Urban Sensing and Smart Home Energy Optimisations: A Machine Learning Approach

Energy efficiency 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 efficient 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 traffic 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 efficient applications using machine learning techniques with IoT enabled sensor data.

[1]  Ryosuke Shibasaki,et al.  Sensing Urban Density Using Mobile Phone GPS Locations: A Case Study of Odaiba Area, Japan , 2014, ICTCC.

[2]  Il-Woo Lee,et al.  PLC-based photovoltaic system management for smart home energy management system , 2014, IEEE Transactions on Consumer Electronics.

[3]  Carmem S. Hara,et al.  An efficient data acquisition model for urban sensor networks , 2012, 2012 IEEE Network Operations and Management Symposium.

[4]  Ali Behnood,et al.  Predicting modulus elasticity of recycled aggregate concrete using M5′ model tree algorithm , 2015 .

[5]  M. Etezadi-Amoli,et al.  Practical approach for sub-hourly and hourly prediction of PV power output , 2010, North American Power Symposium 2010.

[6]  Soumaya Cherkaoui,et al.  Advanced scheduling protocol for electric vehicle home charging with time-of-use pricing , 2013, 2013 IEEE International Conference on Communications (ICC).

[7]  Il-Woo Lee,et al.  Smart home energy management system including renewable energy based on ZigBee and PLC , 2014, IEEE Transactions on Consumer Electronics.

[8]  João Gama,et al.  Functional Trees , 2001, Machine Learning.

[9]  Mirco Musolesi,et al.  Urban sensing systems: opportunistic or participatory? , 2008, HotMobile '08.

[10]  Sajal K. Das.  Participatory urban sensing: Challenges and opportunities , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[11]  Vidyasagar Potdar,et al.  Energy conservation in a smart home , 2011, 5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011).

[12]  Jeannie R. Albrecht,et al.  Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes , 2012 .

[13]  Giuseppe Lo Re,et al.  User activity recognition for energy saving in smart homes , 2013, SustainIT.

[14]  Omar Abou Khaled,et al.  Machine learning approaches for electric appliance classification , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[15]  Christian R. Prause,et al.  The Energy Aware Smart Home , 2010, 2010 5th International Conference on Future Information Technology.

[16]  Biswajeet Pradhan,et al.  Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran , 2015 .

[17]  Seth Earley.  Analytics, Machine Learning, and the Internet of Things , 2015, IT Professional.

[18]  Shan Jiang,et al.  Discovering urban spatial-temporal structure from human activity patterns , 2012, UrbComp '12.

[19]  Sung-Kwan Joo,et al.  Smart electric vehicle charging for smart home/building with a photovoltaic system , 2013, 2013 IEEE International Conference on Consumer Electronics (ICCE).

[20]  T. M. Sweda,et al.  Simultaneous vehicle routing and charging station siting for commercial Electric Vehicles , 2012, 2012 IEEE International Electric Vehicle Conference.

[21]  H. Mori,et al.  A prediction method for photovoltaic power generation with advanced Radial Basis Function Network , 2012, IEEE PES Innovative Smart Grid Technologies.