Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing for Green Energy Development
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing for Green Energy Development

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

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 15846

Special Issue Editor

Dr. Xuan Zhu

E-Mail Website
Guest Editor
School of Earth Atmosphere and Environment, Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia
Interests: remote sensing of environment and natural resources; LiDAR remote sensing; UAV remote sensing; spatial analysis and modelling; spatial data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is embarking on the transition to green energy, which is renewable and clean and emits no or few greenhouse gases – a major cause of climate change. Green energy sources, such as solar, wind, hydro, and biomass, are often readily available and usually naturally replenished. Though it has been widely recognised that green energy will play a key role in the decarbonisation of our energy systems in the near future, the capital cost associated with their development can be high. Unlike coal and natural gas that are highly centralised sources of power, wind, solar, and biomass are decentralised, which means smaller generating stations spread across a large area and working together to provide power. It is necessary to conduct a comprehensive evaluation of site suitability before making green energy investments. Remote sensing has proven enormously valuable and highly effective and efficient for the assessment of green energy potential. This Special Issue aims to offer a collection of papers that represent the recent advances in the application of remote sensing in the development of green energy. Topics to be covered include, but are not limited to, the following:

  1. new capabilities of remote sensing technologies in green energy development,
  2. novel approaches and methodologies for the assessment of green energy resources and potentials, and
  3. the integration of remote sensing and GIS for site characterisation and site suitability assessment for green energy development.

Dr. Xuan Zhu
Guest Editor

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

  • green energy resources
  • green energy potentials
  • green energy development
  • site suitability assessment
  • site characterisation
  • GIS and remote sensing integration

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 9082 KiB  
Article
Solar Photovoltaic Hotspot Inspection Using Unmanned Aerial Vehicle Thermal Images at a Solar Field in South India
by Umesh Pruthviraj, Yashwant Kashyap, Effrosyni Baxevanaki and Panagiotis Kosmopoulos
Remote Sens. 2023, 15(7), 1914; https://doi.org/10.3390/rs15071914 - 02 Apr 2023
Cited by 4 | Viewed by 3781
Abstract
The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each [...] Read more.
The sun is an abundant source of energy, and solar energy has been at the forefront of the renewable energy sector for years. A way to convert it into electricity is by the use of solar cells. Multiple solar cells, connected to each other, create solar panels, which in their turn, are connected in a solar string, and they create solar farms. These structures are extremely efficient in electricity production, but also, cells are fragile in nature and delicate to environmental conditions, which is the reason why some of them show discrepancies and are called defective. In this research, a thermal camera mounted on a drone has been used for the first time in the solar farm operating conditions of India in order to capture images of the solar field and investigate solar panels for defective cells and create an orthomosaic image of the entire area. This procedure next year will be established on an international scale as a best practice example for commercialization, providing effortless photovoltaic monitoring and maintenance planning. For this process, an open source software WebODM has been used, and the entire field was digitized so as to identify the location of defective panels in the field. This software was the base in order to provide and analyze a digital twin of the studied area and the included photovoltaic panels. The defects on solar cells were identified with the use of thermal bands, which record and point out their temperature of them, whereas anomalies in the detected temperature in defective solar cells were captured using thermal electromagnetic waves, and these areas are mentioned as hotspots. In this research, a total number of 232.934 solar panels were identified, and 2481 defective solar panels were automatically indicated. The majority of the defects were due to manufacturing failure and normal aging, but also due to persistent shadowing and soiling from aerosols and especially dust transport, as well as from extreme weather conditions, including hail. The originality of this study relies on the application of the proposed under development technology to the specific conditions of India, including high photovoltaic panels wear rates due to extreme aerosol loads (India presents one of the highest aerosol levels worldwide) and the monsoon effects. The ability to autonomously monitor solar farms in such conditions has a strong energy and economic benefit for production management and for long-term optimization purposes. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
►▼ Show Figures

Figure 1

22 pages, 5120 KiB  
Article
Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery
by Zeyad Awwad, Abdulaziz Alharbi, Abdulelah H. Habib and Olivier L. de Weck
Remote Sens. 2023, 15(5), 1356; https://doi.org/10.3390/rs15051356 - 28 Feb 2023
Cited by 1 | Viewed by 1711
Abstract
With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to [...] Read more.
With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
►▼ Show Figures

