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 stora. 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.••••Dynamic SHEMS forecast over one week was proposed to enhance energy consumption.••SHEMS model based on a hybrid PV/GES and dynamic electricity pricing was performed.••Proposed SHEMS model has a substantial effect on lowering electricity bills.••Forecasting model errors vary between 13.4 %–23.2 % for RMSE and 4.1 %–11.3 % for MAPE.The increasing concerns about the environmental effects of traditional energy sources and fossil fuels finite live, have shifted emphasis to renewable energy sources [1,2]. These latter significantly contribute to reducing greenhouse gas (GHG) emissions and traditional energy consumption based primarily on electric grid supply. Recent statistics prove that buildings, and particularly the civic sector, require more than 40 % of the total energy consumed in comparison with other sectors. A significant portion of this consumption could be met by the integration of renewable energy systems combined with energy storage technologies. Different sources of renewable energy can be integrated into buildings to cover the heating, cooling, and electrical needs of the occupants. Among the most widely used renewable energy resources, solar energy draws increasing attention for building applications as a way to achieve sustainable buildings. Solar energy is collected by photovoltaic (PV) modules or thermal panels in buildings. The amount of energy gained is considerably affected by the weather conditions, mainly the magnitude of solar radiation, which output intermittent energy and therefore requires support from energy storage systems. However, the integration of such decentralized and intermittent power technologies with variable capacity into the traditional electrical power system will create a new challenge for the stability and reliability of the electric grid. These crucial challenges have prompted aca. The SHEMS infrastructure consists of a SHEMS center, smart meters, communication and networking systems, and other smart devices (see Fig. 1). Through these smart infrastructures, SHEMS can access, monitor, manage, and improve the functioning of various distributed generator sources (renewable energy systems, energy storage, as well as the electric grid), electric vehicles, and household appliances. In addition, the SHEMS supports two-way communication between smart home users and grid utilities. SHEMS should be more flexible in managing and controlling smart home appliances, renewable energy resources, and energy storage systems in order to participate in electricity conservation and demand response. The SHEMS employed in this current study is composed of a dynamic PV forecast model, GES state of charge forecast model, the electricity price, and the scheduled load on the horizon for one week. Fig. 1 illustrates the concept of SHEMS investigated in this study.The control system within the SHEMS is responsible for balancing the distribution of power between various sources and loads. The system initially prioritizes powering the loads using the electricity generated by the photovoltaic system. PV power is utilized to meet the energy demands of the loads within the house. If the PV production exceeds the immediate energy requirements of th. The effectiveness of the proposed model is validated by the case study presented in this section. The energy management system used is based on a forecast model of a hybrid PV/ gravity energy storage system. The forecast model considers the prediction of weather conditions, PV system production, and gravity energy storage state of charge in order to cover the load profiles scheduled over one week. The investigated house is located in Madrid, Spain.The aim of this model is to optimize the house's consumption. In addition, the charging of the car's battery is only based on the energy price variation. In this case, neither the PV system nor GES are used to charge the battery of the electric vehicle. However, for all the used loads, PV installations as well as GES will be considered for the supply of energy. Those loads' scheduling can be described as a linear optimization problem that aims to either minimize consumption at the peak pricing hours or increase consumption at peak PV and GES generation hours.In the presented scenarios, household appliances are supposed to account for the majority of electricity consumption. Appliances are divided into two categories: shiftable and non-shiftable equipment. The model will place emphasis on shiftable equipment because the user's satisfaction must be cont.
