Exploring the operational potential of the forest-photovoltaic utilizing the simulated solar tree

Um, Dan-Bi

doi:10.1038/s41598-022-17102-5

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…In the Seoul metropolitan area, the main destination of internal Korean migration and housing approximately 50% of the country's population, the building industry has capitalized on the limited available areas to construct new residential districts and towns [69], catering to the migrating population [70]. Accordingly, large PV commercial plants' construction has been concentrated on three types of areas: (i) agricultural fields (agro-photovoltaics); (ii) mountainous, hillside areas previously covered by autochthonous forests [71], and (iii) available water surfaces such as large ponds in city areas or lakes [72]. While the coexistence of agriculture and PV systems of different dimensions is being investigated to find an optimal solution [73], the greenfield operations required for the construction of commercial PV plants in mountainous areas are associated with land use change, such as deforestation, increased landslide risk [74], and the need for extensive connective infrastructure construction to supply electricity to end-users.…”

Section: Building Typementioning

confidence: 99%

Carbon Life Cycle Assessment and Costing of Building Integrated Photovoltaic Systems for Deep Low-Carbon Renovation

Amoruso

Schuetze

2023

Sustainability

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Building integrated photovoltaic (BIPV) systems can achieve high yields through high percentages of building envelope surface coverage associated with material savings by substituting conventional building envelope components and avoiding land-use change to install open-land PV installations. This article discusses the life cycle assessment (LCA) and the life cycle costing (LCC) of BIPV systems in timber-hybrid building extensions and envelope renovation systems of three exemplary buildings in the Republic of Korea: apartment, mixed-use commercial/industrial, and low-rise multi-unit residential. The BIPV system’s electricity production was quantified with simulation tools. Minimum and average carbon LCAs were calculated using a global product inventory database for 50 years. Greenhouse gas (GHG) emission savings by substituting conventional energy supplies were calculated based on the associated primary energy demands. LCC calculations were based on international datasets for BIPV LCC for 25 and 50 years. As a result, the BIPV system-associated GHG emissions can be decreased by up to 30% with a payback time of 12 (apartment) to 41 (mixed-use building) years for buildings with full PV coverage. The positive cumulative net present value (NPV) for both LCC scenarios encourages economic investments in building renovations with BIPV systems.

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Section: Building Typementioning

confidence: 99%

Carbon Life Cycle Assessment and Costing of Building Integrated Photovoltaic Systems for Deep Low-Carbon Renovation

Amoruso

Schuetze

2023

Sustainability

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“…Renewable energy sources have the potential for effective advancement and sustainable growth. The importance of using renewable energy sources holds a unique position in the realm of contemporary technological development 1 . The utilization of solar power represents a highly promising opportunity for the production of energy in the near future, primarily due to its capacity to serve as a renewable natural energy source, which ranks among the largest of such sources in the world and can be harnessed for distribution beyond the demands of local consumption 2 , 3 .…”

Section: Introductionmentioning

confidence: 99%

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Byun

Jeong

2023

Sci Rep

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Solar energy is a very efficient alternative for generating clean electric energy. However, pollution on the surface of solar panels reduces solar radiation, increases surface transmittance, and raises the surface temperature. All these factors cause photovoltaic (PV) panels to be less efficient. To address this problem, a stacking ensemble classifier-based machine learning model is proposed. In this study, different sources of pollution on each solar panel are used, and their power generation is recorded. The proposed model includes gradient boost, extra tree, and random forest classifiers, with the extra tree classifier serving as a meta-learner. The model takes into account various weather features during the training process, including irradiance and temperature, aiming to increase its accuracy and robustness in identifying pollution sources on the PV panel. Moreover, the proposed model is evaluated using various methods in order to examine performance metrics such as accuracy, F1 score, and precision. Results show that the model can achieve an accuracy score of 97.37%. The model’s performance is also compared to state-of-the-art machine learning models, demonstrating its superiority in accurately classifying pollution sources on PV panels. By utilizing different sources of pollution and weather features during training, the model can accurately classify different pollution sources, resulting in increased power generation efficiency and the longevity of PV panels. The main results of this study can be used to manage and maintain PV panels since the model can identify PV modules that need to be cleaned to keep producing the most power. Furthermore, the efficiency, reliability, and sustainability of PV panels can be further enhanced by the proposed model.

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Local versus outsider developers: Impact on development duration and its’ implications for community acceptance of solar PV plants in South Korea

Ki,

Yun

2024

Energy Policy

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Exploring the operational potential of the forest-photovoltaic utilizing the simulated solar tree

Cited by 5 publications

References 26 publications

Carbon Life Cycle Assessment and Costing of Building Integrated Photovoltaic Systems for Deep Low-Carbon Renovation

Carbon Life Cycle Assessment and Costing of Building Integrated Photovoltaic Systems for Deep Low-Carbon Renovation

A stacking ensemble classifier-based machine learning model for classifying pollution sources on photovoltaic panels

Local versus outsider developers: Impact on development duration and its’ implications for community acceptance of solar PV plants in South Korea

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