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GIS and Remote Sensing for Renewable Energy Assessment and Maps

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (25 July 2021) | Viewed by 45507

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Special Issue Editors

Dr. Benedetto Nastasi

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Guest Editor

Department of Planning, Design & Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, Italy
Interests: building physics; building services engineering; building simulation; renewable energy technologies; indoor environmental quality; open data & energy analytics; energy efficiency; zero energy buildings; power-to-X solutions; buildings, district and national energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Meysam Majidi Nezhad

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Guest Editor

Department of Astronautical, Electrical & Energy Engineering, Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy
Interests: satellite remote sensing techniques; environmental sciences; sustainable development; environmental parameters analysis; renewable energy sources potential assessment and mapping; re-analysis and large scale dataset; GIS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geographic information systems and remote sensing techniques are tools which are largely applied to the energy field. The assessment of the potential of renewable energy sources (RES) is one of the key steps in planning high-penetration renewable energy systems. To do so, several different methods can be used, such as in situ measurements (cup anemometers and buoys), satellite image data, on-site remote sensing tools (e.g., LIDAR and SODAR), and reanalysis datasets (e.g., ECMWF and MERRA).

This Special Issue aims at providing the state-of-the-art on all of the aforementioned tools in different energy applications and at different scales, i.e., urban, regional, national, and even continental for renewable scenarios planning and policy making.

For all the aforementioned reasons, we encourage researchers to share their original works in the field of GIS and remote sensing for renewable energy assessment and maps.

Topics of primary interest include but are not limited to:

Geospatial Information tools for renewable energy assessment;
Remote sensing techniques and tools;
Reanalysis datasets and platform;
Spatial planning for Sustainable Development Goals;
Mapping and assessing RES potential;
Urban energy tools;
Best practices and case studies.

Dr. Benedetto Nastasi
Dr. Meysam Majidi Nezhad
Guest Editors

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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. Energies 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 2600 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

georeferenced data
satellite maps
large scale dataset
renewable energy observatory
decision support tools

Published Papers (14 papers)

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Editorial

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3 pages, 174 KiB

Open AccessEditorial

GIS and Remote Sensing for Renewable Energy Assessment and Maps

by Benedetto Nastasi and Meysam Majidi Nezhad

Energies 2022, 15(1), 14; https://doi.org/10.3390/en15010014 - 21 Dec 2021

Cited by 2 | Viewed by 2670

Abstract

Geographic Information Systems (GIS) and Remote Sensing (RS) techniques are of great interest for the renewable energy field [...] Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

Research

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19 pages, 4131 KiB

Open AccessArticle

Computing and Assessment of Discrete Angle Positions for Optimizing the Solar Energy Harvesting for Urban Sustainable Development

by Guillermo Quiroga-Ocaña, Julio C. Montaño-Moreno, Enrique A. Enríquez-Velásquez, Victor H. Benitez, Luis C. Félix-Herrán, Jorge de-J. Lozoya-Santos and Ricardo A. Ramírez-Mendoza

Energies 2021, 14(20), 6441; https://doi.org/10.3390/en14206441 - 09 Oct 2021

Cited by 1 | Viewed by 2029

Abstract

This paper proposes the computation and assessment of optimal tilt and azimuth angles for a receiving surface, using a mathematical model developed at the University of Tomsk, Russia. The model was validated and analyzed for the Nuevo León State, Northeast Mexico, utilizing a set of metrics, comparing against satellite data from NASA. A point of interest in the city of Monterrey was analyzed to identify orientation patterns throughout the year for an optimal solar energy gathering. The aim is providing the best orientation tilt angles for photovoltaic or solar thermal panels without tracking systems. In addition, this analysis is proposed as a tool to achieve optimal performance in sustainable urban development in the region. Based on the findings, a set of optimal tilt and azimuth surface angles are proposed for the analyzed coordinates. The aim is to identify the optimal performance to obtain the maximum solar irradiation possible over the year for solar projects in the region. The results show that the model can be used as a tool to accelerate decision making in the design of solar harvesting surfaces and allows the design of discrete tracking systems with an increase in solar energy harvesting above 5% annually. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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37 pages, 17011 KiB

Open AccessArticle

Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

by Fausto André Valenzuela-Domínguez, Luis Alfonso Santa Cruz, Enrique A. Enríquez-Velásquez, Luis C. Félix-Herrán, Victor H. Benitez, Jorge de-J. Lozoya-Santos and Ricardo A. Ramírez-Mendoza

Energies 2021, 14(19), 6427; https://doi.org/10.3390/en14196427 - 08 Oct 2021

Cited by 3 | Viewed by 2168

Abstract

The estimation of the solar resource on certain surfaces of the planet is a key factor in deciding where to establish solar energy collection systems. This research uses a mathematical model based on easy-access geographic and meteorological information to calculate total solar radiation at ground surface. This information is used to create a GIS analysis of the State of Nuevo León in Mexico and identify solar energy opportunities in the territory. The analyzed area was divided into a grid and the coordinates of each corner are used to feed the mathematical model. The obtained results were validated with statistical analyses and satellite-based estimations from the National Aeronautics and Space Administration (NASA). The applied approach and the results may be replicated to estimate solar radiation in other regions of the planet without requiring readings from on-site meteorological stations and therefore reducing the cost of decision-making regarding where to place the solar energy collection equipment. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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15 pages, 2941 KiB

Open AccessArticle

Evaluation of Air Quality Index by Spatial Analysis Depending on Vehicle Traffic during the COVID-19 Outbreak in Turkey

by Kadir Diler Alemdar, Ömer Kaya, Antonino Canale, Muhammed Yasin Çodur and Tiziana Campisi

Energies 2021, 14(18), 5729; https://doi.org/10.3390/en14185729 - 11 Sep 2021

Cited by 15 | Viewed by 2415

Abstract

As in other countries of the world, the Turkish government is implementing many preventive partial and total lockdown practices against the virus’s infectious effect. When the first virus case has been detected, the public authorities have taken some restriction to reduce people and traffic mobility, which has also turned into some positive affect in air quality. To this end, the paper aims to examine how this pandemic affects traffic mobility and air quality in Istanbul. The pandemic does not only have a human health impact. This study also investigates the social and environmental effects. In our analysis, we observe, visualize, compare and discuss the impact of the post- and pre-lockdown on Istanbul’s traffic mobility and air quality. To do so, a geographic information system (GIS)-based approach is proposed. Various spatial analyses are performed in GIS with the statistical data used; thus, the environmental effects of the pandemic can be better observed. We test the hypothesis that this has reduced traffic mobility and improved air quality using traffic density cluster set and air monitoring stations (five air pollutant parameters) data for five months. The results shows that there are positive changes in terms of both traffic mobility and air quality, especially in April–May. PM₁₀, SO₂, CO, NO₂ and NO_x parameter values improved by 21.21%, 16.55%, 18.82%, 28.62% and 39.99%, respectively. In addition, there was a 7% increase in the average traffic speed. In order for the changes to be permanent, it is recommended to integrate e-mobility and sharing systems into the current transportation network. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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17 pages, 12068 KiB

Open AccessArticle

Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks

by Salah Vaisi, Saleh Mohammadi and Kyoumars Habibi

Energies 2021, 14(17), 5462; https://doi.org/10.3390/en14175462 - 02 Sep 2021

Cited by 5 | Viewed by 2335

Abstract

District heating (DH) has a major potential to increase the efficiency, security, and sustainability of energy management at the community scale. However, there is a huge challenge for decision makers due to the lack of knowledge about thermal energy demand during a year. Thermal energy demand is strongly dependent on the outdoor temperature, building area, and activities. In this context, this paper presents an innovative monthly thermal energy mapping method to calculate and visualize heat demand accurately for various types of buildings. The method includes three consecutive phases: (i) calculating energy loss, (ii) completing a dataset that includes energy and building information, and (iii) generating the monthly heat demand maps for the community. Determining the amount of demand and the best location for energy generators from the perspective of energy efficiency in a DH system in an urban context is one of the important applications of heat maps. Exploring heat demand characteristics and visualizing them on maps is the foundation of smart DHs. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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13 pages, 3155 KiB

Open AccessArticle

A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data

by Azim Heydari, Meysam Majidi Nezhad, Mehdi Neshat, Davide Astiaso Garcia, Farshid Keynia, Livio De Santoli and Lina Bertling Tjernberg

Energies 2021, 14(12), 3459; https://doi.org/10.3390/en14123459 - 11 Jun 2021

Cited by 19 | Viewed by 2869

Abstract

A cost-effective and efficient wind energy production trend leads to larger wind turbine generators and drive for more advanced forecast models to increase their accuracy. This paper proposes a combined forecasting model that consists of empirical mode decomposition, fuzzy group method of data handling neural network, and grey wolf optimization algorithm. A combined K-means and identifying density-based local outliers is applied to detect and clean the outliers of the raw supervisory control and data acquisition data in the proposed forecasting model. Moreover, the empirical mode decomposition is employed to decompose signals and pre-processing data. The fuzzy GMDH neural network is a forecaster engine to estimate the future amount of wind turbines energy production, where the grey wolf optimization is used to optimize the fuzzy GMDH neural network parameters in order to achieve a lower forecasting error. Moreover, the model has been applied using actual data from a pilot onshore wind farm in Sweden. The obtained results indicate that the proposed model has a higher accuracy than others in the literature and provides single and combined forecasting models in different time-steps ahead and seasons. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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16 pages, 2618 KiB

Open AccessArticle

Wind Climate and Wind Power Resource Assessment Based on Gridded Scatterometer Data: A Thracian Sea Case Study

by Nikolaos Kokkos, Maria Zoidou, Konstantinos Zachopoulos, Meysam Majidi Nezhad, Davide Astiaso Garcia and Georgios Sylaios

Energies 2021, 14(12), 3448; https://doi.org/10.3390/en14123448 - 10 Jun 2021

Cited by 12 | Viewed by 2756

Abstract

The present analysis utilized the 6-hourly data of wind speed (zonal and meridional) for the period between 2011 and 2019, as retrieved from the Copernicus Marine Environmental Service (CMEMS), covering the Thracian Sea (the northern part of the Aegean Sea). Data were estimated from the global wind fields derived from the Advanced Scatterometer (ASCAT) L2b scatterometer on-board Meteorological Operational (METOP) satellites, and then processed towards the equivalent neutral-stability 10 m winds with a spatial resolution of 0.25° × 0.25°. The analysis involved: (a) descriptive statistics on wind speed and direction data; (b) frequency distributions of daily-mean wind speeds per wind direction sector; (c) total wind energy content assessment per wind speed increment and per sector; (d) total annual wind energy production (in MWh/yr); and (e) wind power density, probability density function, and Weibull wind speed distribution, together with the relevant dimensionless shape and scale parameters. Our results show that the Lemnos Plateau has the highest total wind energy content (4455 kWh/m²/yr). At the same time, the area to the SW of the Dardanelles exhibits the highest wind energy capacity factor (~37.44%), producing 7546 MWh/yr. This indicates that this zone could harvest wind energy through wind turbines, having an efficiency in energy production of 37%. Lower capacity factors of 24–28% were computed at the nearshore Thracian Sea zone, producing between 3000 and 5600 MWh/yr. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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21 pages, 10360 KiB

Open AccessArticle

SHP Assessment for a Run-of-River (RoR) Scheme Using a Rectangular Mesh Sweeping Approach (MSA) Based on GIS

by Gerardo Alcalá, Luis Fernando Grisales-Noreña, Quetzalcoatl Hernandez-Escobedo, Jose Javier Muñoz-Criollo and J. D. Revuelta-Acosta

Energies 2021, 14(11), 3095; https://doi.org/10.3390/en14113095 - 26 May 2021

Cited by 7 | Viewed by 2956

Abstract

This work proposed a base method for automated assessment of Small Hydro-Power (SHP) potential for a run-of-river (RoR) scheme using geographic information systems (GIS). The hydro-power potential (HP) was represented through a comprehensive methodology consisting of a structured raster database. A calibrated and validated hydrological model (Soil and Water Assessment Tool—SWAT) was used to estimate monthly streamflow as the Mesh Sweeping Approach (MSA) driver. The methodology was applied for the upper part of the Huazuntlan River Watershed in Los Tuxtlas Mountains, Mexico. The MSA divided the study area into a rectangular mesh. Then, at every location within the mesh, SHP was obtained. The main components of the MSA as a RoR scheme were the intake, the powerhouse, and the surge tank. The surge tank was located at cells where the hydro-power was calculated and used as a reference to later locate the intake and powerhouse by maximizing the discharge and head. SHP calculation was performed by sweeping under different values of the penstock’s length, and the headrace’s length. The maximum permissible lengths for these two variables represented potential hydro-power generation locations. Results showed that the headrace’s length represented the major contribution for hydro-power potential estimation. Additionally, values of 2000 m and 1500 m for the penstock and the headrace were considered potential thresholds as there is no significant increment in hydro-power after increasing any of these values. The availability of hydro-power on a raster representation has advantages for further hydro-power data analysis and processing. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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15 pages, 3807 KiB

Open AccessArticle

Remote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation

by Marcus Vinícius Coelho Vieira da Costa, Osmar Luiz Ferreira de Carvalho, Alex Gois Orlandi, Issao Hirata, Anesmar Olino de Albuquerque, Felipe Vilarinho e Silva, Renato Fontes Guimarães, Roberto Arnaldo Trancoso Gomes and Osmar Abílio de Carvalho Júnior

Energies 2021, 14(10), 2960; https://doi.org/10.3390/en14102960 - 20 May 2021

Cited by 37 | Viewed by 4539

Abstract

Brazil is a tropical country with continental dimensions and abundant solar resources that are still underutilized. However, solar energy is one of the most promising renewable sources in the country. The proper inspection of Photovoltaic (PV) solar plants is an issue of great interest for the Brazilian territory’s energy management agency, and advances in computer vision and deep learning allow automatic, periodic, and low-cost monitoring. The present research aims to identify PV solar plants in Brazil using semantic segmentation and a mosaicking approach for large image classification. We compared four architectures (U-net, DeepLabv3+, Pyramid Scene Parsing Network, and Feature Pyramid Network) with four backbones (Efficient-net-b0, Efficient-net-b7, ResNet-50, and ResNet-101). For mosaicking, we evaluated a sliding window with overlapping pixels using different stride values (8, 16, 32, 64, 128, and 256). We found that: (1) the models presented similar results, showing that the most relevant approach is to acquire high-quality labels rather than models in many scenarios; (2) U-net presented slightly better metrics, and the best configuration was U-net with the Efficient-net-b7 encoder (98% overall accuracy, 91% IoU, and 95% F-score); (3) mosaicking progressively increases results (precision-recall and receiver operating characteristic area under the curve) when decreasing the stride value, at the cost of a higher computational cost. The high trends of solar energy growth in Brazil require rapid mapping, and the proposed study provides a promising approach. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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16 pages, 6162 KiB

Open AccessArticle

Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands

by Bala Bhavya Kausika and Wilfried G. J. H. M. van Sark

Energies 2021, 14(7), 1865; https://doi.org/10.3390/en14071865 - 27 Mar 2021

Cited by 21 | Viewed by 3684

Abstract

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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11 pages, 485 KiB

Open AccessArticle

Life-Cycle Land-Use Requirement for PV in Vietnam

by Eleonora Riva Sanseverino, Maurizio Cellura, Le Quyen Luu, Maria Anna Cusenza, Ninh Nguyen Quang and Nam Hoai Nguyen

Energies 2021, 14(4), 861; https://doi.org/10.3390/en14040861 - 07 Feb 2021

Cited by 3 | Viewed by 2258

Abstract

Over the last 15 years, photovoltaics (PV) in Vietnam has experienced development. The increased installed capacity of PV requires more land for installation sites as well as for manufacturing the plants’ component and waste treatment during the plants’ decommissioning. As a developing country, in which more than 80% of the population’s livelihood depends on agriculture, there are concerns about the competition of land for agriculture and solar development. This paper estimates the life-cycle land-use requirement for PV development in Vietnam, to provide the scientific-based evidence for policy makers on the quantity of land required, so that the land budget can be suitably allocated. The direct land-use requirement for PV ranges from 3.7 to 6.7 m² MWh⁻¹ year, and the total fenced area is 7.18 to 8.16 m² MWh⁻¹ year. Regarding the life-cycle land use, the land occupation is 241.85 m²a and land transformation is 16.17 m² per MWh. Most of the required land area is for the installation of the PV infrastructure, while the indirect land use of the background process is inconsiderable. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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23 pages, 20071 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Sustainable Spatial Energy Planning of Large-Scale Wind and PV Farms in Israel: A Collaborative and Participatory Planning Approach

by Sofia Spyridonidou, Georgia Sismani, Eva Loukogeorgaki, Dimitra G. Vagiona, Hagit Ulanovsky and Daniel Madar

Energies 2021, 14(3), 551; https://doi.org/10.3390/en14030551 - 21 Jan 2021

Cited by 39 | Viewed by 4294

Abstract

In this work, an innovative sustainable spatial energy planning framework is developed on national scale for identifying and prioritizing appropriate, technically and economically feasible, environmentally sustainable as well as socially acceptable sites for the siting of large-scale onshore Wind Farms (WFs) and Photovoltaic Farms (PVFs) in Israel. The proposed holistic framework consists of distinctive steps allocated in two successive modules (the Planning and the Field Investigation module), and it covers all relevant dimensions of a sustainable siting analysis (economic, social, and environmental). It advances a collaborative and participatory planning approach by combining spatial planning tools (Geographic Information Systems (GIS)) and multi-criteria decision-making methods (e.g., Analytical Hierarchy Process (AHP)) with versatile participatory planning techniques in order to consider the opinion of three different participatory groups (public, experts, and renewable energy planners) within the site-selection processes. Moreover, it facilitates verification of GIS results by conducting appropriate field observations. Sites of high suitability, accepted by all participatory groups and field verified, form the final outcome of the proposed framework. The results illustrate the existence of high suitable sites for large-scale WFs’ and PVFs’ siting and, thus, the potential deployment of such projects towards the fulfillment of the Israeli energy targets in the near future. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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19 pages, 4626 KiB

Open AccessFeature PaperArticle

Combined Multi-Layer Feature Fusion and Edge Detection Method for Distributed Photovoltaic Power Station Identification

by Yongshi Jie, Xianhua Ji, Anzhi Yue, Jingbo Chen, Yupeng Deng, Jing Chen and Yi Zhang

Energies 2020, 13(24), 6742; https://doi.org/10.3390/en13246742 - 21 Dec 2020

Cited by 31 | Viewed by 3176

Abstract

Distributed photovoltaic power stations are an effective way to develop and utilize solar energy resources. Using high-resolution remote sensing images to obtain the locations, distribution, and areas of distributed photovoltaic power stations over a large region is important to energy companies, government departments, and investors. In this paper, a deep convolutional neural network was used to extract distributed photovoltaic power stations from high-resolution remote sensing images automatically, accurately, and efficiently. Based on a semantic segmentation model with an encoder-decoder structure, a gated fusion module was introduced to address the problem that small photovoltaic panels are difficult to identify. Further, to solve the problems of blurred edges in the segmentation results and that adjacent photovoltaic panels can easily be adhered, this work combines an edge detection network and a semantic segmentation network for multi-task learning to extract the boundaries of photovoltaic panels in a refined manner. Comparative experiments conducted on the Duke California Solar Array data set and a self-constructed Shanghai Distributed Photovoltaic Power Station data set show that, compared with SegNet, LinkNet, UNet, and FPN, the proposed method obtained the highest identification accuracy on both data sets, and its F1-scores reached 84.79% and 94.03%, respectively. These results indicate that effectively combining multi-layer features with a gated fusion module and introducing an edge detection network to refine the segmentation improves the accuracy of distributed photovoltaic power station identification. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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Review

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20 pages, 6182 KiB

Open AccessReview

Tides and Tidal Currents—Guidelines for Site and Energy Resource Assessment

by Silvio Barbarelli and Benedetto Nastasi

Energies 2021, 14(19), 6123; https://doi.org/10.3390/en14196123 - 26 Sep 2021

Cited by 11 | Viewed by 3417

Abstract

The main aim of this paper was to classify and to analyze the expeditious resource assessment procedure to help energy planners and system designers dealing with tides and tidal currents. Depending on the geographical features of the site to be evaluated, this paper reported the easiest methods to adopt for later working plans, crucial for preliminary considerations but to be supported by in situ measurements and by a more complex and detailed modelling. While tide trends are predictable by using Laplace equations and Fourier series, tidal currents velocities prediction is not easy, requiring suitable methods or hydraulic applications. Natural and artificial sites were analyzed and the best method for each type of them was presented. The latter together highlighting the minimum set of required information was discussed and provided as a toolkit for assessing tides and tidal current energy potential. Full article

(This article belongs to the Special Issue GIS and Remote Sensing for Renewable Energy Assessment and Maps)

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