Power Management by LSTM Network for Nanogrids

Abstract

Nanogrids can be considered smart grids that are implemented for small-scale buildings, houses, and apartments. A typical power management framework for nanogrids determines the scheduling of operations of electric appliances for each time interval with objectives related to total power consumption and total delay due to scheduling. Such a framework of power management has limitations in accommodating future operating conditions of nanogrids. Taking future outdoor temperature as a future operating condition, a proactive power management for nanogrids is presented in this paper. The goal of proactive power management for nanogrids is to achieve the proper level of indoor temperature in a cost-efficient way, sooner rather than later, by taking into account future outdoor temperature. To achieve this goal, a long short-term memory (LSTM) network is used as the controller. Simulations have been performed to verify the performance of the proposed power management. The results of the simulations demonstrate that living comfort measured in terms of room temperature is enhanced while the overall electricity cost is reduced, mainly due to the ability of the LSTM network to predict the trend of outdoor temperature.

Keywords

• power management
• nanogrid
• peak load shifting
• LSTM network
• shiftable appliance