###### Abstract  
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology. deep learning; home energy management system; Q-learning; reinforcement learning solar photovoltaic; vehicle-to-home Article  
A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit. Article  
[PERSON], [PERSON], [PERSON], [PERSON]. Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit. Article  
merging the electric motor productivity map based on collected data with the motor productivity experiment and a current closed-loop motor design to overcome limited energy storage. The study [4] proposed a thorough control method intended to address all usual scenarios, employing a hierarchical design to achieve four-wheel independent control, which will simultaneously improve driving performance and reduce energy consumption. Aside from conventional vehicles, all independent chassis actuators drive-by-wire enable dynamic enhancement of EVs to assist the driver in enhancing handling and cornering stability. The study [5], introduced a multilevel integrated control with control allocation and motion controller to enhance the performance by reducing the tire effort on each wheel. Moreover, a survey of vehicle control for autonomous, connected, and automated EVs was conducted in [6]. The analysis included the techniques, essential elements, collaborative EV control strategies, and possible uses of cooperative driving control techniques, that could increase the sustainability, safety, and efficiency of the future transportation system. Therefore, incorporating EV batteries into smart buildings is a technique for optimizing cost reduction [7; 8] and enhancing the usage of energy sources. Thus, the novel vehicle-to-home (V2H) technology is appealing due to its ability to reduce electricity prices in the home. In addition, when the SG is overloaded, V2H technology transfers the peak load and supplies constant emergency lighting [9]. According to the research conducted by [PERSON] et al., the development of EV technology by focusing on discharging modes, namely, vehicle-to-grid (V2G), vehicle-to-building (V2B), and V2H, has been evaluated [10]. In the discharging modes, EVs discharge their batteries and distribute the excess energy to the power grid, buildings, or residences. The authors argued that the V2G mode is less efficient than V2B and V2H due to the significant transmission power losses.