Urban Sensing and Smart Home Energy Optimisations
Author:
Affiliation:
1. CSIRO Digital Productivity Flagship, Hibart, Australia
2. Tampere University of Technology (TUT), Tampere, Finland
Funder
Department of state growth Tasmania
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/2820975.2820979
Reference21 articles.
1. http://solar-energy.uq.edu.au/. http://solar-energy.uq.edu.au/.
2. Predicting modulus elasticity of recycled aggregate concrete using m5p model tree algorithm;Behnood Ali;Construction and Building Materials,2015
3. Pietro Cottone Salvatore Gaglio Giuseppe Lo Re and Marco Ortolani. User activity recognition for energy saving in smart homes. Pervasive Mob. Comput. 16(PA):156--170 2015. 10.1016/j.pmcj.2014.08.006 Pietro Cottone Salvatore Gaglio Giuseppe Lo Re and Marco Ortolani. User activity recognition for energy saving in smart homes. Pervasive Mob. Comput. 16(PA):156--170 2015. 10.1016/j.pmcj.2014.08.006
4. Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran
5. Participatory urban sensing: Challenges and opportunities
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