Rooftop Detection for Planning of Solar PV Deployment: A Case Study in Abu Dhabi

Joshi, Bikash; Baluyan, Hayk; Al-Hinai, Amer; Woon, Wei Lee

doi:10.1007/978-3-319-13290-7_11

Cited by 7 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research regarding urban applications of PV has addressed, with growing sophistication, methodological issues in determining the overall technical potential of PV in cities . Such investigations have been performed for a wide variety of urban conditions ranging from cities in Nigeria to India, Abu Dhabi, the Netherlands, the United States, and Brazil …”

Section: Brief Review Of Recent ‘Solar City’ Assessment Literaturementioning

confidence: 99%

Multivariate analysis of solar city economics: impact of energy prices, policy, finance, and cost on urban photovoltaic power plant implementation

Byrne

Taminiau

Kim

et al. 2017

WIREs Energy & Environment

View full text Add to dashboard Cite

Previous research suggests that the potential for city‐scale photovoltaic (PV) applications is substantial across the globe. Successful implementation of ‘solar city’ options will depend on the strategic application of finance mechanisms, solar energy soft cost policies, and other policy tools, as well as the grid price of electricity. Capital markets recently have embraced the roll‐out of new financial instruments, including ‘green bonds,’ which could be incorporated into solar city project design to attract large investments at a low cost. A multivariate analysis method is employed to consider solar city possibilities for six cities: Amsterdam, London, Munich, New York, Seoul, and Tokyo. A Monte Carlo simulation is conducted to capture the probabilistic nature of uncertainties in the parameters and their relative importance to the financial viability of a solar city project. The analysis finds that solar city implementation strategies can be practical under a broad range of circumstances. WIREs Energy Environ 2017, 6:e241. doi: 10.1002/wene.241 This article is categorized under: Photovoltaics > Systems and Infrastructure Solar Heating and Cooling > Economics and Policy Energy Policy and Planning > Economics and Policy

show abstract

Section: Brief Review Of Recent ‘Solar City’ Assessment Literaturementioning

confidence: 99%

Multivariate analysis of solar city economics: impact of energy prices, policy, finance, and cost on urban photovoltaic power plant implementation

Byrne

Taminiau

Kim

et al. 2017

WIREs Energy & Environment

View full text Add to dashboard Cite

show abstract

“…Furthermore, a study initially utilized Multi-Layer Perceptron (MLP) to ascertain annual electricity generation and peak power of photovoltaic panels on residential rooftops. Subsequently, Support Vector Regression (SVP) was employed for data classification, facilitating finer-grained energy planning [12]. Additionally, the utilization of the DBSCAN clustering algorithm, in combination with infrastructure data from OpenStreetMap, led to the construction of a global solar photovoltaic installation dataset [13].…”

Section: Introductionmentioning

confidence: 99%

Assessing Rooftop Photovoltaic Strategies in Shenzhen's Pingshan District: Cluster-Based Analysis of Energy, Environment, and Economy

Li,

Wang,

et al. 2023

Preprint

View full text Add to dashboard Cite

Rooftop photovoltaics (RPVs) play a crucial role in reducing urban carbon emissions and aiding the shift toward net-zero energy systems. However, the complexities of urban settings introduce significant variability in RPV costs across different locations and times. This study focuses on the Pingshan District in Shenzhen, a representative example of cities in southern China. The study analyzes geometric characteristics and community types from various urban areas to perform K-means++ clustering. These typical community types are then used to comprehensively assess RPV deployment in terms of energy, environment, and economics. The study provides practical strategies for integrating RPVs, offering insights that contribute to sustainable urban energy transitions.

show abstract

“…With the development of satellite sensor technology, many remote-sensing images have been acquired for PV extraction. PV panels can be detected and segmented from remote-sensing images by designing representative features (e.g., color, geometry, and texture) using the threshold segmentation algorithm [6,7], the edge detection algorithm [8,9], or the SVM algorithm in machine learning [10,11]. However, these features vary due to the atmospheric conditions, lighting, and observation scales, resulting in weak accuracy and the generalization of traditional methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The world is transitioning to renewable energy, with photovoltaic (PV) solar power being one of the most promising energy sources. Large-scale PV mapping provides the most up-to-date and accurate PV geospatial information, which is crucial for planning and constructing PV power plants, optimizing energy structure, and assessing the ecological impact of PVs. However, previous methods of PV extraction relied on simple models and single data sources, which could not accurately obtain PV geospatial information. Therefore, we propose the Filter-Embedded Network (FEPVNet), which embeds high-pass and low-pass filters and Polarized Self-Attention (PSA) into a High-Resolution Network (HRNet) to improve its noise resistance and adaptive feature extraction capabilities, ultimately enhancing the accuracy of PV extraction. We also introduce three data migration strategies by combining Sentinel-2, Google-14, and Google-16 images in varying proportions and transferring the FEPVNet trained on Sentinel-2 images to Gaofen-2 images, which improves the generalization performance of models trained on a single data source for extracting PVs in images of different scales. Our model improvement experiments demonstrate that the Intersection over Union (IoU) of FEPVNet in segmenting China PVs in Sentinel-2 images reaches 88.68%, a 2.37% increase compared to the HRNet. Furthermore, we use FEPVNet and the optimal migration strategy to extract photovoltaics across scales, achieving a precision of 94.37%. In summary, this study proposes the FEPVNet model with adaptive strategies for extracting PVs from multiple image sources, with significant potential for application in large-scale PV mapping.

show abstract

Rooftop Detection for Planning of Solar PV Deployment: A Case Study in Abu Dhabi

Cited by 7 publications

References 14 publications

Multivariate analysis of solar city economics: impact of energy prices, policy, finance, and cost on urban photovoltaic power plant implementation

Multivariate analysis of solar city economics: impact of energy prices, policy, finance, and cost on urban photovoltaic power plant implementation

Assessing Rooftop Photovoltaic Strategies in Shenzhen's Pingshan District: Cluster-Based Analysis of Energy, Environment, and Economy

FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images

Contact Info

Product

Resources

About