## 5 Conclusions  
Understanding future electricity demand patterns constitutes a critical factor in ensuring the stability and security of energy systems especially in Malaysia's power market. This information proves invaluable for government authorities as they make strategic decisions and plan a power system for the sustainable growth of a country. To forecast the  
Figure 9: Convergence curve of BFOA and IBFOA for (**a**) Monday; (**b**) Tuesday–Thursday; (**c**) Friday; (**d**) Saturday; and (**e**) Sunday. electric load accurately, in this paper, a hybrid method based on LSSVM optimized by IBFOA (LSSVM-IBFOA) for daily electricity load forecasting is proposed. The Pearson correlation is utilized to analyze the correlation of present load with past load and weather. The past weather data incorporating temperature, humidity, and dew point are negligible since they show no until moderate correlation with the load. Thus, the historical load with a strong correlation is taken as the input to the LSSVM. While Pearson correlation analysis is useful for identifying linear relationships, it can be limited by its sensitivity to outliers and its inability to detect non-linear relationships. To address these shortcomings, advanced feature engineering techniques like Spearman rank correlation, random forest, and gradient boosting can be incorporated into the models to enhance feature selection accuracy in load forecasting. For parameter optimization of LSSVM, an IBFOA is proposed to overcome the shortcomings of fixed constant value in chemotaxis from the original BFOA. Comparative analyses of algorithm performance demonstrated that IBFOA significantly enhances convergence accuracy and optimization performance, as evidenced by superior convergence curves and reduced convergence times. The proposed approach, LSSVM-IBFOA, is used to forecast the daily electricity load in peninsular Malaysia during the pandemic based on the historical load data in the year 2021, considering the five-day types in a week. The simulation result demonstrates that the hybrid of LSSVM-IBFOA outperforms the DNN, LSSVM, and LSSM-BFOA with the lowest value of MAPE, MAE, RMSE, MSE, and NRMSE as well as the biggest R\({}^{2}\) for all day types. On average, the values obtained from the day type were 4.1615%, 617.6613, 730.0336, 646,979.9275, 0.0498, and 0.9464, respectively. Additionally, detailed analyses across five-day types revealed that all models exhibited the highest accuracy on Mondays and the lowest on Fridays. Consequently, future research should focus on improving the performance of these models for other days of the week. While this study focused on Malaysia, a country with a tropical climate influenced by monsoons, the proposed methodology offers potential for application in regions with four seasons. To enhance forecasting accuracy in such areas, seasonal variations should be considered, and meteorological parameters affecting load demand in each season should be identified through correlation analysis. For countries in Southeast Asia with similar climatic conditions, researchers can consider using the same meteorological parameters to identify the potential inputs to the model in the correlation analysis. Additionally, the IBFOA parameters can be adjusted accordingly to accommodate the specific characteristics of different datasets and regional variations, demonstrating the model's adaptability. The recommendations for future directions are included but not limited to:  
1. Application for LSSVM-IBFOA for forecasting on a smaller load aggregation for residential and commercial buildings or considering specific electrical loads such as air-conditioning;
2. Perform forecasting analyses using two approaches: one incorporating weather data as input features, and another relying solely on historical load data during normal periods;
3. Inclusion of sensitivity analysis on the tuning parameters of the hybrid method;
4. Adding more experiments for calculation of speed and resources of the forecasting model. F.A.Z.