Energy storage management in smart homes based on resident activity of daily life recognition

Recently, home energy storage system is emerging as one of the main driving forces to prompt the development of the future smart grid. By leveraging time-based electricity pricing, the home energy storage system can store energy during off-peak periods and supply energy to residential customers during on-peak periods, such that the stress on main power system can be relieved. Yet, an efficient use of home energy storage system is still a challenging issue, due to the nonlinear properties of the battery in terms of energy conversion loss and shortened battery life, and the randomness in residential energy demand. In this paper, we investigate the utilization of smart home monitoring and communication technologies to recognize the resident activity of daily life (ADL) and propose a non-homogeneous hidden Markov model (NHMM) to characterize the residential energy demand. An optimal energy storage management problem is formulated by taking into account the NHMM and nonlinear battery properties. This problem belongs to a class of adaptive stochastic control problems in smart grid with nonlinear value functions. In order to solve this problem efficiently, piecewise linear approximation is applied to the energy conversion function, and a state-dependent multi-threshold policy is proposed and proved to be optimal. The performance of the proposed energy management scheme is evaluated via a case study based on CASAS smart home dataset collected in real life by Washington State University. Numerical results indicate that our proposed energy storage management scheme can achieve energy cost savings, in comparison with existing schemes with uniform and non-uniform discharging profiles.