Assessment of Renewable Energy Resources with Remote Sensing

The book "Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide a...

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Bibliographic Details
Other Authors: Martins, Fernando Ramos (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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520 |a The book "Assessment of Renewable Energy Resources with Remote Sensing" focuses on disseminating scientific knowledge and technological developments for the assessment and forecasting of renewable energy resources using remote sensing techniques. The eleven papers inside the book provide an overview of remote sensing applications on hydro, solar, wind and geothermal energy resources and their major goal is to provide state of art knowledge to contribute with the renewable energy resource deployment, especially in regions where energy demand is rapidly expanding. Renewable energy resources have an intrinsic relationship with local environmental features and the regional climate. Even small and fast environment and/or climate changes can cause significant variability in power generation at different time and space scales. Methodologies based on remote sensing are the primary source of information for the development of numerical models that aim to support the planning and operation of an electric system with a substantial contribution of intermittent energy sources. In addition, reliable data and knowledge on renewable energy resource assessment are fundamental to ensure sustainable expansion considering environmental, financial and energetic security. 
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653 |a parameter extraction 
653 |a solar photovoltaic 
653 |a whale optimization algorithm 
653 |a cloud detection 
653 |a digitized image processing 
653 |a artificial neural networks 
653 |a solar irradiance estimation 
653 |a solar irradiance forecasting 
653 |a solar energy 
653 |a sky camera 
653 |a remote sensing 
653 |a CSP plants 
653 |a coastal wind measurements 
653 |a scanning LiDAR 
653 |a plan position indicator 
653 |a velocity volume processing 
653 |a Hazaki Oceanographical Research Station 
653 |a cloud coverage 
653 |a image processing 
653 |a total sky imagery 
653 |a geothermal energy 
653 |a geophysical prospecting 
653 |a time domain electromagnetic method 
653 |a electrical resistivity tomography 
653 |a potential well field location 
653 |a GES-CAL software 
653 |a smart island 
653 |a solar radiation forecasting 
653 |a light gradient boosting machine 
653 |a multistep-ahead prediction 
653 |a feature importance 
653 |a voxel-design approach 
653 |a shading envelopes 
653 |a point cloud data 
653 |a computational design method 
653 |a passive design strategy 
653 |a lake breeze influence 
653 |a hydropower reservoir 
653 |a solar irradiance enhancement 
653 |a solar energy resource 
653 |a wind speed 
653 |a extreme value analysis 
653 |a scatterometer 
653 |a feature engineering 
653 |a forecasting 
653 |a graphical user interface software 
653 |a machine learning 
653 |a photovoltaic power plant 
653 |a surface solar radiation 
653 |a global radiation 
653 |a satellite 
653 |a Baltic area 
653 |a coastline 
653 |a cloud 
653 |a convection 
653 |a climate 
653 |a renewable energy resource assessment and forecasting 
653 |a remote sensing data acquisition 
653 |a data processing 
653 |a statistical analysis 
653 |a machine learning techniques 
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