Special Issue "Renewable Energy Mapping"

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

Deadline for manuscript submissions: closed (1 October 2021).

Special Issue Editors

Dr. Susana Lagüela López
E-Mail Website
Guest Editor
Cartographic and Land Engineering Department, Higher Polytechnic School of Avila, University of Salamanca, Hornos Caleros, 50 05003 Avila, Spain
Interests: infrared thermography; laser scanning; ground-penetrating radar; 3D modeling; renewable energy; civil and environmental engineering; geographic information systems
Special Issues and Collections in MDPI journals
Dr. Massimo Menenti
E-Mail Website
Guest Editor
Department of Geosciennce and Remote Sensing, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands
Interests: earth observation; land surface processes; hydrology; water management; optical and laser remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The concern about energy use has been increasing in the last decades, covering areas from reducing the energy consumption to the use of energy from renewable resources. For all cases, urban areas are the major zones of interest for the study, due to their nature of big energy consumer, for industrial, working and residential activities.

Given the scale of the areas under study (cities, neighborhoods) and the variety of data required for the evaluation of the energy situation regarding both consumption and generation (temperature values, atmospheric data, geometry), satellite acquired data is essential for the resolution of all the questions of interest. From imagery to punctual sensor and radar data, the space and aerial points of view are the main focus.

This special issue is mainly dedicated to publishing a selection of papers that provides a comprehensive and up-to-date overview of the state of the art of research activities dealing with the use of remote sensing data (space and aerial) for the analysis of the energy problem. We invite you to submit articles on the following topics:

- Urban energy consumption (residential, transportation)

- Urban energy potential of renewable resources

- Urban heat island

- Novel data processing strategies for the characterization of the scene with energy purposes

- Novel combinations of data types for energy balance

- Novel methodologies to analyze the combined exploitation of renewable energy resources

Dr. Susana Laguela
Dr. Massimo Menenti

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 papers will be 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 2400 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

  • energy
  • radiation
  • satellite imagery
  • geometry
  • heat transfer
  • energy balance

Published Papers (5 papers)

