GIS and Remote Sensing for Renewable Energy Assessment and Maps
This book 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.
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Other Authors: | , |
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
Basel
MDPI - Multidisciplinary Digital Publishing Institute
2022
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Subjects: | |
Online Access: | DOAB: download the publication DOAB: description of the publication |
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100 | 1 | |a Nastasi, Benedetto |4 edt | |
700 | 1 | |a Nezhad, Meysam Majidi |4 edt | |
700 | 1 | |a Nastasi, Benedetto |4 oth | |
700 | 1 | |a Nezhad, Meysam Majidi |4 oth | |
245 | 1 | 0 | |a GIS and Remote Sensing for Renewable Energy Assessment and Maps |
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300 | |a 1 electronic resource (244 p.) | ||
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338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This book 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. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by/4.0/ |2 cc |4 https://creativecommons.org/licenses/by/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a distributed photovoltaic power stations | ||
653 | |a remote sensing images | ||
653 | |a convolutional neural network | ||
653 | |a multi-layer features | ||
653 | |a edge | ||
653 | |a spatial energy planning | ||
653 | |a site-selection process | ||
653 | |a participatory planning | ||
653 | |a onshore wind farms | ||
653 | |a photovoltaic farms | ||
653 | |a GIS | ||
653 | |a AHP | ||
653 | |a Borda Count | ||
653 | |a TOPSIS | ||
653 | |a Israel | ||
653 | |a land use | ||
653 | |a life cycle thinking and photovoltaic system | ||
653 | |a photovoltaic solar potential | ||
653 | |a calibration | ||
653 | |a validation | ||
653 | |a ArcGIS solar radiation | ||
653 | |a Netherlands | ||
653 | |a solar panel | ||
653 | |a deep learning | ||
653 | |a semantic segmentation | ||
653 | |a small-hydro | ||
653 | |a tuxtlas mountains | ||
653 | |a Grid | ||
653 | |a SWAT | ||
653 | |a MSA | ||
653 | |a marine renewable energy | ||
653 | |a wind climate | ||
653 | |a wind power assessment | ||
653 | |a wind energy capacity factor | ||
653 | |a scatterometer | ||
653 | |a Thracian Sea | ||
653 | |a power system | ||
653 | |a wind power production | ||
653 | |a SCADA data | ||
653 | |a fuzzy GMDH neural network | ||
653 | |a grey wolf optimization | ||
653 | |a district heat network | ||
653 | |a thermal energy modelling | ||
653 | |a heat map | ||
653 | |a university campus | ||
653 | |a Ireland | ||
653 | |a GIS energy mapping | ||
653 | |a building heat demand and generation | ||
653 | |a heat balance | ||
653 | |a pollutant emission | ||
653 | |a traffic mobility | ||
653 | |a COVID-19 | ||
653 | |a sustainable transportation | ||
653 | |a paired sample t-test | ||
653 | |a tides | ||
653 | |a tidal current | ||
653 | |a tidal velocity | ||
653 | |a barrages | ||
653 | |a channels | ||
653 | |a bathymetry | ||
653 | |a flow rate | ||
653 | |a site analysis | ||
653 | |a coastal resources | ||
653 | |a total solar irradiation | ||
653 | |a GIS analysis | ||
653 | |a mathematical model | ||
653 | |a grid map design | ||
653 | |a statistical analysis | ||
653 | |a sustainable urban planning | ||
653 | |a solar irradiation | ||
653 | |a renewable energy assessment | ||
653 | |a solar harvesting surface | ||
653 | |a urban energy tools | ||
653 | |a n/a | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/4882 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/78788 |7 0 |z DOAB: description of the publication |