LEADER 00750nam0-22002771i-450- 001 990001096800403321 035 $a000109680 035 $aFED01000109680 035 $a(Aleph)000109680FED01 035 $a000109680 100 $a20000920d1954----km-y0itay50------ba 101 0 $aeng 200 1 $aIntroduction to Nuclear Engineering$fBy Raymond L. Murray 210 $aNew York$cPrentice-Hall$d1954 610 0 $aEnergia nucleare 610 0 $aReattori 676 $a539.74 700 1$aMurray,$bRaymond L.$018439 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001096800403321 952 $a34B-074$b16971$fFI1 959 $aFI1 996 $aIntroduction to nuclear engineering$9130854 997 $aUNINA DB $aING01 LEADER 02172nam 2200493 450 001 9910707383603321 005 20161019143338.0 035 $a(CKB)5470000002463679 035 $a(OCoLC)960910499 035 $a(EXLCZ)995470000002463679 100 $a20161019d2005 ua 0 101 0 $aeng 135 $aurmn||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFuels planning: science synthesis and integration$iEnvironmental consequences fact sheet$h15$iThe Wildlife Habitat Response Model /$fDavid Pilliod 210 1$a[Fort Collins, Colo.] :$cUnited States Department of Agriculture, Forest Service, Rocky Mountain Research Station,$d2005. 215 $a1 online resource (2 unnumbered pages) $ccolor illustrations 225 1 $aResearch note RMRS ;$vRN-23-15-WWW 300 $aTitle from caption (viewed October 19, 2016). 300 $a"November 2005." 300 $a"Pacific Northwest Research Station." 300 $a"North Central Research Station." 300 $a"Pacific Southwest Research Station." 300 $a"Synthesizing scientific information for fire and fuels project managers." 517 $aFuels planning 606 $aForest animals$xEffect of forest management on$zWest (U.S.)$xComputer programs 606 $aForest animals$xHabitat$zWest (U.S.)$xComputer programs 606 $aFire management$xEnvironmental aspects$zWest (U.S.)$xComputer programs 615 0$aForest animals$xEffect of forest management on$xComputer programs. 615 0$aForest animals$xHabitat$xComputer programs. 615 0$aFire management$xEnvironmental aspects$xComputer programs. 700 $aPilliod$b David S.$01396487 712 02$aRocky Mountain Research Station (Fort Collins, Colo.), 712 02$aPacific Northwest Research Station (Portland, Or.) 712 02$aUnited States.$bForest Service.$bNorth Central Research Station. 712 02$aPacific Southwest Research Station. 801 0$bGPO 801 1$bGPO 906 $aBOOK 912 $a9910707383603321 996 $aFuels planning: science synthesis and integration$93487048 997 $aUNINA LEADER 04877nam 2201069z- 450 001 9910557494303321 005 20210501 035 $a(CKB)5400000000042888 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68414 035 $a(oapen)doab68414 035 $a(EXLCZ)995400000000042888 100 $a20202105d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aShort-Term Load Forecasting 2019 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (324 p.) 311 08$a3-03943-442-X 311 08$a3-03943-443-8 330 $aShort-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030-50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system. 606 $aHistory of engineering and technology$2bicssc 610 $abuilding electric energy consumption forecasting 610 $abus load forecasting 610 $acold-start problem 610 $acombined model 610 $acomponent estimation method 610 $aconvolution neural network 610 $acost analysis 610 $acubic splines 610 $adata augmentation 610 $adata preprocessing technique 610 $aday ahead 610 $aDBN 610 $adeep learning 610 $adeep residual neural network 610 $ademand response 610 $ademand-side management 610 $adistributed energy resources 610 $aelectric load forecasting 610 $aelectricity 610 $aelectricity consumption 610 $aelectricity demand 610 $afeature extraction 610 $afeature selection 610 $aforecasting 610 $ahierarchical short-term load forecasting 610 $ahybrid energy system 610 $alasso 610 $aload forecasting 610 $aLoad forecasting 610 $aload metering 610 $along short-term memory 610 $amodeling and forecasting 610 $amultiobjective optimization algorithm 610 $amultiple sources 610 $amultivariate random forests 610 $aNordic electricity market 610 $apattern similarity 610 $aperformance criteria 610 $apower systems 610 $apreliminary load 610 $aprosumers 610 $aPSR 610 $arandom forest 610 $areal-time electricity load 610 $aregressive models 610 $aresidential load forecasting 610 $aseasonal patterns 610 $ashort term load forecasting 610 $ashort-term load forecasting 610 $aspecial days 610 $aTikhonov regularization 610 $atime series 610 $atransfer learning 610 $aunivariate and multivariate time series analysis 610 $aVSTLF 610 $awavenet 610 $aweather station selection 615 7$aHistory of engineering and technology 700 $aGabaldón$b Antonio$4edt$01277616 702 $aRuiz-Abellón$b Dr. María Carmen$4edt 702 $aFernández-Jiménez$b Luis Alfredo$4edt 702 $aGabaldón$b Antonio$4oth 702 $aRuiz-Abellón$b Dr. María Carmen$4oth 702 $aFernández-Jiménez$b Luis Alfredo$4oth 906 $aBOOK 912 $a9910557494303321 996 $aShort-Term Load Forecasting 2019$93011722 997 $aUNINA