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Remote Sensing of Renewable Energy
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Remote Sensing of Renewable Energy

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 11025

Special Issue Editors

Prof. Dr. Stephan Schlüter

E-Mail Website
Guest Editor
Department of Mathematics, Natural and Economic Sciences, Ulm University of Applied Sciences, Ulm 89233, Germany
Interests: statistical data analytics; forecasting; renewable energy
Prof. Dr. Jung-Sup Um

E-Mail Website
Guest Editor
Department of Geography, Kyungpook National University, Daegu 41566, Republic of Korea
Interests: cyber-physical system; remote sensing; geographic information system; designing of special information system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

The energy sector is one of the key drivers of global warming as it contributes to large parts of worldwide greenhouse gas emissions. In order to reach the target of a sustainable economy, the sector needs to transform, especially by significantly increasing the share of renewable energies. However, energy sources such as wind and sunshine are highly stochastic. Both weather conditions and production outputs need to be surveyed closely in order to maintain grid stability. For this purpose, all kinds of sensors and other recording systems such as unmanned aerial vehicles (UAVs) are required. For example, UAVs are applied to monitor the current status of solar panels, sensors measure heat, electricity production, wind speed, etc. With the increasing significance of renewable energies techniques in this field have been developing quickly.

This Special Issue intends to provide an overview over the latest developments in the field of remote sensing on renewable energies. These might be novel/improved methods, techniques, or algorithms in the field of remote sensing. The objective is clear but the variety of methods is high. Therefore, articles may come from the fields of engineering, statistics, data science, economics, or mathematics, for example. Articles may address, but are not limited to, the following topics:

  • Advancements in error detection on solar panels or concentrated solar power mirrors.
  • Advancements in UAV-based solar panel monitoring.
  • Advances in the analysis of data (from sensors or satellites, for example).
  • Data analytics in general.
  • Technical advances in the field of remote sensing.
  • Sensor data-based forecasting of renewable energy.
  • Wind power remote sensing.
  • Solar power remote sensing.

Prof. Dr. Stephan Schlüter
Prof. Dr. Jung-Sup Um
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • UAVs
  • Pattern recognition
  • Greenhouse gases
  • Data analytics
  • Sensor technology
  • Wind and solar power

