### Literature Review  
Accurate load prediction is a critical component of effective demand-side load management (DSLM), especially with the unpredictable nature of EV charging patterns. Various studies have explored traditional methods, highlighting challenges in meeting user demand due to high prediction errors [7] and non-linear problems [8]. Other studies utilized ML techniques to forecast residential load demand, including EV charging. [PERSON] et al. (2021) developed a ML-based model that uses historical data and real-time inputs to predict short-term load (STL) demand, enabling more effective load management and grid balancing [9]. Similarly, [PERSON] et al. (2019) proposed a deep-learning approach to predict residential electricity demand. Their model demonstrated superior performance compared to traditional forecasting methods [10]. More recently, [PERSON] et al. (2022) applied the CEEMDAN, the SD methods, and deep neural networks to the problem of STL forecasting in EV fleets Canada-wide [11; 12]. The numerical results show that the proposed model outperformed the single and other hybrid models with the smallest error of 2.63%, highlighting the potential of ML in enhancing the efficiency of DSLM systems.  
EV charging strategies, including overnight and in the workplace, play a pivotal role in DSLM. According to a study published in Cell Reports Physical Science, strategic management of EV charging can significantly mitigate the effects of peak electricity demand and enhance solar energy utilization [13; 14]. The study suggests that overnight/delayed home charging avoids coinciding peak residential demand with peak charging demand. Workplace charging of EVs can help reduce peak electricity demand, store excess solar energy, and lower electricity costs for EV owners and grid operators [14]. Workplace  
Figure 1: Number of total new BEVS and PHEV registrations in Canada.  
charging can provide a significant amount of power to the grid, especially during daytime hours when solar generation is high and EVs are idle. One study found that workplace charging in the US could reduce the curtailment of solar electricity by half and lower the peak demand by 18% [14]. Another study predicted that workplace charging in Munich could provide up to 200 MW of power by 2030, about 20% of the city's peak load [15]. A recent study by Stanford University suggests that shifting current EV charging from home to work and night to day could cut costs and help the grid [16]. Proportions for overnight and workplace/other charging sites are influenced by user behavior, local infrastructure, and policy incentives. [PERSON] found that these proportions for homes, workplaces, and other charging sites are around 50-80%, 15-25%, and 10-20% [17].  
The effectiveness of EV charging strategies is often evaluated in terms of their impact on energy costs and the Peak-to-Average load ratio (PAR). DSLM aims to reduce the PAR of the electric grid, achieve a more balanced daily load shape, and improve system efficiency and stability. DSLM is utilized to reduce the PAR across various applications, including renewable energy integration [18] time-of-use pricing incentive schemes [19], load control in a multi-residential building [20] real-time scheduling of EVs charging [21], and future smart grid [22].The utilities consider the overall PAR ratio as an objective or a constraint in formulating the problem of price calculation to reduce the overall peak demand. Thus, peak demand minimization reduces the utility capital cost requirements [23]. The deployment of DSLM will motivate the end-users to utilize less energy during peak hours or to shift energy use to off-peak times [1,5,6], which will help the utility company reduce peak load demand and reshape the load profile.