Comparative analysis on the effectiveness of green roofs and photovoltaic panels as sustainable rooftop technologies

Jayasooriya, Varuni; Fernando, Samuel; Silva, Channa; Muthukumaran, Shobha

doi:10.1007/s11356-023-28124-5

Cited by 8 publications

(3 citation statements)

References 66 publications

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“…In China, there are many policies to promote photovoltaic roofing. However, there is no specific policy for photovoltaic green roofs, and many researchers have shown that incentive policies play a crucial role in promoting technology [16][17][18][19], which is also applicable to photovoltaic green roofs. On the one hand, incentive policies can motivate enterprises to provide materials for photovoltaic green roofs.…”

Section: Discussionmentioning

confidence: 99%

“…Fernando et al investigated the synergistic effect of photovoltaic panels and green roofs and verified the benefits of their combination [16]. Varuni Jayasooriya validated the financial feasibility of green and photovoltaic roofs through an investment payback period and net present value analysis [17]. Through probability analysis, Zhaozhi Wang et al verified that the GR-PV system is a low-risk investment, resulting in a shorter payback period for energy and carbon emission investments [18].…”

Section: Introductionmentioning

confidence: 99%

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What Are the Obstacles to Promoting Photovoltaic Green Roofs in Existing Buildings? The Integrated Fuzzy DEMATEL-ISM-ANP Method

Li,

Xiang,

Xia

et al. 2023

Sustainability

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Photovoltaic green roofs can contribute to energy conservation in buildings and the sustainable development of cities, but they have yet to be widely used due to many factors. Therefore, it is necessary to investigate the factors limiting the promotion of photovoltaic green roofs and to clarify their interactions. Based on the existing literature and expert recommendations, this paper summarizes 20 factors affecting its promotion. Fuzzy DEMATEL was used to analyze the causal relationship and importance of the influencing factors. A hierarchical structure was established using the interpretative structural model (ISM) to visually represent the layered structure and pathways of the factors. The role and status of each influencing factor were determined using the cross-influence matrix analysis method (MICMAC). Finally, the analytic network process (ANP) was combined with the overall influence matrix to determine the overall weight of each factor. Combined with the DEMATEL-ISM-ANP method, nine key influencing factors, namely, the lack of incentive policies, imperfect technical specifications and evaluation standard system, local economic development level, residents’ cognition, residents’ willingness, enterprises’ technical problems, the lack of relevant talents, the lack of capital investment, and cooperation mode, were finally identified and analyzed, and suggestions and strategies for improvement were proposed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What Are the Obstacles to Promoting Photovoltaic Green Roofs in Existing Buildings? The Integrated Fuzzy DEMATEL-ISM-ANP Method

Li,

Xiang,

Xia

et al. 2023

Sustainability

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show abstract

“…Several governments have proposed the establishment of utility-scale solar PV plants worldwide to achieve the transition from conventional to modern energy structures, but utility-scale PV power plants pose the challenges of not only substantial installation space but also high construction and maintenance costs [4,5]. In contrast, rooftop PV panels can be easily installed on residential, commercial, and industrial building rooftops, featuring the advantages of effective space utilization and cost-effectiveness, thereby reducing land requirements for utility-scale PV power plants and making rooftop-distributed PV systems a more attractive and encouraged option [6]. According to SolarPower Europe, global rooftop PV installations experienced a staggering growth of 50%, reaching 118 GW in 2022, representing 49.5% of total installations, demonstrating the promising potential of rooftop PV systems as an alternative energy generation technology.…”

Section: Introductionmentioning

confidence: 99%

Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery

Wang,

Chen,

Shi

et al. 2023

Remote Sensing

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The photovoltaic (PV) industry boom has accelerated the need for accurately understanding the spatial distribution of PV energy systems. The synergy of remote sensing and artificial intelligence presents significant prospects for PV energy monitoring. Currently, numerous studies have focused on extracting rooftop PV systems from airborne or satellite imagery, but their small-scale and size-varying characteristics make the segmentation results suffer from PV internal incompleteness and small PV omission. To address these issues, this study proposed a size-aware deep learning network called Rooftop PV Segmenter (RPS) for segmenting small-scale rooftop PV systems from high-resolution imagery. In detail, the RPS network introduced a Semantic Refinement Module (SRM) to sense size variations of PV panels and reconstruct high-resolution deep semantic features. Moreover, a Feature Aggregation Module (FAM) enhanced the representation of robust features by continuously aggregating deeper features into shallower ones. In the output stage, a Deep Supervised Fusion Module (DSFM) was employed to constrain and fuse the outputs at different scales to achieve more refined segmentation. The proposed RPS network was tested and shown to outperform other models in producing segmentation results closer to the ground truth, with the F1 score and IoU reaching 0.9186 and 0.8495 on the publicly available California Distributed Solar PV Array Dataset (C-DSPV Dataset), and 0.9608 and 0.9246 on the self-annotated Heilbronn Rooftop PV System Dataset (H-RPVS Dataset). This study has provided an effective solution for obtaining a refined small-scale energy distribution database.

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Global Analysis of Combined Photovoltaic Green and Cool Roofs Under Climate Change

Hassoun,

Cook

2024

Advanced Sustainable Systems

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Sustainable roofing configurations, including green and white roofs, can reduce rooftop surface temperatures compared to conventional surfaces and can therefore enhance photovoltaic (PV) system performance due to the temperature dependence of PV cells. Previous research, primarily experimental, recognized the synergy of combining PV with green or cool roofs. However, the influence of geographic and climatic factors on the performance of these combined systems, particularly in future climates affected by climate change, remains unclear. This work integrates three roof configurations (gravel, green, and white) into rooftop solar energy modeling across thirteen cities with different climate types, under current and future climate scenarios. Results indicate limited efficiency gains (< 2%) across all cities and climates, challenging previous findings. Yield is expected to increase in some cities receiving more solar irradiation in the future but decrease in others due to rising temperatures. Green and cool roofs can partially offset the effects of climate change on yield. PV‐white roofs consistently outperform PV‐green roofs, with the performance gap expected to widen in future climates. PV‐green roofs excel in tropical climates with high irradiation and precipitation levels. Overall, the outcomes of this study inform the design and planning of sustainable buildings in response to climate change challenges.

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Comparative analysis on the effectiveness of green roofs and photovoltaic panels as sustainable rooftop technologies

Cited by 8 publications

References 66 publications

What Are the Obstacles to Promoting Photovoltaic Green Roofs in Existing Buildings? The Integrated Fuzzy DEMATEL-ISM-ANP Method

What Are the Obstacles to Promoting Photovoltaic Green Roofs in Existing Buildings? The Integrated Fuzzy DEMATEL-ISM-ANP Method

Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery

Global Analysis of Combined Photovoltaic Green and Cool Roofs Under Climate Change

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