| 000 | 03397nam a22005415i 4500 | ||
|---|---|---|---|
| 001 | 978-981-99-9199-0 | ||
| 003 | DE-He213 | ||
| 005 | 20251030112330.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 240624s2024 si | s |||| 0|eng d | ||
| 020 |
_a9789819991990 _9978-981-99-9199-0 |
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| 024 | 7 |
_a10.1007/978-981-99-9199-0 _2doi |
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| 050 | 4 | _aTH7005-7699 | |
| 072 | 7 |
_aTNKH _2bicssc |
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| 072 | 7 |
_aTEC009020 _2bisacsh |
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| 072 | 7 |
_aTNKE _2thema |
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| 100 | 1 |
_aNinagawa, Chuzo. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _979257 |
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| 245 | 1 | 0 |
_aIoT/AI Control of VRF Distributed Building Air-Conditioners _h[electronic resource] / _cby Chuzo Ninagawa. |
| 250 | _a1st ed. 2024. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2024. |
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| 300 |
_aXIV, 205 p. 123 illus., 2 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _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. | |
| 520 | _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. | ||
| 650 | 0 |
_aBuildings _xEnvironmental engineering. _966264 |
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| 650 | 0 |
_aAutomation. _959149 |
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| 650 | 0 |
_aInternet of things. _958845 |
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| 650 | 0 |
_aMachine learning. _955059 |
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| 650 | 1 | 4 |
_aBuilding Physics, HVAC. _966265 |
| 650 | 2 | 4 |
_aAutomation. _959149 |
| 650 | 2 | 4 |
_aInternet of Things. _958845 |
| 650 | 2 | 4 |
_aMachine Learning. _955059 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819991983 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819992003 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789819992010 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-99-9199-0 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 999 |
_c60990 _d60990 |
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