03214nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001600172072001700188072002300205072001600228100008500244245010900329250001800438264007200456300006500528336002600593337002600619338003600645347002400681505054000705520096601245650004902211650002302260650003102283650002902314650003502343650002302378650003102401650002902432710003402461773002602495776003602521776003602557776003602593856004602629912001402675912001402689999001702703978-981-99-9199-0DE-He21320251030112330.0cr nn 008mamaa240624s2024    si |    s    |||| 0|eng d  a97898199919909978-981-99-9199-07 a10.1007/978-981-99-9199-02doi 4aTH7005-7699 7aTNKH2bicssc 7aTEC0090202bisacsh 7aTNKE2thema1 aNinagawa, Chuzo.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut97925710aIoT/AI Control of VRF Distributed Building Air-Conditionersh[electronic resource] /cby Chuzo Ninagawa.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXIV, 205 p. 123 illus., 2 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 1. Innovations in Air-conditioning Energy Control -- Chapter 2. BACnet Communication Control -- Chapter 3. LON Communication Control -- Chapter 4. WEB Cloud Communication Control -- Chapter 5. Virtual Wattmeter for VRF Air-conditioner -- Chapter 6. Fast Automated Demand Response -- Chapter 7. Deep Learning for FastADR -- Chapter 8. IEEJ Power Supply-Demand Adjustment Service  -- Chapter 9. OpenADR Communication Control -- Chapter 10. Optimal Real-Time Pricing Control -- Chapter 11. Power Control by Reinforcement Learning.  aThis book describes new energy service controls of VRF (Variable Refrigerant Flow) air-conditioners, i.e., distributed-type air-conditioners for commercial buildings in the near future, in the context of the energy savings for CO2 reduction and the reform of the electric power system. In other words, this book introduces the state-of-the-art technology of the next-generation distributed building air-conditioning energy service system, from IoT cloud control to AI optimal control, as well as standards for the smart grid supply and demand adjustment market. Rather than simple saving energy by On Off operations or shifting set- temperatures, the author proposes technology that sends numerical commands for the air-conditioner inverters directly from the cloud. By using this innovative IoT method, this book describes how to realizes the AI optimal cloud control as a cluster of air-conditioners while machine-learning of each air conditioner's situation. 0aBuildingsxEnvironmental engineering.966264 0aAutomation.959149 0aInternet of things.958845 0aMachine learning.95505914aBuilding Physics, HVAC.96626524aAutomation.95914924aInternet of Things.95884524aMachine Learning.9550592 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981999198308iPrinted edition:z978981999200308iPrinted edition:z978981999201040uhttps://doi.org/10.1007/978-981-99-9199-0  aZDB-2-ENG  aZDB-2-SXE  c60990d60990