Chuzo Ninagawa

IoT/AI Control of VRF Distributed Building Air-Conditioners (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Singapore
• Seitenzahl: 205
• Erscheinungstermin: 24. Juni 2024
• Englisch
• ISBN-13: 9789819991990
• Artikelnr.: 71168152

Autorenporträt

Prof. Chuzo Ninagawa is Professor at the Faculty of Engineering, Gifu University, Japan, also CEO of N Labobaroty, Inc. Dr. Ninagawa has been Executive Chief Engineer of Mitsubishi Heavy Industries, Ltd., which is one of the largest hi-tech manufacturers inJapan. His research interests span various topics of smart grid, with special focus on virtual power plant (VPP) with a large-scale aggregation of fast automated demand responses. He has published over110 academic papers and three advanced research books

Inhaltsangabe

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.