IoT/AI Control of VRF Distributed Building Air-Conditioners

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...

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Մատենագիտական մանրամասներ
Հիմնական հեղինակ: Ninagawa, Chuzo (Հեղինակ)
Համատեղ հեղինակ: SpringerLink (Online service)
Ձևաչափ: Էլեկտրոնային էլ․ գիրք
Լեզու:անգլերեն
Հրապարակվել է: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Հրատարակություն:1st ed. 2024.
Խորագրեր:
Առցանց հասանելիություն:https://doi.org/10.1007/978-981-99-9199-0
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250 |a 1st ed. 2024. 
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300 |a XIV, 205 p. 123 illus., 2 illus. in color.  |b online resource. 
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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. 
532 8 |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. 
532 8 |a No reading system accessibility options actively disabled 
532 8 |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com 
650 0 |a Buildings  |x Environmental engineering. 
650 0 |a Automation. 
650 0 |a Internet of things. 
650 0 |a Machine learning. 
650 1 4 |a Building Physics, HVAC. 
650 2 4 |a Automation. 
650 2 4 |a Internet of Things. 
650 2 4 |a Machine Learning. 
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