LEADER 01056nam a2200241 i 4500 001 991004363235807536 005 20250121200432.0 008 250121s1980 it er2 001 0 ita d 040 $aBibl. Dip.le Aggr. Ingegneria Innovazione - Sez. IngegneriaInnovazione$beng$cSocioculturale Scs 041 0 $aita 082 04$a610$223. 100 1 $aBaldini, Alfonso$01782999 245 12$aL'importanza di riferimenti socio-ambientali nella cartella clinica /$cA. Baldini, A. Gattai, A. Perini Carobbi 260 $aPisa :$bIstituto di elaborazione della informazione,$c1980 300 $a7 p. ;$c29 cm 500 $aReport of "2. Congresso Nazionale SIASO Viareggio, 17 maggio 1980" 500 $aOn cover: Consiglio Nazionale delle Ricerche, Istituto di elaborazione della informazione Pisa, Pubblicazione n. L 80-1 650 4$aMedical records$xManagement 700 1 $aGattai, Aldo 700 1 $aPerini Carobbi, Antonio 912 $a991004363235807536 996 $aImportanza di riferimenti socio-ambientali nella cartella clinica$94309832 997 $aUNISALENTO LEADER 01393nam a2200313 i 4500 001 991000862989707536 005 20250219132148.0 008 050228s1980 it er 001 0 ita d 035 $ab13284526-39ule_inst 040 $aBibl. Dip.le Aggr. Studi Umanistici - Sez. Filosofia$bita$dSocioculturale Scs 041 0 $aita$alat 082 04$a192$223 100 1 $aFattori, Marta$0170756 245 10$aLessico del Novum organum di Francesco Bacone /$cMarta Fattori 260 $aRoma :$bEdizioni dell'Ateneo & Bizzarri,$c1980 300 $a2 volumi ;$c24 cm 490 1 $aLessico intellettuale europeo ;$v23-24 504 $aContiene bibliografia. 505 00$g[1]:$tIntroduzione, lessico. -$gIL, 543. -$g(23) 505 00$g[2]:$tIndex locorum, lista di frequenza, distribuzione dei lemmi. -$gXIV, 520 p. -$g(24) 600 14$aBacon, Francis$d<1561-1626>$xSpogli lessicali 830 0$aLessico intellettuale europeo ;$v23-24 907 $a.b13284526$b02-04-14$c28-02-05 912 $a991000862989707536 945 $aLE005 102 L.I.E. V. 23$g1$i2005000165375$lle005$o-$pE42.00$q-$rl$s-$t0$u2$v1$w2$x0$y.i14018238$z28-02-05 945 $aLE005 102 L.I.E. v. 24$g1$i2005000165382$lle005$o-$pE42.00$q-$rl$s-$t0$u1$v0$w1$x0$y.i1401824x$z28-02-05 996 $aLessico del Novum organum di Francesco Bacone$91107395 997 $aUNISALENTO 998 $ale005$b28-02-05$cm$da$e-$fmul$git$h0$i0 LEADER 04324nam 22006855 450 001 9910640389803321 005 20250723051721.0 010 $a3-030-82848-4 024 7 $a10.1007/978-3-030-82848-6 035 $a(MiAaPQ)EBC7173149 035 $a(Au-PeEL)EBL7173149 035 $a(CKB)25994568600041 035 $a(DE-He213)978-3-030-82848-6 035 $a(PPN)267808771 035 $a(EXLCZ)9925994568600041 100 $a20230107d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnergy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks $ePredictive Modelling and Control Techniques /$fby William Holderbaum, Feras Alasali, Ayush Sinha 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (218 pages) 225 1 $aLecture Notes in Energy,$x2195-1292 ;$v85 311 08$aPrint version: Holderbaum, William Energy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks Cham : Springer International Publishing AG,c2023 9783030828479 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Basic tools -- Short term load forecasting -- Control strategies in low voltage network for energy saving -- Optimal control with load forecasting -- Case study: Energy saving based on optimal control and load forecasts -- Conclusion. 330 $aThis book describes the stochastic and predictive control modelling of electrical systems that can meet the challenge of forecasting energy requirements under volatile conditions. The global electrical grid is expected to face significant energy and environmental challenges such as greenhouse emissions and rising energy consumption due to the electrification of heating and transport. Today, the distribution network includes energy sources with volatile demand behaviour, and intermittent renewable generation. This has made it increasingly important to understand low voltage demand behaviour and requirements for optimal energy management systems to increase energy savings, reduce peak loads, and reduce gas emissions. Electrical load forecasting is a key tool for understanding and anticipating the highly stochastic behaviour of electricity demand, and for developing optimal energy management systems. Load forecasts, especially of the probabilistic variety, can support moreinformed planning and management decisions, which will be essential for future low carbon distribution networks. For storage devices, forecasts can optimise the appropriate state of control for the battery. There are limited books on load forecasts for low voltage distribution networks and even fewer demonstrations of how such forecasts can be integrated into the control of storage. This book presents material in load forecasting, control algorithms, and energy saving and provides practical guidance for practitioners using two real life examples: residential networks and cranes at a port terminal. 410 0$aLecture Notes in Energy,$x2195-1292 ;$v85 606 $aEnergy storage 606 $aElectric power distribution 606 $aAutomatic control 606 $aEnergy policy 606 $aEnergy policy 606 $aMechanical and Thermal Energy Storage 606 $aEnergy Grids and Networks 606 $aControl and Systems Theory 606 $aEnergy Policy, Economics and Management 615 0$aEnergy storage. 615 0$aElectric power distribution. 615 0$aAutomatic control. 615 0$aEnergy policy. 615 0$aEnergy policy. 615 14$aMechanical and Thermal Energy Storage. 615 24$aEnergy Grids and Networks. 615 24$aControl and Systems Theory. 615 24$aEnergy Policy, Economics and Management. 676 $a621.319 700 $aHolderbaum$b William$01353334 702 $aAlasali$b Feras 702 $aSinha$b Ayush 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910640389803321 996 $aEnergy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks$93251570 997 $aUNINA