Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?

• URL: http://arxiv.org/abs/2309.12214v3
• Date: Thu, 9 Nov 2023 13:09:01 GMT
• Title: Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems?
• Authors: Gabriel Kasmi and Laurent Dubus and Yves-Marie Saint-Drenan and Philippe Blanc
• Abstract summary: Remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. We leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions.
• Score: 0.8192907805418583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems.

Related papers

• Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic Systems [0.7499722271664147]
  Remote sensing of rooftop PV systems using deep learning emerged as a promising solution. Existing techniques are not reliable enough to construct up-to-date statistics on the rooftop PV fleet. This work proposes a comprehensive evaluation of the effects of distribution shifts on the classification accuracy of deep learning models trained to detect rooftop PV panels on overhead imagery.
  arXiv  Detail & Related papers  (2024-07-31T14:34:18Z)
• S3Former: Self-supervised High-resolution Transformer for Solar PV Profiling [6.646508986504754]
  We introduce S3Former, designed to segment solar panels from aerial imagery and provide size and location information. S3Former features a Masked Attention Mask Transformer incorporating a self-supervised learning pretrained backbone. We evaluate S3Former using diverse datasets, demonstrate improvement state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-07T16:56:21Z)
• Advanced simulation-based predictive modelling for solar irradiance sensor farms [0.5292801941204784]
  This work introduces a novel framework named Cloud-based Analysis and Integration for Data Efficiency (CAIDE) CAIDE is designed for real-time monitoring, management, and forecasting of solar irradiance sensor farms. The framework has important implications for the deployment of solar plants and the future of renewable energy sources.
  arXiv  Detail & Related papers  (2024-04-05T15:44:51Z)
• Unveiling the Invisible: Enhanced Detection and Analysis of Deteriorated Areas in Solar PV Modules Using Unsupervised Sensing Algorithms and 3D Augmented Reality [1.0310343700363547]
  This article presents a groundbreaking methodology for automatically identifying and analyzing anomalies like hot spots and snail trails in Solar Photovoltaic (PV) modules. By transforming the traditional methods of diagnosis and repair, our approach not only enhances efficiency but also substantially cuts down the cost of PV system maintenance. Our immediate objective is to leverage drone technology for real-time, automatic solar panel detection, significantly boosting the efficacy of PV maintenance.
  arXiv  Detail & Related papers  (2023-07-11T09:27:00Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Learning Stability Attention in Vision-based End-to-end Driving Policies [100.57791628642624]
  We propose to leverage control Lyapunov functions (CLFs) to equip end-to-end vision-based policies with stability properties. We present an uncertainty propagation technique that is tightly integrated into att-CLFs.
  arXiv  Detail & Related papers  (2023-04-05T20:31:10Z)
• DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping [0.0]
  We build on existing work to propose an automated PV registry pipeline. This pipeline automatically generates a dataset recording all distributed PV installations' location, area, installed capacity, and tilt angle. We propose an unsupervised method based on the it Registre national d'installation (RNI), that centralizes all individual PV systems aggregated at communal level. We deploy our model on 9 French it d'epartements covering more than 50 000 square kilometers, providing the largest mapping of distributed PV panels with this level of detail to date.
  arXiv  Detail & Related papers  (2022-07-15T13:23:24Z)
• A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle [65.99880594435643]
  We propose a novel model to detect panel defects on aerial images captured by unmanned aerial vehicle. The model combines detections of panels and defects to refine its accuracy. The proposed model has been validated on two big PV plants in the south of Italy.
  arXiv  Detail & Related papers  (2021-11-23T08:04:32Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space [62.39318039798564]
  We propose a scheduling mechanism capable of taking advantage of correlated information. The proposed mechanism is capable of determining the frequency with which sensors should transmit their updates. We show that our solution can significantly extend the sensors' lifetime.
  arXiv  Detail & Related papers  (2020-11-19T09:53:27Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.