LEADER 04689nam 2201069z- 450 001 9910557427903321 005 20240315202342.0 035 $a(CKB)5400000000043445 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68491 035 $a(EXLCZ)995400000000043445 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAssessment of Renewable Energy Resources with Remote Sensing 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (244 p.) 311 $a3-0365-0480-X 311 $a3-0365-0481-8 330 $aThe 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. 606 $aResearch & information: general$2bicssc 610 $ametaheuristic 610 $aparameter extraction 610 $asolar photovoltaic 610 $awhale optimization algorithm 610 $acloud detection 610 $adigitized image processing 610 $aartificial neural networks 610 $asolar irradiance estimation 610 $asolar irradiance forecasting 610 $asolar energy 610 $asky camera 610 $aremote sensing 610 $aCSP plants 610 $acoastal wind measurements 610 $ascanning LiDAR 610 $aplan position indicator 610 $avelocity volume processing 610 $aHazaki Oceanographical Research Station 610 $acloud coverage 610 $aimage processing 610 $atotal sky imagery 610 $ageothermal energy 610 $ageophysical prospecting 610 $atime domain electromagnetic method 610 $aelectrical resistivity tomography 610 $apotential well field location 610 $aGES-CAL software 610 $asmart island 610 $asolar radiation forecasting 610 $alight gradient boosting machine 610 $amultistep-ahead prediction 610 $afeature importance 610 $avoxel-design approach 610 $ashading envelopes 610 $apoint cloud data 610 $acomputational design method 610 $apassive design strategy 610 $alake breeze influence 610 $ahydropower reservoir 610 $asolar irradiance enhancement 610 $asolar energy resource 610 $awind speed 610 $aextreme value analysis 610 $ascatterometer 610 $afeature engineering 610 $aforecasting 610 $agraphical user interface software 610 $amachine learning 610 $aphotovoltaic power plant 610 $asurface solar radiation 610 $aglobal radiation 610 $asatellite 610 $aBaltic area 610 $acoastline 610 $acloud 610 $aconvection 610 $aclimate 610 $arenewable energy resource assessment and forecasting 610 $aremote sensing data acquisition 610 $adata processing 610 $astatistical analysis 610 $amachine learning techniques 615 7$aResearch & information: general 700 $aMartins$b Fernando Ramos$4edt$01297532 702 $aMartins$b Fernando Ramos$4oth 906 $aBOOK 912 $a9910557427903321 996 $aAssessment of Renewable Energy Resources with Remote Sensing$93024506 997 $aUNINA