LEADER 00829nam0-22002891i-450- 001 990000849390403321 005 20001010 035 $a000084939 035 $aFED01000084939 035 $a(Aleph)000084939FED01 035 $a000084939 100 $a20001010d--------km-y0itay50------ba 101 0 $aita 105 $ay-------001yy 200 1 $aOptimization in mechanics$eproblems and methods 210 $aAmsterdam$cElsevier Science Publishers$d1988. 215 $aXII 279 p.$d23 cm 225 1 $aNorth-Holland series in applied mathematics and mechanics$v34 700 1$aBrousse,$bPierre$03109 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990000849390403321 952 $a02 66 A 4$b5648$fFINBN 959 $aFINBN 996 $aOptimization in mechanics$9113922 997 $aUNINA DB $aING01 LEADER 03761nam 2200685z- 450 001 9910557722403321 005 20210501 035 $a(CKB)5400000000046100 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69209 035 $a(oapen)doab69209 035 $a(EXLCZ)995400000000046100 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData Mining in Smart Grids 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 online resource (116 p.) 311 08$a3-03943-326-1 311 08$a3-03943-327-X 330 $aEffective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: ? Fuzziness in smart grids computing ? Emerging techniques for renewable energy forecasting ? Robust and proactive solution of optimal smart grids operation ? Fuzzy-based smart grids monitoring and control frameworks ? Granular computing for uncertainty management in smart grids ? Self-organizing and decentralized paradigms for information processing 606 $aInformation technology industries$2bicssc 610 $acase-based reasoning 610 $acomputational intelligence 610 $adata matching 610 $adata mining 610 $adata preprocessing 610 $adata visualization 610 $adecentral smart grid control (DSGC) 610 $adecentralized control architecture 610 $aDSHW 610 $adynamic Bayesian network 610 $afuzzy rule-based classifiers 610 $agas insulated switchgear 610 $ainterpretable and accurate DSGC-stability prediction 610 $aMarkov model 610 $amulti-agent systems 610 $amulti-objective evolutionary optimization 610 $aNN-AR 610 $anumerical weather prediction 610 $apartial discharge 610 $apower systems resilience 610 $aprobabilistic modeling 610 $aresilience models 610 $asmart grid 610 $at-SNE algorithm 610 $aTBATS 610 $atime-series clustering 610 $avariational autoencoder 610 $avoltage regulation 610 $awind power generation 615 7$aInformation technology industries 700 $aVaccaro$b Alfredo$4edt$01309863 702 $aVaccaro$b Alfredo$4oth 906 $aBOOK 912 $a9910557722403321 996 $aData Mining in Smart Grids$93029676 997 $aUNINA