###### Abstract  
The global electric vehicle (EV) market is experiencing exponential growth, driven by technological advancements, environmental awareness, and government incentives. As EV adoption accelerates, it introduces opportunities and challenges for power systems worldwide due to the large battery capacity, uncertain charging behaviors of EV users, and seasonal variations. This could result in significant peak-valley differences in load in featured time slots, particularly during winter periods when EVs' heating systems use increases. This paper proposes three future charging strategies, namely the Overnight, Workplace/Other Charging Sites, and Overnight Workplace/Other Charging Sites, to reduce overall charging in peak periods. The charging strategies are based on predicted load utilizing a hybrid machine learning (ML) approach to reduce overall charging in peak periods. The hybrid ML method combines similar day selection, complete ensemble empirical mode decomposition with adaptive noise, and deep neural networks. The dataset utilized in this study was gathered from 1000 EVs across nine provinces in Canada between 2017 and 2019, encompassing charging loads for thirty-five vehicle models, and charging locations and levels. The analysis revealed that the aggregated charging power of EV fleets aligns and overlaps with the peak periods of residential buildings energy consumption. The proposed Overnight Workplace/Other Charging Sites strategically reduce the Peak-to-Average Ratio (PAR) and energy cost during the day by leveraging predictions made three days in advance. It showed that the PAR values were approximately half those on the predicted load profile (50% and 51%), while charging costs were reduced by 54% and 56% in spring and winter, respectively. The proposed strategies can be implemented using incentive programs to motivate EV owners to charge in the workplace and at home during off-peak times.  
electric vehicle; charging loads; residential building: overnight; workplace/other charging sites; charging strategies; peak-to-average ratio; energy cost; machine learning +
Footnote †: journal: Applied sciences  
growing adoption of electric vehicles (EVs) is expected to significantly impact power demand in residential areas, potentially increasing peak loads by 30% [6].  
Peak electricity demand in residential buildings typically occurs in the evening, aligning with the peak charging time for electric vehicles (EVs). In winter, colder temperatures negatively affect EV battery performance by reducing battery efficiency, which leads to longer charging times and higher energy consumption. This increased electricity demand is compounded by heightened heating needs during colder months, further contributing to overall load spikes on the grid. As a result, winter periods often see a convergence of higher residential and EV loads, placing significant strain on energy infrastructure. Managed charging strategies could be crucial in reducing the strain on electrical grids and optimizing energy use as EV adoption rises. Despite the rapid expansion of the EV market and infrastructure, utilities and power generators face a major challenge in developing effective charging strategies for residential EV fleets to mitigate peak electricity consumption and rising energy costs.