Special Issue "Remote Sensing for Smart Renewable Cities"

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

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editor

Dr. Panagiotis Kosmopoulos
E-Mail Website
Guest Editor
Institute for Environmental Research and Sustainable Development & Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, 15236 Penteli, Greece
Interests: sustainable development; renewable energy; environmental research; earth observation; atmospheric impacts on solar irradiance and human health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As cities around the world strive to become smart, renewable solar and wind power can play a crucial role in helping them provide reliable, affordable, and environmentally responsible energy and hence, reducing climate change and air pollution. A smart renewable city is characterized by efficient and sustainable planning and management of its renewable energy resources for economic growth and overall citizens’ quality of life. New technological options such as photovoltaics (PV), wind turbines, passive buildings, bio-methane injection into grids, small-scale combined heat and power with storage and PV-driven heat pumps alter common strategies to supply larger settlements. Especially, rooftop PV systems are able to directly produce electricity and add value to urban roofs, reducing also the heat island effect.

This Special Issue will highlight modern remote sensing and modeling approaches that are able to assess urban renewable solutions adaptability, in terms of optimum and viable ways for shifting from current cities to smart renewable cities. Therefore, there is a need for remote sensing tools for urban energy decision-makers, distribution and smart grid system operators, solar and wind energy use and trading. These tools and supporting solutions are meant to provide urban-scale remote sensing and modeling renewable energy management and planning in terms of continuous monitoring and forecasting the spatiotemporal variability of energy production and consumption. We invite you to submit articles on the following topics:

  • Roof-top PV installations
  • Complex shadowing effects
  • Energy management systems
  • Energy planning and mapping
  • Smart grids in renewable cities
  • Building and urban aerodynamics
  • Atmospheric and meteorological effects
  • Penetration of renewables in urban scale
  • Remote sensing energy data and solutions

Dr. Panagiotis Kosmopoulos
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

  • Smart grids
  • Aerodynamics
  • Solar radiation
  • Renewable energy
  • Urban environment
  • Electricity production
  • Roof-top photovoltaic

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Impact of Aerosol and Cloud on the Solar Energy Potential over the Central Gangetic Himalayan Region
Remote Sens. 2021, 13(16), 3248; https://doi.org/10.3390/rs13163248 - 17 Aug 2021
Viewed by 1197
Abstract
We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of [...] Read more.
We examine the impact of atmospheric aerosols and clouds on the surface solar radiation and solar energy at Nainital, a high-altitude remote location in the central Gangetic Himalayan region (CGHR). For this purpose, we exploited the synergy of remote-sensed data in terms of ground-based AERONET Sun Photometer and satellite observations from the MODerate Resolution Imaging Spectroradiometer (MODIS) and the Meteosat Second Generation (MSG), with radiative transfer model (RTM) simulations and 1 day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Clouds and aerosols are one of the most common sources of solar irradiance attenuation and hence causing performance issues in the photovoltaic (PV) and concentrated solar power (CSP) plant installations. The outputs of RTM results presented with high accuracy under clear, cloudy sky and dust conditions for global horizontal (GHI) and beam horizontal irradiance (BHI). On an annual basis the total aerosol attenuation was found to be up to 105 kWh m−2 for the GHI and 266 kWh m−2 for BHI, respectively, while the cloud effect is much stronger with an attenuation of 245 and 271 kWh m−2 on GHI and BHI. The results of this study will support the Indian solar energy producers and electricity handling entities in order to quantify the energy and financial losses due to cloud and aerosol presence. Full article
(This article belongs to the Special Issue Remote Sensing for Smart Renewable Cities)
Show Figures

Figure 1

Article
A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation
Remote Sens. 2021, 13(13), 2605; https://doi.org/10.3390/rs13132605 - 02 Jul 2021
Viewed by 744
Abstract
Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar [...] Read more.
Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model. Full article
(This article belongs to the Special Issue Remote Sensing for Smart Renewable Cities)
Show Figures

Graphical abstract

Article
Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques
Remote Sens. 2020, 12(12), 1921; https://doi.org/10.3390/rs12121921 - 14 Jun 2020
Cited by 2 | Viewed by 1302
Abstract
This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. [...] Read more.
This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations. Full article
(This article belongs to the Special Issue Remote Sensing for Smart Renewable Cities)
Show Figures

Graphical abstract

Back to TopTop