the accumulation of dust on PV module surfaces, poses several challenges to PV system performance. Among these challenges, the nonuniform deposition of soiling across the module surface has received scarce attention. Soiling is directly associated with an overall performance loss, but can also potentially give rise to localised hotspots that can lead to long-term PV module failure. Therefore, addressing the issues arising from this nonuniformity is not only important for optimising energy production, but also for enhancing system reliability, and ensuring the long-term operation of relevant power generation systems. In this study, the impact of nonuniform soiling on PV performance is investigated experimentally by examining soil deposition on the upper surfaces of low-iron glass samples. Samples positioned at four different tilt angles were collected on a monthly basis over a one-year study period. Since the horizontal samples were found to represent the worst-case conditions, the most soiled sample at horizontal tilt was divided into four zones, each housing a single monocrystalline solar cell and examined further. The findings reveal that the soiled sample experiences an average transmittance deterioration of 13% relative to a clean sample, and a maximum (relative) spatial variation of 4% between the four zones. These optical losses affect the amount of sunlight received by the cells, resulting in a power deterioration of similar to 6-7% per 5% drop in transmittance. The soiled sample experienced an average temperature rise of 2 degrees C, and an average power output (and efficiency) reduction of 30% relative to the clean sample, and a maximum (relative) spatial variation of 7% between the zones. The 30% average power loss measured in this nonuniform soiling case is more than double that which would be expected theoretically for a transmittance loss of 13% but from uniform soiling, so these results highlight the importance of addressing PV soiling for optimal PV performance, and of accounting for spatial soiling nonuniformity.
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    The Drivers and Barriers of the Solar Water Heating Entrepreneurial System: A Cost-Benefit Analysis
    (Mdpi, 2023) Yilmaz, Sidika Ece; Yildizhan, Hasan; Yildirim, Cihan; Zhao, Chuang-Yao; Gomes, Joao; Alkharusi, Tarik
    Sustainable development objectives place a high priority on entrepreneurship and renewable energy. Supporting entrepreneurial activities in the renewable energy industry can provide economic growth and employment to accomplish the Sustainable Development Goals Agenda 2030. Solar water heating systems can provide clear benefits for both the environment and economic growth. There is a gap in the literature regarding the study of the factors hindering or driving the development of the solar water heating system industry. This study aims to investigate the solar water heating system industry's challenges and attempts to define the drivers to further develop the industry. Thus, solar water heating entrepreneurship parameters can be identified. Additionally, energy savings and carbon dioxide emissions were calculated for the region to raise awareness among consumers. This study used the qualitative analysis method through semi-structured interviews with 40 business owners in Adana/Turkey. The findings showed that the industry has administrative, production, political, and economic issues; there is a need for economic support and expanding education and control mechanisms. Also, the payback period is 1.63 similar to 3.27 years for a solar water heating system and this system prevents 800.75 kg of CO2 emission. The study has implications for policy-making, practice, scientific research, and the SDGs Agenda 2030.