research-article

Smart Technologies in Reducing Carbon Emission: Artificial Intelligence and Smart Water Meter

Authors:

Khoi Anh Nguyen,

Oz Sahin,

Rodney Anthony Stewart,

Hong ZhangAuthors Info & Claims

ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

Pages 517 - 522

https://doi.org/10.1145/3055635.3056566

Published: 24 February 2017 Publication History

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Abstract

Global warming caused by greenhouse gases (GHG) is regarded as one of the biggest threats facing our world. Climate scientists predict that a 1.5°C rise in global temperature may cause the extinction of 25% of the Earth's animals and plants disappear. In this fearsome prospect, carbon emission was identified as the main factor contributing to this issue, and needed to be effectively controlled to mitigate their detrimental impacts on the environment as well as human life. GHG mitigation requires developing and implementing policies, and utilizing new technologies to reduce GHG. In this paper, we explore the role of smart technologies in reducing the carbon emission. With the increasing deployment of Smart water meters across Australia in the last five years, an intelligent and knowledge base system called Autoflow© has been developed to help: (i) monitor and predict carbon emission level from water consumption in realtime (e.g. Property A: Carbon emission from 6am-6pm tomorrow is 12.4kg), and (ii) suggest options for reducing water consumption and carbon emission. This Autoflow© system operates based on smart algorithms including Dynamic Time Warping, Hidden Markov Model, Dynamic Harmonic Regression and Artificial Neural Network, and has potential to go beyond Australian border in a very near future to help effectively sustain the limited water resource and environment around the word.

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Sharma NDe PSharma NDe P(2024)Klimawandel und KI in den Finanz-, Energie-, Haushalts- und VerkehrssektorenAuf dem Weg zu Netto-Null-Zielen10.1007/978-981-97-0335-7_1(1-24)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-981-97-0335-7_1
Wilhelm SKasbauer JJakob DElser BAhrens D(2023)Exploiting Smart Meter Water Consumption Measurements for Human Activity Event RecognitionJournal of Sensor and Actuator Networks10.3390/jsan1203004612:3(46)Online publication date: 6-Jun-2023
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Goh CWang H(2023)Applications of Artificial Intelligence Enabled Systems in Buildings for Optimised Sustainability PerformanceProceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate10.1007/978-981-99-3626-7_32(405-416)Online publication date: 5-Aug-2023
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Published In

ICMLC '17: Proceedings of the 9th International Conference on Machine Learning and Computing

February 2017

545 pages

ISBN:9781450348171

DOI:10.1145/3055635

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 February 2017

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ICMLC 2017

ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing

February 24 - 26, 2017

Singapore, Singapore

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Sharma NDe PSharma NDe P(2024)Klimawandel und KI in den Finanz-, Energie-, Haushalts- und VerkehrssektorenAuf dem Weg zu Netto-Null-Zielen10.1007/978-981-97-0335-7_1(1-24)Online publication date: 18-Apr-2024
https://doi.org/10.1007/978-981-97-0335-7_1
Wilhelm SKasbauer JJakob DElser BAhrens D(2023)Exploiting Smart Meter Water Consumption Measurements for Human Activity Event RecognitionJournal of Sensor and Actuator Networks10.3390/jsan1203004612:3(46)Online publication date: 6-Jun-2023
https://doi.org/10.3390/jsan12030046
Goh CWang H(2023)Applications of Artificial Intelligence Enabled Systems in Buildings for Optimised Sustainability PerformanceProceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate10.1007/978-981-99-3626-7_32(405-416)Online publication date: 5-Aug-2023
https://doi.org/10.1007/978-981-99-3626-7_32
Sharma NDe PSharma NDe P(2022)Climate Change and AI in the Financial, Energy, Domestic, and Transport SectorsTowards Net-Zero Targets10.1007/978-981-19-5244-9_1(1-21)Online publication date: 17-Sep-2022
https://doi.org/10.1007/978-981-19-5244-9_1
Gade D(2021)Reinventing Smart Water Management System through ICT and IoT Driven Solution for Smart CitiesInternational Journal of Applied Engineering and Management Letters10.47992/IJAEML.2581.7000.0109(132-151)Online publication date: 26-Nov-2021
https://doi.org/10.47992/IJAEML.2581.7000.0109
Gourmelon NBayer SMayle MBach GBebber CMunck CSosna CMaier A(2021)Implications of Experiment Set-Ups for Residential Water End-Use ClassificationWater10.3390/w1302023613:2(236)Online publication date: 19-Jan-2021
https://doi.org/10.3390/w13020236
Mor SMadan SPrasad K(2021)Artificial intelligence and carbon footprints: Roadmap for Indian agricultureStrategic Change10.1002/jsc.240930:3(269-280)Online publication date: 10-May-2021
https://doi.org/10.1002/jsc.2409

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