LEADER 03422nam 22005653 450 001 9911004738803321 005 20250304142008.0 010 $a1-83724-504-5 010 $a1-5231-5348-2 010 $a1-5231-4674-5 010 $a1-83953-562-8 035 $a(MiAaPQ)EBC7018186 035 $a(Au-PeEL)EBL7018186 035 $a(CKB)23843333500041 035 $aEBL7018186 035 $a(AU-PeEL)EBL7018186 035 $a(NjHacI)9923843333500041 035 $a(BIP)083130365 035 $a(OCoLC)1333082263 035 $a(EXLCZ)9923843333500041 100 $a20220619d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIndustrial Demand Response $eMethods, Best Practices, Case Studies, and Applications 205 $a1st ed. 210 1$aStevenage :$cInstitution of Engineering & Technology,$d2022. 210 4$dİ2022. 215 $a1 online resource (426 pages) 225 1 $aEnergy Engineering 300 $aDescription based upon print version of record. 311 08$a1-83953-561-X 327 $aChapter 1: A comprehensive review on industrial demand response strategies and applicationsChapter 2: Demand response cybersecurity for power systems with high renewable power shareChapter 3: Recurrent neural networks for electrical load forecasting to use in demand responseChapter 4: Optimal demand response strategy of an industrial customerChapter 5: Price-based demand response for thermostatically controlled loadsChapter 6: Electric vehicle massive resources mining and demand response applicationChapter 7: Demand response measurement and verification approaches: analyses and guidelinesChapter 8: Transactive energy industry demand response management marketChapter 9: Industrial demand response opportunities with residential appliances in smart gridsChapter 10: Modelling and optimal scheduling of flexibility in energy-intensive industryChapter 11: Industrial demand response: coordination with asset managementChapter 12: A machine learning-based approach for industrial demand responseChapter 13: Feasibility assessment of industrial demand responseChapter 14: Measurement and verification of demand response: the customer load baselineChapter 15: Modeling and optimizing the value of flexible industrial processes in the UK electricity marketChapter 16: Case study of Aran Islands: optimal demand response control of heat pumps and appliancesChapter 17: Use case of artificial intelligence, and neural networks in energy consumption markets, and industrial demand response. 330 $aDemand response (DR) describes controlled changes in the power consumption whose role is to better match the power demand with the supply. This reference, written by an international team of experts from academia and industry, covers the principles, implementation and applications of DR. 410 0$aEnergy Engineering 606 $aElectric power consumption$xForecasting 615 0$aElectric power consumption$xForecasting. 676 $a621.3 700 $aAlhelou$b Hassan Haes$01823035 701 $aMoreno-Mun?oz$b Antonio$00 701 $aSiano$b Pierluigi$01289215 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911004738803321 996 $aIndustrial Demand Response$94390847 997 $aUNINA