Figure 1

26 pages, 7672 KiB  
Article
A Novel Deep Stack-Based Ensemble Learning Approach for Fault Detection and Classification in Photovoltaic Arrays
by Ehtisham Lodhi, Fei-Yue Wang, Gang Xiong, Lingjian Zhu, Tariku Sinshaw Tamir, Waheed Ur Rehman and M. Adil Khan
Remote Sens. 2023, 15(5), 1277; https://doi.org/10.3390/rs15051277 - 25 Feb 2023
Cited by 3 | Viewed by 1790
Abstract
The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV [...] Read more.
The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
►▼ Show Figures

Figure 1

21 pages, 7084 KiB  
Article
Estimation of Rooftop Solar Power Potential by Comparing Solar Radiation Data and Remote Sensing Data—A Case Study in Aichi, Japan
by Xiaoxun Huang, Kiichiro Hayashi, Toshiki Matsumoto, Linwei Tao, Yue Huang and Yuuki Tomino
Remote Sens. 2022, 14(7), 1742; https://doi.org/10.3390/rs14071742 - 05 Apr 2022
Cited by 13 | Viewed by 4499
Abstract
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost [...] Read more.
There have been significant advances in the shift from fossil-based energy systems to renewable energies in recent years. Decentralized solar photovoltaic (PV) is one of the most promising energy sources because of the availability of rooftop areas, ease of installation, and reduced cost of PV panels. The current modeling method using remote sensing data based on a geographic information system (GIS) is objective and accurate, but the analysis processes are complicated and time-consuming. In this study, we developed a method to estimate the rooftop solar power potential over a wide area using globally available solar radiation data from Solargis combined with a building polygon. Our study also utilized light detection and ranging (LiDAR) data and AW3D to estimate rooftop solar power potential in western Aichi, Japan, and the solar radiation was calculated using GIS. The estimation using LiDAR data took into account the slope and azimuth of rooftops. A regression analysis of the estimated solar power potential for each roof between the three methods was conducted, and the conversion factor 0.837 was obtained to improve the accuracy of the results from the Solargis data. The annual rooftop solar power potential of 3,351,960 buildings in Aichi Prefecture under Scenario A, B, and C was 6.92 × 107, 3.58 × 107, and 1.27 × 107 MWh/year, estimated using Solargis data after the adjustment. The estimated solar power potential under Scenario A could satisfy the total residential power demand in Aichi, revealing the crucial role of rooftop solar power in alleviating the energy crisis. This approach of combining Solargis data with building polygons can be easily applied in other parts of the world. These findings can provide useful information for policymakers and contribute to local planning for cleaner energy. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
►▼ Show Figures

Graphical abstract

21 pages, 8677 KiB  
Article
Solar Resource Potentials and Annual Capacity Factor Based on the Korean Solar Irradiance Datasets Derived by the Satellite Imagery from 1996 to 2019
by Chang Ki Kim, Hyun-Goo Kim, Yong-Heack Kang, Chang-Yeol Yun, Boyoung Kim and Jin Young Kim
Remote Sens. 2021, 13(17), 3422; https://doi.org/10.3390/rs13173422 - 28 Aug 2021
Cited by 7 | Viewed by 2178
Abstract
The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, [...] Read more.
The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019. During the investigation period, the monthly mean of daily total irradiance was in a good agreement with the in situ measurements at 18 ground stations; the mean absolute error is also normalized to 9.4%. It is observed that the irradiance estimates in the datasets have been gradually increasing at a rate of 0.019 kWh m−2 d−1 per year. The monthly variation in solar irradiance indicates that the meteorological conditions in the spring season dominate the annual solar insolation. In addition, the local distribution of solar irradiance is primarily affected by the geographical environment; higher solar insolation is observed in the southern part of Korea, but lower solar insolation is observed in the mountainous range in Korea. The annual capacity factor is the secondary output from the Korean solar irradiance datasets. The reliability of the estimate of this factor is proven by the high correlation coefficient of 0.912. Thus, in accordance with the results from the spatial distribution of solar irradiance, the southern part of Korea is an appropriate region for establishing solar power plants exhibiting a higher annual capacity factor than the other regions. Full article
(This article belongs to the Special Issue Remote Sensing for Green Energy Development)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop