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Assessment of Solar Energy Based on Remote Sensing Data

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2188

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Special Issue Editors

Dr. Panagiotis Kosmopoulos

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Guest Editor

Institute for Environmental Research and Sustainable Development, National Observatory of Athens, 15236 Penteli, Greece
Interests: renewable energy; environmental studies; computer science; earth observations; artificial intelligence; numerical models; smart cities; digital twins
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Fernando Ramos Martins

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Institute of Marine Science, Federal University of São Paulo (IMar/UNIFESP), Santos 11070-102, SP, Brazil
Interests: renewable energy assessment and forecasting; energy transition; earth system science; climate change; numerical modeling; data science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In 14 and a half seconds, the sun provides as much energy to Earth as its population uses daily. However, assessing solar energy potential and performance require accurate and reliable data on the solar resource and the environmental conditions, which can be used to evaluate solar energy variability and availability and optimize the design and operation of solar energy systems. Remote sensing data can support this direction in the planning and managing of solar energy production. Planning can facilitate the transition to green energy, the integration of solar energy into the power grid, and the development of solar energy policies and markets. At the same time, efficient management ensures energy security based on renewables.

This Special Issue will showcase the overall improvement in research and developments in remote sensing data, modelling approaches, and techniques for solar radiation and energy assessment, nowcasting, and forecasting from rooftop photovoltaic installations in urban environments to big solar farms on regional or even global scales. Such solutions can actively support energy producers and electricity transmission and distribution system operators, promoting and supporting sustainable development as well as affordable and modern energy for all citizens.

Dr. Panagiotis Kosmopoulos
Prof. Dr. Fernando Ramos Martins
Guest Editors

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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

solar energy nowcasting and forecasting
solar power planning and management
radiative transfer modelling
concentrated solar power
aerosol and cloud effect
surface solar irradiation
solar resource mapping
solar cadasters
photovoltaics

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Published Papers (2 papers)

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Research

30 pages, 2856 KiB

Open AccessArticle

Improved Surface Solar Irradiation Estimation Using Satellite Data and Feature Engineering

by Jinyong Kim, Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Baekcheon Kim and Sungshin Kim

Remote Sens. 2025, 17(1), 65; https://doi.org/10.3390/rs17010065 - 27 Dec 2024

Viewed by 483

Abstract

Planning an optimal installation site to maximize power-generation efficiency is crucial for the effective operation of photovoltaic power plants. Achieving this requires accurate, reliable information on solar irradiation across different regions. However, ground-based measurements using pyranometers are resource-intensive, requiring substantial time and human effort, and their measurement range is limited, complicating data collection. To address this, we propose a method to accurately estimate surface solar irradiation (SSI) using satellite data and feature engineering. By leveraging satellite data as the primary input, we overcome the spatial and temporal limitations of ground-based measurements. Additionally, we improve SSI-estimation performance through designed features based on the geometric information of the Sun and satellite. A hybrid deep neural network model is used for SSI estimation, effectively handling data of varying dimensions. Hourly SSI data from 12 synoptic observation stations collected over one year, excluded from the model’s training and validation sets, are utilized to evaluate the proposed method. Experimental results demonstrate strong SSI-estimation performance, with an average root mean square error (RMSE) of 0.1813 MJ/m², a relative RMSE of 0.1601, mean absolute error of 0.1159 MJ/m², and coefficient of determination of 0.9680. Full article

(This article belongs to the Special Issue Assessment of Solar Energy Based on Remote Sensing Data)

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18 pages, 4496 KiB

Open AccessArticle

Estimation of Aerosol Characteristics from Broadband Solar Radiation Measurements Carried Out in Southern Algeria

by Mohamed Zaiani, Abdanour Irbah and Julien Delanoë

Remote Sens. 2024, 16(18), 3365; https://doi.org/10.3390/rs16183365 - 10 Sep 2024

Viewed by 1038

Abstract

Aerosols in the atmosphere significantly reduce the solar radiation reaching the Earth’s surface through scattering and absorption processes. Knowing their properties becomes essential when we are interested in measuring solar radiation at a given location on the ground. The commonly used parameters that [...] Read more.

τ

, the Angstrom exponent

α

, and the Angstrom coefficient

β

. One method for estimating these parameters is to fit ground-based measurements of clear-sky direct solar radiation using a model on which it depends. However, the choice of model depends on its suitability to the atmospheric conditions of the site considered. Eleven empirical solar radiation models depending on

α

and

β

were thus chosen and tested with solar radiation measurements recorded between 2005 and 2014 in Tamanrasset in southern Algeria. The results obtained were compared to measurements made with the AERONET solar photometer on the same site during the same period. Among the 11 models chosen, the best performing ones are REST2 and CPCR2. They proved to be the best suited to estimate

β

with approximately the same RMSE of 0.05 and a correlation coefficient R with respect to AERONET of 0.95. The results also highlighted good performances of these models for the estimation of

τ

with an

R M S E

of 0.05 and 0.04, and an R of 0.95 and 0.96, respectively. The values of

α

obtained from the fitting of these models were, however, less good, with R around 0.38. Additional treatments based on a Recurrent Neural Network (RNN) were necessary to improve its estimation. They provided promising results showing a significant improvement in

α

estimates with R reaching 0.7 when referring to AERONET data. Furthermore, this parameter made it possible to identify different types of aerosols in Tamanrasset such as the presence of maritime, dust, and mixed aerosols representing, respectively, 31.21%, 3.25%, and 65.54%, proportions calculated over the entire period studied. The seasonal analysis showed that maritime aerosols are predominant in the winter in Tamanrasset but decrease with the seasons to reach a minimum in the summer (JJA). Dust aerosols appear in February and persist mainly in the spring (MAM) and summer (JJA), then disappear in September. These results are also consistent with those obtained from AERONET. Full article