Intelligent energy management system for smart home with grid-connected hybrid photovoltaic/ gravity energy storage system

Abstract

A dynamic smart home energy management system (SHEMS) is proposed in this study to address the growing concerns of energy conservation and environmental preservation. This study contributes a novel one-week dynamic forecasting model for a hybrid PV/GES system integrated into a smart house energy management system, encompassing dynamic electricity pricing, smart appliance control, PV generation forecasting, and gravity energy storage state of charge prediction. The findings of this study demonstrate that the developed dynamic SHEMS model significantly reduces household energy use and lowers the cost of power. With this SHEMS model, the hybrid PV/GES can supply the house's energy needs for eight and a half hours each day. In addition, it offers the advantage of low electricity price for charging the battery of the electric vehicle. Performance indicators such as RMSE and MAPE are employed, yielding forecast error results ranging from 13.45 % to 23.16 % for RMSE and 4.06 % to 11.27 % for MAPE.