Abstract—This work proposes a smart home electricity
management approach that can predict and schedule electricity
demand and supply by considering: the ‘state’ of the smart grid,
local power generation capacity, and electrical consumption of
household appliances. The prediction of weather conditions and
the immediate and longer-term plans of the residential home
occupants are crucial parameters in the smart home
decision-making system that acts on behalf of the occupants.
This paper provides a motivation example and associated
scenarios, electrical energy supply/demand models,
formalization of the cost optimization problem, and scheduling
schemes for a smart home electricity management system in the
context of a smart grid, smart appliances, and local renewable
energy resources. A case study is provided to illustrate how the
proposed approach works.
Index Terms—Smart grid, renewable energy, smart home,
demand side energy management.
Weiliang Zhao, Lan Ding, and Paul Cooper are with the Sustainable
Building Research Centre, Faculty of Engineering, University of
Wollongong, NSW, 2522, Australia (e-mail: {wzhao, lding, pcooper}@
uow.edu.au).
Pascal Perez is with the SMART Infrastructure Facility, University of
Wollongong, NSW, 2522, Australia (e-mail: pascal@ uow.edu.au).
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Cite:Weiliang Zhao, Lan Ding, Paul Cooper, and Pascal Perez, "Smart Home Electricity Management in the Context of Local Power Resources and Smart Grid," Journal of Clean Energy Technologies vol. 2, no. 1, pp. 73-79, 2014.
