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