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

17 pages, 5412 KiB  
Article
Annual Daily Irradiance Analysis of Clusters in Mexico by Machine Learning Algorithms
by Jared D. Salinas-González, Alejandra García-Hernández, David Riveros-Rosas, Adriana E. González-Cabrera, Alejandro Mauricio-González, Carlos E. Galván-Tejada, Sodel Vázquez-Reyes and Hamurabi Gamboa-Rosales
Remote Sens. 2024, 16(4), 709; https://doi.org/10.3390/rs16040709 - 18 Feb 2024
Cited by 1 | Viewed by 1365
Abstract
The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges [...] Read more.
The assessment of solar resources involves the utilization of physical or satellite models for the determination of solar radiation on the Earth’s surface. However, a critical aspect of model validation necessitates comparisons against ground-truth measurements obtained from surface radiometers. Given the inherent challenges associated with establishing and maintaining solar radiation measurement networks—characterized by their expense, logistical complexities, limited station availability and the imperative consideration of climatic criteria for siting—countries endowed with substantial climatic diversity face difficulties in station placement. In this investigation, the measurements of annual solar irradiation, from meteorological stations of the National Weather Service in Mexico, were compared in different regions clustered by similarities in altitude, TL Linke, albedo and cloudiness index derived from satellite images; the main objective is to find the best ratio of annual solar irradiation in a set of clusters. Employing machine learning algorithms, this research endeavors to identify the most suitable model for predicting the ratio of annual solar irradiation and to determine the optimal number of clusters. The findings underscore the efficacy of the L-method as a robust technique for regionalization. Notably, the cloudiness index emerges as a pivotal feature, with the Random Forest algorithm yielding superior performance with a R2 score of 0.94, clustering Mexico into 17 regions. Full article
(This article belongs to the Special Issue Remote Sensing of Renewable Energy)
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21 pages, 34189 KiB  
Article
Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery
by Jianxun Wang, Xin Chen, Weiyue Shi, Weicheng Jiang, Xiaopu Zhang, Li Hua, Junyi Liu and Haigang Sui
Remote Sens. 2023, 15(21), 5232; https://doi.org/10.3390/rs15215232 - 3 Nov 2023
Cited by 11 | Viewed by 2704
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Remote Sensing of Renewable Energy)
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23 pages, 14369 KiB  
Article
A Downscaling Methodology for Extracting Photovoltaic Plants with Remote Sensing Data: From Feature Optimized Random Forest to Improved HRNet
by Yinda Wang, Danlu Cai, Luanjie Chen, Lina Yang, Xingtong Ge and Ling Peng
Remote Sens. 2023, 15(20), 4931; https://doi.org/10.3390/rs15204931 - 12 Oct 2023
Cited by 7 | Viewed by 1835
Abstract
Present approaches in PV (Photovoltaic) detection are known to be scalable to a larger area using machine learning classification and have improved accuracy on a regional scale with deep learning diagnostics. However, it may cause false detection, time, and cost-consuming when regional deep [...] Read more.
Present approaches in PV (Photovoltaic) detection are known to be scalable to a larger area using machine learning classification and have improved accuracy on a regional scale with deep learning diagnostics. However, it may cause false detection, time, and cost-consuming when regional deep learning models are directly scaled to a larger area, particularly in large-scale, highly urbanized areas. Thus, a novel two-step downscaling methodology integrating machine learning broad spatial partitioning (step-1) and detailed deep learning diagnostics (step-2) is designed and applied in highly urbanized Jiangsu Province, China. In the first step, this methodology selects suitable feature combinations using the recursive feature elimination with distance correlation coefficient (RFEDCC) strategy for the random forest (RF), considering not only feature importance but also feature independence. The results from RF (overall accuracy = 95.52%, Kappa = 0.91) indicate clear boundaries and little noise. Furthermore, the post-processing of noise removal with a morphological opening operation for the extraction result of RF is necessary for the purpose that less high-resolution remote sensing tiles should be applied in the second step. In the second step, tiles intersecting with the results of the first step are selected from a vast collection of Google Earth tiles, reducing the computational complexity of the next step in deep learning. Then, the improved HRNet with high performance on the test data set (Intersection over Union around 94.08%) is used to extract PV plants from the selected tiles, and the results are mapped. In general, for Jiangsu province, the detection rate of the previous PV database is higher than 92%, and this methodology reduces false detection noise and time consumption (around 95%) compared with a direct deep learning methodology. Full article
(This article belongs to the Special Issue Remote Sensing of Renewable Energy)
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27 pages, 8103 KiB  
Article
An Empirical Correction Model for Remote Sensing Data of Global Horizontal Irradiance in High-Cloudiness-Index Locations
by Martín Muñoz-Salcedo, Fernando Peci-López and Francisco Táboas
Remote Sens. 2022, 14(21), 5496; https://doi.org/10.3390/rs14215496 - 31 Oct 2022
Cited by 4 | Viewed by 4167
Abstract
Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to [...] Read more.
Facing the energy transition, solar energy, whether thermal or electric, is currently one of the most viable alternatives, due to its technological maturity and its ease of operation and maintenance compared to other renewable energies. However, before its implementation, it is necessary to assess its potential. Remote sensing represents one of the low-cost solutions for solar energy assessment. Nevertheless, cloud cover is a main problem when validating the data. This study identifies satellite GHI profiles that cannot be used in energy production simulation. The validation is performed using parametric and non-parametric statistical tests. From the profile identified as invalid for simulation purposes, a site-adaptation methodology is proposed based on statistical learning using the machine learning algorithms “Best subset selection” and “Forward Stepwise Selection”. Linear and non-linear heuristic models are also proposed. The final AS7 model is selected through RMSE, MBE and adjusted R2 indicators and is valid for any sky condition. The results show an increase in R2 from 0.607 to 0.876. Full article
(This article belongs to the Special Issue Remote Sensing of Renewable Energy)
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