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IoT/AI control of vrf distributed building air-conditioners

Record Type:	Electronic resources : Monograph/item
Title/Author:	IoT/AI control of vrf distributed building air-conditioners/ by Chuzo Ninagawa.
Author:	Ninagawa, Chuzo.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xiv, 205 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Air conditioning - Control. -
Online resource:	https://doi.org/10.1007/978-981-99-9199-0
ISBN:	9789819991990

IoT/AI control of vrf distributed building air-conditioners
Ninagawa, Chuzo.

IoT/AI control of vrf distributed building air-conditioners[electronic resource] /by Chuzo Ninagawa. - Singapore :Springer Nature Singapore :2024. - xiv, 205 p. :ill. (some col.), digital ;24 cm.

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.

ISBN: 9789819991990

Standard No.: 10.1007/978-981-99-9199-0doiSubjects--Topical Terms:

649490
Air conditioning
--Control.

LC Class. No.: TH7687.5

Dewey Class. No.: 697.93

IoT/AI control of vrf distributed building air-conditioners
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100 1 $a Ninagawa, Chuzo. $3 3589886
245 1 0 $a IoT/AI control of vrf distributed building air-conditioners $h [electronic resource] / $c by Chuzo Ninagawa.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xiv, 205 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Air conditioning $x Control. $3 649490
650 0 $a Air conditioning $x Technological innovations. $3 3719144
650 1 4 $a Building Physics, HVAC. $3 2055153
650 2 4 $a Automation. $3 633305
650 2 4 $a Internet of Things. $3 3538511
650 2 4 $a Machine Learning. $3 3382522
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-9199-0
950 $a Engineering (SpringerNature-11647)