## 5 Methods for Short-Term Load Forecasting

Table 5: Overview of Statistical Models for STLF. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Models** & **Advantage** & **Disadvantage** \\ \hline \hline \multicolumn{3}{c}{Machine Learning Models} \\ \hline ANN & Powerful prediction, discovers non-linear relations. & Low interpretability, limited scalability. \\ \hline DL & Improved initial values, general purpose learning. & Training process could get stuck at bad local optima. \\ \hline MLP & Higher accuracy and superior for non-linear problems. & Local minima affect training; slow convergence. \\ \hline SVM & Accurate even with non-separable datasets. & Large memory utilization, high computational cost. \\ \hline ELM & Fast training with good generalization. & Generality degradation and ambiguity hinder ELM. \\ \hline SOM & Superior interpretation with few training samples. & Scalability issues; inputs must be vectors. \\ \hline DT & Non-parametric with scalability for handling large data. & Poor prediction performance. \\ \hline RNN & Processes any length of time-series data. & Slow recurrent computation. \\ \hline LSTM & Addresses RNN issues with long-term memory. & High memory bandwidth demand, overfitting. \\ \hline AN & Simplifies problems via numeric subdivision functions. & Elusive completeness, identifies all alternatives. \\ \hline \multicolumn{3}{c}{Expert Systems} \\ \hline GP & Minimizes external factors, less reliance on historical data. & Unreliable prediction effects with large datasets. \\ \hline FCMs & Superior error estimation index. & Sensitive to local minima, lengthy computation. \\ \hline FRBS & Short development time, no design parameters required. & Difficult to recognize optimal FRBS. \\ \hline FR & Mitigates LR limits, studies system’s structural uncertainty. & Problematic tasks; guidelines, setting accuracy. \\ \hline \multicolumn{3}{c}{Metaheuristic Systems} \\ \hline ABCO & Efficient for multi-variable optimization and flexibility. & Needs new fitness experiments for new factors. \\ \hline AIS & Decodes and encodes information efficiently. & Limited potential of extrapolation. \\ \hline GA & Does not break easily in presence of moderate noise. & Not scalable with complexity. \\ \hline FA & Able to find local and global optima synchronously. & Convergence speed is low and accuracy is imprecise. \\ \hline CSA & Low parameter setting and easy to implement. & Slow convergence rate. \\ \hline MA & Improves fitness using local research in GA. & Complexity linked to problem’s extrapolation. \\ \hline GSA & Precision in solving adaptive non-linear optimization. & Slow iteration’s search, post-convergence inactivity. \\ \hline SAA & Evades local maxima and finds near-optimal solutions. & Sensitive to input, slow optimal solution. \\ \hline HSA & Quick convergence and fewer modifiable parameters. & Needs improved parameter tuning for diverse applications. \\ \hline PSO & High training speed, efficient search algorithm. & Slow convergence, global optimum search issues. \\ \hline ACO & Parallelized concurrent processing and derivative free. & Uncertain convergence time. \\ \hline CASO & Overcomes untimely local optimum concern of SVR. & Insufficient impact of organization variable on ant. \\ \hline DE & Homogeneity around the mean compared to GA and PSO. & Unstable convergence risk, local optimum trapping. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Overview of Key Hybrid Models for STLF.