Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China

An, Yaning; Chen, Tianyi; Shi, Lei; Heng, Chye Kiang; Fan, Jinglin

doi:10.1016/j.scs.2023.104547

Cited by 30 publications

(14 citation statements)

References 98 publications

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“…Artificial intelligence can assist investors in determining the risk level of new green energy projects, predicting the energy production of various renewable energy sources under different conditions, and anticipating energy demand in different locations at different times of the day through neural modeling analysis. An et al (2023) demonstrated the use of artificial intelligence in determining the optimal location for a solar farm based on the time and intensity of the sun, assisting operators in site layout planning, and controlling solar panels to rotate toward the sun throughout the day for maximum sunlight capture when generating electricity through solar energy.…”

Section: Potential Of Artificial Intelligence-assisted Renewable Ener...mentioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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Climate change is a major threat already causing system damage to urban and natural systems, and inducing global economic losses of over $500 billion. These issues may be partly solved by artificial intelligence because artificial intelligence integrates internet resources to make prompt suggestions based on accurate climate change predictions. Here we review recent research and applications of artificial intelligence in mitigating the adverse effects of climate change, with a focus on energy efficiency, carbon sequestration and storage, weather and renewable energy forecasting, grid management, building design, transportation, precision agriculture, industrial processes, reducing deforestation, and resilient cities. We found that enhancing energy efficiency can significantly contribute to reducing the impact of climate change. Smart manufacturing can reduce energy consumption, waste, and carbon emissions by 30–50% and, in particular, can reduce energy consumption in buildings by 30–50%. About 70% of the global natural gas industry utilizes artificial intelligence technologies to enhance the accuracy and reliability of weather forecasts. Combining smart grids with artificial intelligence can optimize the efficiency of power systems, thereby reducing electricity bills by 10–20%. Intelligent transportation systems can reduce carbon dioxide emissions by approximately 60%. Moreover, the management of natural resources and the design of resilient cities through the application of artificial intelligence can further promote sustainability.

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Section: Potential Of Artificial Intelligence-assisted Renewable Ener...mentioning

confidence: 99%

Artificial intelligence-based solutions for climate change: a review

Chen

Zhang

et al. 2023

Environ Chem Lett

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“…It has been demonstrated that solar energy resources are abundant in cities and have great potential for solar energy resource development. Yawning An et al [27] used GIS software to model and analyze the solar energy potential of four representative areas in Shenzhen. The analysis results showed that Shenzhen had vast solar potential, with 92% of the building rooftops producing more than 1000 kWh/m 2 per year, which provided an alternative energy source for residential and local electricity consumption in Shenzhen.…”

Section: Study Of Solar Energy Utilization In Urban Settlementsmentioning

confidence: 99%

Energy Utilization and Carbon Reduction Potential of Solar Energy in Residential Blocks: A Case Study on a Tropical High-Density City in China

Li,

Wang

et al. 2023

Sustainability

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Energy efficiency in high-density urban areas is increasingly gaining more attention as the energy crisis and environmental issues worsen. Urban morphology is an essential factor affecting the energy consumption and solar energy development potential of buildings. In response to the research gap of previous studies that only analyzed building energy consumption or solar energy potential from a single objective, this paper aims to combine the two objectives of block-scale building energy consumption and solar development potential to explore the joint influence of urban residential morphological elements on correlations between the two. By investigating and summarizing 100 sample cases of Wuhan city blocks, 30 urban residential block prototypes were constructed. The correlations between the leading morphological indicators of the blocks with the building energy consumption and solar energy potential of the residential prototypes were quantified, respectively. The study results show that at certain floor area ratios, the highest solar power generation can be achieved with a mixture of high-rise slabs and high-rise towers, but the building energy intensity level is relatively high; combining building energy consumption and solar power generation, the residential block form of high-rise towers and low-rise villas has incredible energy-saving potential. In addition, the regression analysis results show that three block form indicators, namely the roof-to-envelope area ratio, compacity, and site coverage, have the most prominent influence on building energy intensity and solar power generation, and they all show positive correlations. This study can provide suggestions for urban residential planners and managers to promote urban energy conservation at the design stage.

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“…In this case, PV is the framework to contextualize the research, making the relevance of the topic explicit, but it may lead to misunderstandings. The same applies to other studies in the opposite situation, e.g., [17,26,74,81]. These papers limit their application to solar energy-in two cases adding the term "potential"-in the title and abstract, despite the fact that they also include the calculation of the photovoltaic potential.…”

Section: Scopementioning

confidence: 99%

Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation

Anselmo,

Ferrara

2023

Energies

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In the current framework of energy transition, renewable energy production has gained a renewed relevance. A set of 75 papers was selected from the existing literature and critically analyzed to understand the main inputs and tools used to calculate solar energy and derive theoretical photovoltaic production based on geographic information systems (GISs). A heterogeneous scenario for solar energy estimation emerged from the analysis, with a prevalence of 2.5D tools—mainly ArcGIS and QGIS—whose calculation is refined chiefly by inputting weather data from databases. On the other hand, despite some minor changes, the formula for calculating the photovoltaic potential is widely acknowledged and includes solar energy, exploitable surface, performance ratio, and panel efficiency. While sectorial studies—targeting a specific component of the calculation—are sound, the comprehensive ones are generally problematic due to excessive simplification of some parts. Moreover, validation is often lacking or, when present, only partial. The research on the topic is in constant evolution, increasingly moving towards purely 3D models and refining the estimation to include the time component—both in terms of life cycle and variations between days and seasons.

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Solar energy potential using GIS-based urban residential environmental data: A case study of Shenzhen, China

Cited by 30 publications

References 98 publications

Artificial intelligence-based solutions for climate change: a review

Artificial intelligence-based solutions for climate change: a review

Energy Utilization and Carbon Reduction Potential of Solar Energy in Residential Blocks: A Case Study on a Tropical High-Density City in China

Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation

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