## 3 Load Forecasting
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Beneficiaries**} & \multicolumn{5}{c}{**Impact on Beneficiaries**} \\ \hline \multirow{2}{*}{**Benefits**} & \multirow{2}{*}{**Oracle**} & \multirow{2}{*}{**Oracle**} & \multirow{2}{*}{**Oracle**} & \multirow{2}{*}{**Oracle**} & \multirow{2}{*}{**Oracle**} & \multirow{2}{*}{**Oracle**} \\  & & & & & & & & \\ \hline Ahead-of-load demand & ✓ & - & ✓ & ✓ & - & - & Aids decision-making. \\ \hline Planning and execution & ✓ & - & ✓ & ✓ & - & - & Plan and make decisions to avoid disruptions. \\ \hline Maintaining infrastructure & ✓ & - & ✓ & ✓ & - & - & Prioritize infrastructure advancements. \\ \hline Purchasing and conserving fuel & - & - & ✓ & ✓ & - & - & Pre-negotiation and warehouse preparation. \\ \hline Resource calculation & ✓ & - & ✓ & ✓ & - & - & Management of spinning and non-spinning reserve. \\ \hline Asymmetrical energy preparation & ✓ & - & ✓ & ✓ & - & - & Production facilities and uncertainties management. \\ \hline Development of facilities & - & - & ✓ & ✓ & - & - & Improvements in generation and resource addition. \\ \hline Continuous power supply & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & Intelligent power supply management. \\ \hline Calculation of theft and loss & ✓ & - & ✓ & - & - & - & Disclosing problems that are overloading the system. \\ \hline Contribution of renewable energy & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & Permits efficient integration of RESs. \\ \hline IPP contribution & ✓ & - & ✓ & ✓ & ✓ & ✓ & Prevent cascading collapse effects. \\ \hline Digitalization in industry & ✓ & ✓ & ✓ & ✓ & ✓ & - & Intelligent forecasting-based operational decisions. \\ \hline Implementation of smart cities & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & Intelligently manage distributed energy resources. \\ \hline Market bidding and investment & ✓ & - & ✓ & - & - & - & Efficient handling of bids and investments. \\ \hline Decline in CO\({}_{2}\) emission & ✓ & ✓ & ✓ & ✓ & - & - & RES integration leads to reduction in CO\({}_{2}\) emission. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages of Traditional and Modern Power Grid Load Forecasting Along with The Impact on Beneficiaries. LTLF addresses electrical demand planning for an extended horizon, supporting system comprehension and ensuring a reliable power supply. It enables accurate forecasting of infrastructure changes, impacting grid reliability, customer satisfaction, and utility investments [54]. MTLF includes planning for grid maintenance, considering electricity pricing, managing energy sharing agreements, and fuel preparation, typically over a month to a year horizon [55]. STLF is critical to the day-to-day management of the utility, ranging from minutes to weeks. It has a significant impact on financial savings and operational risk reduction. STLF techniques include statistical methods such as LR, ARIMA, and ES, as well as advanced approaches such as ANN, FL, SVM, RNN, and LSTM [56]. Hybrid models, such as GWDO with MMI and FCRBM, have been proposed for improved accuracy. The combination of LSTM and CNN has shown improved results [57]. Notable ML algorithms such as SVM, RF, and LSTM have been evaluated, with a combined model proposed for superior STLF [58]. Several advanced techniques have been proposed for STLF. For instance, in [59], the chimp optimization methods were coupled (CLFIF-IL-ChOA), combining iterative learning, fractal interpolation function, parameterization, and chimp optimization, which showed superior adaptability compared to other methods. A decision tree (DT) approach achieved 1.90% MAPE in predicting short-term electricity demand, reducing forecast variability [60]. A wavelet-based extreme learning machine (ELM) addressed load fluctuation challenges by automatically determining hidden neurons based on input samples [61]. A multi-layer bidirectional RNN outperformed regression methods in estimating monthly electricity consumption [62]. Hybrid models, CNN, and LSTM networks showed promise, with hybrid models performing best, followed by CNN.