03397nam a22005415i 4500 978-981-99-9199-0 DE-He213 20251030112330.0 cr nn 008mamaa 240624s2024 si | s |||| 0|eng d 9789819991990 978-981-99-9199-0 10.1007/978-981-99-9199-0 doi TH7005-7699 TNKH bicssc TEC009020 bisacsh TNKE thema Ninagawa, Chuzo. author. aut http://id.loc.gov/vocabulary/relators/aut 79257 IoT/AI Control of VRF Distributed Building Air-Conditioners [electronic resource] / by Chuzo Ninagawa. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIV, 205 p. 123 illus., 2 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 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. This 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. Buildings Environmental engineering. 66264 Automation. 59149 Internet of things. 58845 Machine learning. 55059 Building Physics, HVAC. 66265 Automation. 59149 Internet of Things. 58845 Machine Learning. 55059 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819991983 Printed edition: 9789819992003 Printed edition: 9789819992010 https://doi.org/10.1007/978-981-99-9199-0 ZDB-2-ENG ZDB-2-SXE 60990 60990