00865nam0-2200325---450-99000933921040332120110407145252.00878930094000933921FED01000933921(Aleph)000933921FED0100093392120110407d1997----km-y0itaa50------baengUSy-------001yyAnimal behavioran evolutionary approachJohn Alcock6th edSunderland (Mass.)Sinauer Associatesc1997pag. variaill.27 cmEtologia591.51Alcock,John<1942- >291278ITUNINARICAUNIMARCBK990009339210403321IV C 32313965DMVSFDMVSFAnimal behavior30007UNINA01029nam0-22003371i-450-99000543661020331620010829120000.0000543661USA01000543661(ALEPH)000543661USA0100054366120010829d1994-------|0itac50------baitaIT||||Z 1||||<<Il>> comparto dei succhi di agrumiun caso di analisi interorganizzativaAurel io IoriSalernoDipartimento di Scienze Economiche1994 - 38 p. : graf. ; 24 cmWorking paper342001Working paper34IORI,Aurelio249032ITSOL20120104990005436610203316DIP.TO SCIENZE ECONOMICHE - (SA)DS DIP WP34 DISESDIP WP2301 DISESBKDISES20121027USA01153220121027USA011612Comparto dei succhi di agrumi1144139UNISAUSA304804877nam 2201069z- 450 991055749430332120210501(CKB)5400000000042888(oapen)https://directory.doabooks.org/handle/20.500.12854/68414(oapen)doab68414(EXLCZ)99540000000004288820202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierShort-Term Load Forecasting 2019Basel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (324 p.)3-03943-442-X 3-03943-443-8 Short-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.History of engineering and technologybicsscbuilding electric energy consumption forecastingbus load forecastingcold-start problemcombined modelcomponent estimation methodconvolution neural networkcost analysiscubic splinesdata augmentationdata preprocessing techniqueday aheadDBNdeep learningdeep residual neural networkdemand responsedemand-side managementdistributed energy resourceselectric load forecastingelectricityelectricity consumptionelectricity demandfeature extractionfeature selectionforecastinghierarchical short-term load forecastinghybrid energy systemlassoload forecastingLoad forecastingload meteringlong short-term memorymodeling and forecastingmultiobjective optimization algorithmmultiple sourcesmultivariate random forestsNordic electricity marketpattern similarityperformance criteriapower systemspreliminary loadprosumersPSRrandom forestreal-time electricity loadregressive modelsresidential load forecastingseasonal patternsshort term load forecastingshort-term load forecastingspecial daysTikhonov regularizationtime seriestransfer learningunivariate and multivariate time series analysisVSTLFwavenetweather station selectionHistory of engineering and technologyGabaldón Antonioedt1277616Ruiz-Abellón Dr. María CarmenedtFernández-Jiménez Luis AlfredoedtGabaldón AntonioothRuiz-Abellón Dr. María CarmenothFernández-Jiménez Luis AlfredoothBOOK9910557494303321Short-Term Load Forecasting 20193011722UNINA