Smart Home Energy Management Supported by Cloud Computing

Authors

Abstract

The current work studies optimal management of electrical appliances in smart homes. An appliance scheduling problem is addressed via optimisation approaches with the goal of minimising the overall energy bill over a week. Scheduling of electrical appliances must take into account non-controllable domestic demand, grid prices and local energy production by photovoltaic panels. Here, a preliminary cloud architecture is proposed to support smart home sensor data collection, storage, processing and scheduling computation. Optimal schedules achieve a balance between local energy production and grid prices, exploring periods of higher generation to attenuate non-controllable demands and favouring night periods (during which energy is cheaper) for controllable appliances operations.

Keywords

appliance scheduling problem, building management systems, cloud computing, Cloud computing, Cloud Computing, Computer architecture, controllable appliances operations, Cyber-Physical Systems, demand side management, domestic appliances, electrical appliances, energy management systems, grid prices, Home appliances, home automation, Home Energy Management Systems, local energy production, Mixed-Integer Programming, noncontrollable demands, noncontrollable domestic demand, optimal management, optimal schedules, Optimal scheduling, optimisation, optimisation approaches, power engineering computing, preliminary cloud architecture, Processor scheduling, Production, scheduling, smart home energy management, smart homes, Smart homes, Smart Homes

Conference

2019 5th Experiment International Conference (exp.at'19) 2019

DOI