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Träfflista för sökning "WFRF:(Qian Yan) ;lar1:(mdh)"

Sökning: WFRF:(Qian Yan) > Mälardalens universitet

  • Resultat 1-10 av 10
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1.
  • Jiang, Mingkun, et al. (författare)
  • Using Existing Infrastructure to Realize Low-Cost and Flexible Photovoltaic Power Generation in Areas with High-Power Demand in China
  • 2020
  • Ingår i: iScience. - : Elsevier Inc.. - 2589-0042. ; 23:12
  • Tidskriftsartikel (refereegranskat)abstract
    • Energy Policy; Energy Resources; Energy Systems; Energy Management © 2020 This study develops a new concept involving using the existing infrastructure for photovoltaic (PV) generation to reduce the costs associated with increased land use and to avoid curtailment due to the mismatch between power supply and demand. We establish a method to estimate the technological potential and economic performance of the PV systems deployed in coal-fired power plants in China. The potential capacity of the examined 1,082 units in China reaches 4 GWe, which is equivalent to 32% of China's newly installed distributed PV capacity in 2019. A total of 87% of PV systems achieve plant-side grid parity compared with desulfurized coal benchmark electricity prices. To the best of our knowledge, this is the first study that investigates the use of rooftops and coal storage sheds in power plants to facilitate low-cost, flexible PV power generation, thus opening a new channel for future PV generation development.
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2.
  • Liu, Junwei, et al. (författare)
  • Polymer synergy for efficient hole transport in solar cells and photodetectors
  • 2023
  • Ingår i: Energy & Environmental Science. - : ROYAL SOC CHEMISTRY. - 1754-5692 .- 1754-5706.
  • Tidskriftsartikel (refereegranskat)abstract
    • Hole transport materials (HTMs) have greatly advanced the progress of solution-based electronic devices in the past few years. Nevertheless, most devices employing dopant-free organic HTMs can only deliver inferior performance. In this work, we introduced a novel "polymer synergy" strategy to develop versatile dopant-free polymer HTMs for quantum dot/perovskite solar cells and photodetectors. With this synergy strategy, the optical, electrical and aggregation properties of polymer HTMs can be modulated, resulting in complementary absorption, high hole mobility, favorable energy landscape and moderate aggregation. Moreover, a clear orientational transition was observed for the developed HTMs with a 9-fold increase in the face-on/edge-on ratio, providing a highway-like carrier transport for electronic devices, as revealed by in situ characterization and ultrafast transient absorption. With these benefits, the photovoltaic and photodetection performance of quantum dot devices were boosted from 11.8% to 13.5% and from 2.95 x 10(12) to 3.41 x 10(13) Jones (over a 10-fold increase), respectively. Furthermore, the developed polymer HTMs can also significantly enhance the photovoltaic and photodetection performance of perovskite devices from 15.1% to 22.7% and from 2.7 x 10(12) to 2.17 x 10(13)Jones with the same device structure, indicating their great application potential in the emerging optoelectronics.
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4.
  • Qian, Zhen, et al. (författare)
  • Vectorized dataset of roadside noise barriers in China using street view imagery
  • 2022
  • Ingår i: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 14:9, s. 4057-4076
  • Tidskriftsartikel (refereegranskat)abstract
    • Roadside noise barriers (RNBs) are important urban infrastructures to ensure that cities remain liveable. However, the absence of accurate and large-scale geospatial data on RNBs has impeded the increasing progress of rational urban planning, sustainable cities, and healthy environments. To address this problem, this study creates a vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework. First, intensive sampling is performed on the road network of each city based on OpenStreetMap, which is used as the georeference for downloading 6 x 10(6) Baidu Street View (BSV) images. Furthermore, considering the prior geographic knowledge contained in street view images, convolutional neural networks incorporating image context information (IC-CNNs) based on an ensemble learning strategy are developed to detect RNBs from the BSV images. The RNB dataset presented by polylines is generated based on the identified RNB locations, with a total length of 2667.02 km in 222 cities. Last, the quality of the RNB dataset is evaluated from two perspectives, i.e., the detection accuracy and the completeness and positional accuracy. Specifically, based on a set of randomly selected samples containing 10 000 BSV images, four quantitative metrics are calculated, with an overall accuracy of 98.61 %, recall of 87.14 %, precision of 76.44 %, and F-1 score of 81.44 %. A total length of 254.45 km of roads in different cities are manually surveyed using BSV images to evaluate the mileage deviation and overlap level between the generated and surveyed RNBs. The root mean squared error for the mileage deviation is 0.08 km, and the intersection over union for overlay level is 88.08% +/- 2.95 %. The evaluation results suggest that the generated RNB dataset is of high quality and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts. Besides that, the benchmark dataset of the labeled BSV images can also support more work on RNB detection, such as developing more advanced deep learning algorithms, fine-tuning the existing computer vision models, and analyzing geospatial scenes in BSV. The generated vectorized RNB dataset and the benchmark dataset of labeled BSV imagery are publicly available at https://doi.org/10.11888/Others.tpdc.271914 (Chen, 2021).
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5.
