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Urban sensing and smart home energy optimisations: a machine learning approach


Shahriar, MS and Rahman, MS, Urban sensing and smart home energy optimisations: a machine learning approach, Proceedings of the 2015 International Workshop on Internet of Things Towards Applications, 1 November 2015, Seoul, Korea, pp. 19-22. ISBN 978-145033838-7 (2015) [Refereed Conference Paper]

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Copyright 2015 ACM

DOI: doi:10.1145/2820975.2820979


Energy effciency for smart home applications is proposed using urban sensing data with machine learning techniques. We exploit Internet of Things (IoTs) enabled environmental and energy panel sensor data, smart home sensing data and opportunistic crowd-sourced data for energy effcient applications in a smart urban home. We present some applications where data from the IoT enabled sensors can be utilised using machine learning techniques. Prediction of small scale renewable energy using solar photovoltaic panels and environmental sensor data is used in energy management such as water heating system. Smart meter data and motion sensor data are used in household appliance monitoring applications with machine learning techniques towards energy savings. Further event detection from environmental and traffc sensor data is proposed in planning and optimising energy usage of smart electric vehicles for a smart urban home. Initial experimental results show the applicability of developing energy effcient applications using machine learning techniques with IoT enabled sensor data.

Item Details

Item Type:Refereed Conference Paper
Keywords:energy efficiency, machine learning, urban sensing
Research Division:Information and Computing Sciences
Research Group:Software engineering
Research Field:Software engineering not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Shahriar, MS (Dr Sumon Shahriar)
ID Code:117999
Year Published:2015
Deposited By:Information and Communication Technology
Deposited On:2017-06-30
Last Modified:2017-10-17

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