Abstract
This paper presents a review on the Home Energy Management Systems (HEMS) for renewable energy production and used optimization methods. The HEMS is an important Smart Grid application. It is used to monitor and optimally manage the energy flows in buildings including renewable energy production, energy storage and smart home appliances. In this paper, two different methods for the optimal HEMS are selected and compared: Model Predictive Control (MPC) and Reinforcement Learning (RL). As a conclusion, the RL method can overcome the disadvantages of the MCP in the highly dynamic environment of buildings and renewable energies, and is a promising method for HEMS in Smart Grids. Finally, an experimental set-up of the hybrid renewable energy system is presented and its operation is discussed under the Time-of-Use energy management strategy.
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