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Research

Article
Influence of LiDAR Point Cloud Density in the Geometric Characterization of Rooftops for Solar Photovoltaic Studies in Cities
Remote Sens. 2020, 12(22), 3726; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223726 - 13 Nov 2020
Cited by 1 | Viewed by 751
Abstract
The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential [...] Read more.
The use of LiDAR (Light Detection and Ranging) data for the definition of the 3D geometry of roofs has been widely exploited in recent years for its posterior application in the field of solar energy. Point density in LiDAR data is an essential characteristic to be taken into account for the accurate estimation of roof geometry: area, orientation and slope. This paper presents a comparative study between LiDAR data of different point densities: 0.5, 1, 2 and 14 points/m2 for the measurement of the area of roofs of residential and industrial buildings. The data used for the study are the LiDAR data freely available by the Spanish Institute of Geography (IGN), which is offered according to the INSPIRE Directive. The results obtained show different behaviors for roofs with an area below and over 200 m2. While the use of low-density point clouds (0.5 point/m2) presents significant errors in the estimation of the area, the use of point clouds with higher density (1 or 2 points/m2) implies a great improvement in the area results, with no significant difference among them. The use of high-density point clouds (14 points/m2) also implies an improvement of the results, although the accuracy does not increase in the same ratio as the increase in density regarding 1 or 2 points/m2. Thus, the conclusion reached is that the geometrical characterization of roofs requires data acquisition with point density of 1 or 2 points/m2, and that higher point densities do not improve the results with the same intensity as they increase computation time. Full article
(This article belongs to the Special Issue Renewable Energy Mapping)
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Article
Automatic gbXML Modeling from LiDAR Data for Energy Studies
Remote Sens. 2020, 12(17), 2679; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172679 - 19 Aug 2020
Cited by 1 | Viewed by 1322
Abstract
This paper proposes an efficient and simplified procedure for the 3D modelling of buildings, based on the semi-automatic processing of point clouds acquired with mobile LiDAR scanners. The procedure is designed with the aim at generating BIM, in gbXML format, from the point [...] Read more.
This paper proposes an efficient and simplified procedure for the 3D modelling of buildings, based on the semi-automatic processing of point clouds acquired with mobile LiDAR scanners. The procedure is designed with the aim at generating BIM, in gbXML format, from the point clouds. In this way, the main application of the procedure is the performance of energy analysis, towards the increase of the energy efficiency in the construction sector, and its consequent contribution to the mitigation of the climate change. Thus, the main contribution of the methodology proposed is its easiness of use and its level of automation, which allow its utilization by users who are experts in the use of energy in buildings but non-experts on 3D modelling. The software provides a solution for the 3D modelling of complex point clouds of various millions of points in times of execution less than 10 minutes. The system is evaluated through its application to three different real-world scenarios and compared with manual modelling. Moreover, the results are used for an example of an energy application, proving their performance against manually elaborated models. Full article
(This article belongs to the Special Issue Renewable Energy Mapping)
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Article
Study on Geospatial Distribution of the Efficiency and Sustainability of Different Energy-Driven Heat Pumps Included in Low Enthalpy Geothermal Systems in Europe
Remote Sens. 2020, 12(7), 1093; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071093 - 29 Mar 2020
Cited by 5 | Viewed by 933
Abstract
This research work aims at a multinational study in Europe of the emissions and energy costs generated by the operation of low enthalpy geothermal systems, with heat pumps fed by different energy sources. From an economic point of view, natural gas and biogas [...] Read more.
This research work aims at a multinational study in Europe of the emissions and energy costs generated by the operation of low enthalpy geothermal systems, with heat pumps fed by different energy sources. From an economic point of view, natural gas and biogas prices are, usually, lower than electricity ones. So it may be advantageous to use these energy sources to feed the heat pumps instead of electricity. From the environmental point of view, it is intended to highlight the fact that under certain conditions of electricity production (electricity mix), more CO2 emissions are produced by electricity consumption than using other a priori less “clean” energy sources such as natural gas. To establish the countries where each of the different heat pumps may be more cost-efficient and environmentally friendly, data from multi-source geospatial databases have been collected and analyzed. The results show that in the majority of cases, the electric heat pump is the most recommendable solution. However, there are some geographic locations (such as Poland and Estonia), where the gas engine heat pump may be a better alternative. Full article
(This article belongs to the Special Issue Renewable Energy Mapping)
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Article
An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas
Remote Sens. 2020, 12(2), 259; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020259 - 11 Jan 2020
Cited by 19 | Viewed by 1638
Abstract
Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, [...] Read more.
Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Góra quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas. Full article
(This article belongs to the Special Issue Renewable Energy Mapping)
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Article
Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas
Remote Sens. 2020, 12(2), 254; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020254 - 10 Jan 2020
Cited by 8 | Viewed by 1867
Abstract
Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This [...] Read more.
Solar radiation ground data is available in poor spatial resolution, which provides an opportunity and demonstrates the necessity to consider solar irradiance modeling based on satellite data. For the first time, solar energy monitoring in near real-time has been performed for India. This study focused on the assessment of solar irradiance from the Indian Solar Irradiance Operational System (INSIOS) using operational cloud and aerosol data from INSAT-3D and Copernicus Atmosphere Monitoring Service (CAMS)-Monitoring Atmospheric Composition Climate (MACC), respectively. Simulations of the global horizontal irradiance (GHI) and direct normal irradiance (DNI) were evaluated for 1 year for India at four Baseline Surface Radiation Network (BSRN) stations located in urban regions. The INSIOS system outputs as per radiative transfer model results presented high accuracy under clear-sky and cloudy conditions for GHI and DNI. DNI was very sensitive to the presence of cloud and aerosols, where even with small optical depths the DNI became zero, and thus it affected the accuracy of simulations under realistic atmospheric conditions. The median BSRN and INSIOS difference was found to vary from −93 to −49 W/m2 for GHI and −103 to −76 W/m2 for DNI under high solar energy potential conditions. Clouds were able to cause an underestimation of 40%, whereas for various aerosol inputs to the model, the overall accuracy was high for both irradiances, with the coefficient of determination being 0.99, whereas the penetration of photovoltaic installation, which exploits GHI, into urban environments (e.g., rooftop) could be effectively supported by the presented methodology, as estimations were reliable during high solar energy potential conditions. The results showed substantially high errors for monsoon season due to increase in cloud coverage that was not well-predicted at satellite and model resolutions. Full article
(This article belongs to the Special Issue Renewable Energy Mapping)
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