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Leak fault significantly affects the reliable and sustainable green hydrogen energy supply by renewable Power-to-Hydrogen (P2H2) system. Deep learning has been widely applied to automated leak fault detection and diagnosis of hydrogen systems. However, due to the intermittency of wind/solar-power-generation, existing approaches developed by monitored signals have the bad generalization to leak scenario under large volatility of power-to‑hydrogen production. This study introduces graph deep probability learning approach, in which an attention-based graph neural network (GNN) learns the dependency between installed sensors. Variational inference is integrated to model posterior distribution of sensor dependencies, by using which the leak fault under large volatility of power-to‑hydrogen production is detected and localized by using normal time-series signals under different timeslots of one day as training data. Experiment of hydrogen leak faults from P2H2 system is conducted to verify the more accuracy of proposed approach compared to 6 state-of-the-art approaches. Results demonstrated our proposed approach detects the leak fault more accurately with a higher AUC of 0.96 and successfully localizes all the leak faults. This study supports more reliable and efficient safety monitoring for upcoming renewable P2H2 system in future. • A graph deep probability learning approach is proposed to accurately detect and localize leak fault of P2H2 production. • Experiment of hydrogen leak is conducted to compare our proposed approach with 6 existing approaches. • Our approach achieves higher detection accuracy and localizes all faults when large volatility of P2H2 production exists. • Our proposed approach has better generalization to faults detection and diagnosis of upcoming renewable P2H2 system. [ABSTRACT FROM AUTHOR]

• Proposing a framework to assess the emission reduction potential of ride-sharing from empirical mobility data. • Proposing a deep learning-based method that shows higher performance than traditional simulation method. • Over 1 million historical individual GPS records in Tokyo area are employed for analysis. • We found that averagely 26.97% of traveling distance can be saved and maximally 84.52% of reduction of CO2 emission. Spreading green and low-consumption transportation methods is becoming an urgent priority. Ride-sharing, which refers to the sharing of car journeys so that more than one person travel in a car, and prevents the need for others to drive to a location themselves, is a critical solution to this issue. Before being introduced into one place, it needs a potential analysis. However, current studies did this kind of analysis based on home and work locations or social ties between people, which is not precise and straight enough. Few pieces of research departed from real mobility data, but uses time-consuming methodology. In this paper, we proposed an analysis framework to bridge this gap. We chose the case study of Tokyo area with over 1 million GPS travel records and trained a deep learning model to find out this potential. From the computation result, on average, nearly 26.97% of travel distance could be saved by ride-sharing, which told us that there is a significant similarity in the travel pattern of people in Tokyo and there is considerable potential of ride-sharing. Moreover, if half of the original public transit riders in our study case adopt ride-sharing, the quantity of CO 2 is estimated to be reduced by 84.52%; if all of the original public transit riders in our study case adopt ride-sharing, 83.56% of CO 2 emission reduction can be expected with a rebound effect because of increase of participants from public transit. Ride-sharing can not only improve the air quality of these center business districts but also alleviate some city problems like traffic congestion. We believe the analysis of the potential of ride-sharing can provide insight into the decision making of ride-sharing service providers and decision-makers. [ABSTRACT FROM AUTHOR]

Incorrect predictions or underestimation of a city's solar potential can result from neglecting common features of photovoltaic (PV) panels from remote sensing images. This paper proposes an improved approach to address the challenge of accurately segmenting PV panels from remote sensing images using deep learning methods. The proposed method incorporates common features of PV panels and a constraint refinement module (CRM) to perform the localization of PV panel regions and shape regularization more accurately. Specifically, the method uses a color loss function based on prior knowledge of color to refine the predicted region with correct color information among confusing objectives, and a shape loss function based on multi-layer shape targets calculation to refine the initial segments and constrain the edge information of predicted regions. Different CRMs are embedded into the four refined initial segment modules, respectively, to improve the detection IoU of PV panels by up to 7.44%. The best CRM-integrated model performs the best IoU of 74.66% when segmenting PV panels. The proposed method has important implications for urban PV panel segmentation at the city level and provides a promising solution for remote sensing image-based PV plate segmentation tasks in challenging scenarios. • We proposed an improved feature-oriented segmentation method to enhance the accuracy and contextual information of PV panels. • We incorporated common features of PV panels and CRM to perform PV panel region localization and shape regularization. • We designed a color constraint refinement module via prior knowledge to refine the predicted region among confusing targets. • We designed a shape constraint refinement module based on multi-layer shape targets to constrain the edge of predicted areas. [ABSTRACT FROM AUTHOR]

The increasing need to develop renewable energy sources to combat climate change has led to a significant rise in demand for photovoltaic (PV) installations. Consequently, accurately detecting and estimating the capacity and potential for electricity generation of these installed PV systems has become crucial for effective energy management. However, due to the limitation of localized mechanism in convolution operations, traditional FCN-based methods face challenges in capturing global dependencies among remote sensing image patches. As a result, they struggle to model long-range interactions between different PV panels with diverse structures, including varying size, shape, and texture, particularly in distributed residential areas. To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation. Specifically, Mix Transformer block is incorporated in the encoder to enhance the modeling of global context, while the U-Shaped structure enables the combination of multi-level features, resulting in enriched feature representation, which helps the model better understand objects in the images, regardless of their shape, size, and texture. In addition, we implement the PointRend module in the decoder to obtain finer segmentation boundary details, and utilize a novel refiner loss function during the training process to alleviate the problem of extremely unbalanced samples. The experimental results from the Heilbronn datasets demonstrate the remarkable performance of our proposed TransPV model in addressing intra-class diversity of PV structures, surpassing previous state-of-the-art methods with an impressive IoU and accuracy of 0.802 and 0.876, respectively. Furthermore, our model exhibits high generalization capability on the BDPV dataset with IoU of 0.745. The obtained results highlight the superiority of TransPV in improving accuracy and addressing diverse structure issues in PV segmentation, which provides valuable insights for optimizing the efficiency and sustainability of renewable energy. • The main challenges in real-world PV segmentation scenarios are studied. • The Vision Transformer mechanism is pioneeringly applied to enhance PV segmentation. • A novel U-shaped Vision Transformer model is designed for PV segmentation refinement. • Our method showcases remarkable segmentation accuracy and generalization capability. [ABSTRACT FROM AUTHOR]