## 4 Results and Discussion
### Load Forecasting Results  
The following section presents an analysis of the performance of LSSVM in conjunction with BFOA and IBFOA based on different evaluation criteria, as listed in Table 5. In this paper, four models, namely DNN, LSSVM, LSSVM-BFOA, and LSSVM-IBFOA, were utilized to forecast the daily electricity load in Peninsular Malaysia for short-term analysis. The LSSVM model was trained using the RBF as the kernel function, while IBFOA was employed to optimize the parameters of LSSVM. The input from historical load data was separated into 5-day types in a week as follows:  
* Day type: Monday
* Day type: Tuesday, Wednesday, Thursday (Tuesday-Thursday)
* Day type: Friday
* Day type: Saturday
* Day type: Sunday  
Forecasts for Tuesday, Wednesday, and Thursday of each month were aligned due to their similar load curve patterns. Figure 7a-e compared the forecast load demand from DNN, LSSVM, LSSVM-BFOA, and LSSVM-IBFOA models with the actual load demand for each day type in the testing dataset for four weeks. Considering 5-day types, each model generated one-day-ahead load profile forecasts for four consecutive weeks, from the third week of December 2021 to the second week of January 2022 (15 December 2021-14 January 2022) for short-term analysis. From the observation of actual load, the load demand fluctuates depending on the time of day, starting from modest levels in the early morning, rising as the day progresses, and slowing down as night approaches. Also, the load demand curve on weekends, especially Sundays, appears different compared to the weekday (Monday to Friday) load demand curve. The Sunday load profile exhibits a distinct pattern with a higher starting demand from 12:00 a.m., a gradual decrease until 8:00 a.m., and a subsequent rise until 7:00 p.m. The data separation highlights the importance of incorporating predefined day types in the forecasting model. Figure 7: Cont. Figure 7: _Cont._  
The simulations show that the forecasted load curve by the four models on Monday has the most similar pattern to the actual load curve as depicted in Figure 7a. An analysis of the average absolute differences reveals a clear advantage for the LSSVM-IBFOA model in consistently following the actual load profile across all four weeks. Particularly, LSSVM-IBFOA, LSSVM-BFOA, LSSVM, and DNN achieved the average absolute differences across the four weeks with values of 1.42%, 1.95%, 3.99%, and 6.04%, respectively. However, there is a gap between the forecasted load curve and the actual load curve at peak times of the day, especially on Friday and Tuesday-Thursday, which has the largest forecasting error value. The significant discrepancy between the forecasted and actual load, with all forecasting models underestimating demand is observed on the Friday load profile as shown in Figure 7c. Examining the average absolute differences across the four weeks' reveals that all models exhibited underestimation with the highest on the first week, with DNN (12.45%) showing the greatest deviation, LSSVM (10.27%), followed by LSSVM-BFOA (9.43%) and LSSVM-IBFOA (lowest\(-\)8.95%). For the weekend load profiles, while all forecasting models exhibited some level of underestimation, particularly during peak hours, the deviations were generally lower compared to other weekdays (Friday and Tuesday-Thursday). Table 9 provides a detailed comparison of error metrics for the forecasting models evaluated on the 2021 testing dataset, categorized by day type (Monday through Sunday) for total of 96 sampling points. DNN outperforms LSSVM on Tuesday, Thursday, Friday, and Sunday, as indicated by lower error metrics. However, LSSVM exhibits superior performance on Monday and Saturday. These findings suggest that hybridizing LSSVM with IBFOA could potentially enhance its overall performance. Through the analysis, LSSVM-IBFOA shows the consistently highest reductions in error metrics across all day types compared to DNN, LSSVM, and LSSVM-BFOA.