## 4 Results and Discussion
### Case Study  
In this study, the electricity load of Peninsula Malaysia during the COVID-19 pandemic was used as a case study for evaluating the efficiency of the proposed model in disrupted situations. The hourly electricity load demand in megawatts (MW) in 2021 was used as raw input data. Figure 5 depicts the monthly average electricity load demand in 24 h, illustrating the characteristics of the electricity load pattern. During the year of pandemic (2020 and 2021), the electricity load fluctuated and did not follow the trend of the pre-pandemic (2019). For instance, in March 2020, the electricity load dropped by 23.54% compared to the same month in 2019. This is due to the enforcement of the initial movement control order (MCO) 1.0 on 18 March which closed non-essential business operations and restricted human daily activities to control the spread of the virus. The MCO 2.0 was enforced in the early months of 2021 but failed to achieve a significant reduction in COVID-19 cases as it was only enforced in certain states. Therefore, the full nationwide lockdown (MCO 3.0) was implemented in June 2021 due to the rise in COVID-19 cases. Consequently, the average electricity consumption in June 2021 was 1109 MW lower than the average consumption for the same month in 2019, which represents a reduction of 7.69%. From October 2021, a recovery in the load demand can be seen with the further easing of some restrictions and allowing some activities to continue. The COVID-19 pandemic is considered a crisis event due to the unprecedented measures implemented and the demand pattern during the pandemic is expected to be different than the previous periods. Thus, electricity demand needs to be forecasted considering the fluctuating demand pattern. Although the impact of the pandemic is obvious, accurate forecasting remains essential for effective market operations and system planning [66]. Therefore, in this paper, the load demand from January until December 2021 is taken as the case study to observe the ability of the LSSVM-based model to forecast the future load patterns based on past load behavior during the pandemic. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Variables** &
\begin{tabular}{c} **Pearson Correlation Coefficient (\(r\))**, **(load and Model Inputs)** \\ \end{tabular} \\ \hline
**1** & Last day relative humidity (\%) & \(-0.4351\) \\ \hline
**2** & Last two days’ relative humidity (\%) & \(-0.4965\) \\ \hline
**3** & Last week relative humidity (\%) & \(-0.5302\) \\ \hline
**4** & Last day temperature (\({}^{\circ}\)C) & \(0.4874\) \\ \hline
**5** & Last two days’ temperature (\({}^{\circ}\)C) & \(0.5653\) \\ \hline
**6** & Last week temperature (\({}^{\circ}\)C) & \(0.5676\) \\ \hline
**7** & Last day dew point (\({}^{\circ}\)C) & \(-0.0945\) \\ \hline
**8** & Last two days’ dew point (\({}^{\circ}\)C) & \(-0.2429\) \\ \hline
**9** & Last week’s dew point (\({}^{\circ}\)C) & \(-0.0421\) \\ \hline
**10** & Last day load (MW) & \(0.7762\) \\ \hline
**11** & Last two days’ load (MW) & \(0.7256\) \\ \hline
**12** & Last week load (MW) & \(0.6457\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Correlation coefficients between the load and the model inputs (variables). Seasonality in Load  
To capture daily and weekly seasonal patterns, a common approach involves decomposing load data into different day types, each exhibiting a unique load profile. Previous research has established the existence of multiple day types, which are typically identified through a combination of forecaster expertise and clustering methodologies. Typical similar day-based STLF methods predict the load of a target day by leveraging the load profile of a historically similar day, complemented by relevant external factors [67]. Classifying the historical load data by the day of the week will aid in improving the accuracy of STLF since the loads on weekdays are probably higher than on weekends [68].