## 5 Methods for Short-Term Load Forecasting
### Hybrid Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{
\begin{tabular}{c} **Data** \\ **Duration** \\ \end{tabular} } & \multirow{2}{*}{**Contribution**} & \multirow{2}{*}{**Benchmarked**} & \multicolumn{2}{c}{**Accuracy**} \\  & & & & **(Best-Performing** \\  & & SK & & & **Model**) \\ \hline
2002 & 1997–1999 & ANN model for peak load demand forecasting using the Levenberg–Marquardt BP algorithm. & Levenberg–Marquardt BP algorithm. & – & – & 2.87 \\ \hline
2003 & 1993–1994 & Proposed GA with new operations to optimize FNN for De Jong’s functions. & Actual load, GA & – & – & 1.56 \\ \hline
2004 & 2001–2001 & Hybrid STLF technique combines LR and ANN for improved data load forecasting. & ANN, LR & – & – & 0.62 \\ \hline
2005 & 1998–2002 & STLF model utilizes periodic time series and autoregression for load forecasting. & Actual load & – & – & \textless{}3 \\ \hline
2005 & 1995–1997 & Proposed fuzzy logic to correct neural network output for improved load forecasting. & Actual load, NN & – & – & 1.71 \\ \hline
2006 & 2000 & Hybrid FNN with CGA and SA optimizes LF parameter choices. & ANN, FNN, GA-ANNN & – & – & 1.96 \\ \hline
2008 & 2006 & Developed GRNN-based STLF model integrates prices, load, and weather data. & ANN-Fuzzy, ARMA & – & – & 1.85 \\ \hline
2009 & 1988–1992 & Hybrid forecast method combines WT, NN, and evolutionary algorithm (EA) for load prediction. & Actual load, single SVM & – & – & 2.02 \\ \hline
2009 & 1997–1999 & Hybrid genetic algorithm (HGA) optimizes SVR & GA-SVR, actual load & 7.73 & – & 0.76 \\ \hline
2010 & 2004–2006 & SVM and ACO combined for efficient STLF and improved performance. & Actual load, ANN, SVM & – & – & 1.98 \\ \hline
2013 & 2003–2004 & Two-step algorithm for STLF: WT and an ANN for primary forecasting, second step uses similar-hour method and ANFIS to enhance the results. & WNN, RBFNN, ANFIS & – & 1.60 & – \\ \hline
2013 & 2012 & Separate forecast of seasonal and trend components; evaluated using statistical tests. & Actual load, RBFs, regression models & – & – & 2.09 \\ \hline
2013 & 2010–2010 & Utilized harmony search with LS-SVM for the STLF model, GRNN analyzed influencing factors. & BPNN, LS-SVM, PSO, HS & – & – & 1.76 \\ \hline
2013 & 1994–1994 & Introduced CB-FWNN model utilizing multiplication of WNN and multi-dimensional Gaussian activation functions, with fuzzy subtractive clustering and Gaussian mixture models employed for pre-processing. & ABPA, ANFIS, RBF & 3.31 & – & 0.87 \\ \hline
2014 & 2004 & New STLF model combines WT and GP, improving SSA-Recurrent algorithm & Improved SSA, SSA-Recurrent algorithm & – & 1.96 & 3.12 \\ \hline
2014 & – & Combines BP method (to determine suitable weighting and biasing factor parameters) and MFA for better LF. & ANN, ANN-GA, ANN-PSO, ANN-FA & 2.03 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Review Based on Hybrid Methodologies for STLF. (Year: Research paper published, Data Duration: Duration of the dataset used in the research paper, Contribution: Each paper’s contribution, Benchmarked Against: Studied research papers compared their proposed model with different models and load, Accuracy: Proposed model accuracy presented in research papers).