  • Zhang, Kai, et al. (författare)
  • Power generation assessment of photovoltaic noise barriers across 52 major Chinese cities
  • 2024
  • Ingår i: Applied Energy. - : Elsevier Ltd. - 0306-2619 .- 1872-9118. ; 361
  • Tidskriftsartikel (refereegranskat)abstract
    • Photovoltaic noise barriers (PVNBs) have the potential to contribute to sustainable urban development by increasing the supply of renewable energy to cities while decreasing traffic noise pollution. However, estimating the power generation of PVNBs at the city or national scale remains a challenge due to the complexities of the urban environment and the difficulties associated with collecting data on road noise barriers (RNBs) and radiation. This study used RNBs, 2.5-dimensional (2.5D) buildings, and hourly time resolution radiation data, to estimate the power generation of PVNBs in 52 of China's major cities. First, hourly building shadows were estimated for each day of the year, covering the period from sunrise to sunset, to identify areas of RNB that are shaded at any given time. Second, hourly clear-sky radiation data were collected and corrected using a radiation correction model to simulate real weather radiation. Finally, utilizing an inclined surface radiation estimation model, the photovoltaic (PV) potential both inside and outside RNBs affected by building shadows was assessed. Subsequently, the power generation of PVNB was estimated based on parameters of mainstream PV systems in the market. The results show that the RNB mileage in 52 selected cities represents 87.7% of China's total RNB mileage. Building shadows often result in a radiation loss of approximately 30% for RNBs reception. The installed capacity and annual power generation of PVNBs in all investigated cities are 2.04 GW and 690.74 GWh, respectively. This study estimates the comprehensive PV potential of potentially exploitable PVNBs in China, offering essential scientific insights to inform and facilitate the strategic development of PVNB projects at both the national and municipal levels.
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6.
  • Zhang, Kai, et al. (författare)
  • Quantifying the photovoltaic potential of highways in China
  • 2022
  • Ingår i: Applied Energy. - : Elsevier BV. - 0306-2619 .- 1872-9118. ; 324, s. 119600-
  • Tidskriftsartikel (refereegranskat)abstract
    • Installing photovoltaic (PV) modules on highways is considered a promising way to support carbon neutrality in China. However, collecting the area of the highway, and precisely assessing the shadow area of the highway under complex terrain remain challenges. That severely hinders the assessment of highway PV potential. To address these challenges, a spatiotemporal model is developed in this study to estimate the annual solar PV potential on highways over the whole Chinese territory. First, the areas of different highway segments are calculated based on highway network and highway toll stations. Second, hourly shadow area on highways created by nearby terrain is estimated based on a digital elevation model (DEM). When calculating the highway PV potential, the solar irradiation received in these shadow areas is regarded as zero. Finally, the PV potential of all lanes and emergency lanes was estimated at the prefecture-level city scale using surface radiation data and radiation assessment models. Based on the highway data with a total mileage of 143,684 km at the end of 2020, the results show that the annual PV potential is 3,932 TW and that the corresponding installed capacity is 700.85 GW, which can generate clean electricity at a rate of up to 629.06 TWh. The annual PV potential of highways in the southeast is greater than that in the northwest owing to the higher highway density in the southeast. This study provides a reference basis for highway PV construction planning and suitably assessment in each region of China for PV highway development.
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7.
  • Zhang, K., et al. (författare)
  • Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis
  • 2022
  • Ingår i: Sustainable cities and society. - : Elsevier Ltd. - 2210-6707. ; 78
  • Tidskriftsartikel (refereegranskat)abstract
    • Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs).
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8.
  • Zhang, Z., et al. (författare)
  • Carbon mitigation potential afforded by rooftop photovoltaic in China
  • 2023
  • Ingår i: Nature Communications. - : Nature Research. - 2041-1723. ; 14:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries. 
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9.
  • Zhang, Zhixin, et al. (författare)
  • Vectorized rooftop area data for 90 cities in China
  • 2022
  • Ingår i: Scientific Data. - : Springer Nature. - 2052-4463. ; 9:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Reliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km(2) across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.
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10.
  • Zhong, T., et al. (författare)
  • Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery
  • 2021
  • Ingår i: Renewable energy. - : Elsevier Ltd. - 0960-1481 .- 1879-0682. ; 168, s. 181-194
  • Tidskriftsartikel (refereegranskat)abstract
    • Solar energy captured by solar photovoltaic (PV) systems has great potential to meet the high demand for renewable energy sources in urban areas. A photovoltaic noise barrier (PVNB) system, which integrates a PV system with a noise barrier, is a promising source for harvesting solar energy to overcome the problem of having limited land available for solar panel installations. When estimating the solar PV potential at the city scale, it is difficult to identify sites for installing solar panels. A computational framework is proposed for estimating the solar PV potential of PVNB systems based on both existing and planned noise barrier sites. The proposed computational framework can identify suitable sites for installing photovoltaic panels. A deep learning-based method is used to detect existing noise barrier sites from massive street-view images. The planned noise barrier sites are identified with urban policies. Based on the existing and planned sites of noise barriers in Nanjing, the annual solar PV potentials in 2019 are 29,137 MW h and 113,052 MW h, respectively. The estimation results show that the potential PVNB systems based on the existing and planned noise barrier in 2019 have the potential installed capacity of 14.26 MW and 57.24 MW, with corresponding potential annual power generation of 4662 MW h and 18,088 MW h, respectively.
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