LEADER 02664oam 2200445 450 001 9910437776603321 005 20190911103511.0 010 $a1-4471-4968-8 024 7 $a10.1007/978-1-4471-4968-2 035 $a(OCoLC)834096863 035 $a(MiFhGG)GVRL6XNC 035 $a(EXLCZ)992670000000530217 100 $a20130219d2013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aIntelligent energy demand forecasting /$fWei-Chiang Hong 205 $a1st ed. 2013. 210 1$aLondon :$cSpringer,$d2013. 215 $a1 online resource (xiii, 189 pages) $cillustrations (some color) 225 1 $aLecture Notes in Energy,$x2195-1284 ;$v10 300 $a"ISSN: 2195-1284." 311 $a1-4471-4967-X 311 $a1-4471-5930-6 320 $aIncludes bibliographical references. 327 $a1.Introduction -- 2.Modeling for Energy Demand Forecasting -- 3.Evolutionary Algorithms in SVR?s Parameters Determination -- 4.Chaos/Cloud Theories to Avoid Trapping into Local Optimum -- 5.Recurrent/Seasonal Mechanism to Improve the Accurate Level of Forecasting. 330 $aAs industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand.   Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms.   Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools. 410 0$aLecture notes in energy ;$v10. 606 $aPower resources$xForecasting 615 0$aPower resources$xForecasting. 676 $a333.7913 700 $aHong$b Wei-Chiang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0913784 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910437776603321 996 $aIntelligent Energy Demand Forecasting$92047305 997 $aUNINA LEADER 03652nam 22007095 450 001 9910631084103321 005 20230803072421.0 010 $a981-19-7780-1 024 7 $a10.1007/978-981-19-7780-0 035 $a(MiAaPQ)EBC7141538 035 $a(Au-PeEL)EBL7141538 035 $a(CKB)25360921200041 035 $a(OCoLC)1351197248 035 $a(DE-He213)978-981-19-7780-0 035 $a(PPN)266350518 035 $a(EXLCZ)9925360921200041 100 $a20221116d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAgriculture Digitalization and Organic Production $eProceedings of the Second International Conference, ADOP 2022, St. Petersburg, Russia, June 06?08, 2022 /$fedited by Andrey Ronzhin, Alexander Kostyaev 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (457 pages) 225 1 $aSmart Innovation, Systems and Technologies,$x2190-3026 ;$v331 311 08$aPrint version: Ronzhin, Andrey Agriculture Digitalization and Organic Production Singapore : Springer,c2022 9789811977794 320 $aIncludes bibliographical references and index. 327 $aStimulating the Export of Organic Products -- Risks in Animal Feeding and Digital Methods of Their Analysis -- Speci?c Approaches to Robotic Milking with Different Cow Movement Systems -- Fish-Breeding Technological Cadastre as a Mechanism for the Development of Aquaculture in the Kaliningrad Region -- Towards the Managed Transition to Organic Agriculture: Searching for a Strategic Model -- Smart Technologies in Bio-Intensive Organic Agriculture. 330 $aThis book includes selected papers from the Second International Conference on Agriculture Digitalization and Organic Production (ADOP 2022), held in St. Petersburg, Russia, during June 06?08, 2022. The topics covered in the book are ground robotic systems in crop production, unmanned aerial vehicles in crop production, aerospace monitoring tools in crop production, robotic animal husbandry, digitalization of technological processes of agricultural production, evaluation of the effectiveness of digital technologies for the production of organic products, rational nature management and ecology in agricultural production, technologies for the production of organic agricultural products, market analysis of organic agricultural products, and legal aspects of organic production. 410 0$aSmart Innovation, Systems and Technologies,$x2190-3026 ;$v331 606 $aAutomatic control 606 $aRobotics 606 $aAutomation 606 $aAgriculture 606 $aArtificial intelligence 606 $aMechanical engineering 606 $aControl, Robotics, Automation 606 $aAgriculture 606 $aArtificial Intelligence 606 $aMechanical Engineering 615 0$aAutomatic control. 615 0$aRobotics. 615 0$aAutomation. 615 0$aAgriculture. 615 0$aArtificial intelligence. 615 0$aMechanical engineering. 615 14$aControl, Robotics, Automation. 615 24$aAgriculture. 615 24$aArtificial Intelligence. 615 24$aMechanical Engineering. 676 $a016.016 702 $aRonzhin$b A. L$g(Andrei? Leonidovich), 702 $aKostyaev$b Alexander 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910631084103321 996 $aAgriculture digitalization and organic production$92851038 997 $aUNINA