LEADER 02295nam 2200373 450 001 996575000903316 005 20231205170133.0 010 $a1-5044-8777-X 035 $a(IEEE)9828002 035 $a(CKB)24248997900041 035 $a(NjHacI)9924248997900041 035 $a(EXLCZ)9924248997900041 100 $a20231205d2022 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$a"1885-2022 - IEEE Guide for Assessing, Measuring, and Verifying Volt-Var Control and Optimization on Distribution Systems" /$fIEEE 210 1$aNew York :$cIEEE,$d2022. 210 4$dİ2022 215 $a1 online resource (58 pages) 330 $aElectric utilities are seeking to improve the overall efficiency and performance of the distribution system while helping to achieve energy and demand savings. Distribution volt-var optimization (VVO) can play a major role in accomplishing these objectives while maintaining safety, preserving assets, and meeting all operating constraints such as loading and voltage levels. Initial studies and experience show there is significant potential for energy savings, demand management and loss reduction through improved management of distribution voltage profiles and reactive power flow. Consistent methods are needed for verifying the benefits achieved by VVO systems that have already been implemented. Guidelines for modeling system loads as well as distributed resources and their response to voltage and var changes are needed along with methods for performing the evaluations to estimate total benefits. These benefits can then be evaluated as a function of the investment requirements for the improved VVO on a feeder by feeder or substation by substation basis and deployment priorities can be developed. 606 $aElectric power distribution 606 $aEnergy conservation 615 0$aElectric power distribution. 615 0$aEnergy conservation. 676 $a621.319 801 0$bNjHacI 801 1$bNjHacl 906 $aDOCUMENT 912 $a996575000903316 996 $a"1885-2022 - IEEE Guide for Assessing, Measuring, and Verifying Volt-Var Control and Optimization on Distribution Systems"$93880166 997 $aUNISA LEADER 03689nam 22007815 450 001 9910484963903321 005 20250315211408.0 010 $a9781071612828 010 $a1071612824 024 7 $a10.1007/978-1-0716-1282-8 035 $a(CKB)4100000011807207 035 $a(MiAaPQ)EBC6524983 035 $a(Au-PeEL)EBL6524983 035 $a(OCoLC)1243554583 035 $a(PPN)254719716 035 $a(DE-He213)978-1-0716-1282-8 035 $a(EXLCZ)994100000011807207 100 $a20210322d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLinear and Generalized Linear Mixed Models and Their Applications /$fby Jiming Jiang, Thuan Nguyen 205 $a2nd ed. 2021. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2021. 215 $a1 online resource (352 pages) $cillustrations 225 1 $aSpringer Series in Statistics,$x2197-568X 311 08$a9781071612811 311 08$a1071612816 320 $aIncludes bibliographical references and index. 327 $a1. Linear Mixed Models: Part I -- 2. Linear Mixed Models: Part II -- 3. Generalized Linear Mixed Models: Part I -- 4. Generalized Linear Mixed Models: Part II. 330 $aNow in its second edition, this book covers two major classes of mixed effects models?linear mixed models and generalized linear mixed models?and it presents an up-to-date account of theory and methods in analysis of these models as well as their applications in various fields. It offers a systematic approach to inference about non-Gaussian linear mixed models. Furthermore, it discusses the latest developments and methods in the field, incorporating relevant updates since publication of the first edition. These include advances in high-dimensional linear mixed models in genome-wide association studies (GWAS), advances in inference about generalized linear mixed models with crossed random effects, new methods in mixed model prediction, mixed model selection, and mixed model diagnostics. This book is suitable for students, researchers, and practitioners who are interested in using mixed models for statistical data analysis with public health applications. It is best for graduatecourses in statistics, or for those who have taken a first course in mathematical statistics, are familiar with using computers for data analysis, and have a foundational background in calculus and linear algebra. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aBiometry 606 $aProbabilities 606 $aStatistics 606 $aPublic health 606 $aNumerical analysis 606 $aPopulation genetics 606 $aBiostatistics 606 $aProbability Theory 606 $aStatistical Theory and Methods 606 $aPublic Health 606 $aNumerical Analysis 606 $aPopulation Genetics 615 0$aBiometry. 615 0$aProbabilities. 615 0$aStatistics. 615 0$aPublic health. 615 0$aNumerical analysis. 615 0$aPopulation genetics. 615 14$aBiostatistics. 615 24$aProbability Theory. 615 24$aStatistical Theory and Methods. 615 24$aPublic Health. 615 24$aNumerical Analysis. 615 24$aPopulation Genetics. 676 $a519.5 700 $aJiang$b Jiming$0614598 702 $aNguyen$b Thuan 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484963903321 996 $aLinear and generalized linear mixed models and their applications$91891947 997 $aUNINA