## 2 Related Works  
In recent years, significant advancements have been made in the field of SMP forecasting, with researchers leveraging a variety of computational methods to enhance predictive accuracy and reliability. Table 1 provides a detailed examination of notable studies conducted in recent years, showcasing the diverse approaches and methodologies employed to tackle the complexities of SMP forecasting. These studies utilize a range of techniques from traditional statistical models to advanced machine learning algorithms, each contributing uniquely to the evolving landscape of electricity price forecasting. The synthesis of findings from these studies highlights the continuous improvements and innovations that characterize this vital area of research. In their research, [PERSON] et al. (2017) [10] implemented Artificial Neural Networks (ANNs) to project day-ahead hourly market clearing prices and marginal prices in the Turkish electricity market. The models were trained on historical pricing data, aiming to minimize the daily mean absolute percentage error. During the forecasting process, key input variables such as historical load, forecasted load, historical SMP, historical System Day-Ahead Price (SDAP), forecasted SDAP, historical Net Volume Index (NVI), and \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Authors** & **Year** & **Predictor Variables** & **Methods** & **MAPE Values (\%)** \\ \hline [PERSON] et al. [10] & 2017 & Historical SMP, load, SDAP, NVI & ANN & 9.77–21.81 \\ [PERSON] et al. [11] & 2017 & Historical SMP, weather values & AdaBoost, SVM, BPNN & 3.84–8.43 \\ [PERSON] et al. [12] & 2019 & Historical SMP & ANN & 5.36 \\ [PERSON] and [PERSON] [13] & 2019 & Historical SMP & FFNN & 7.42–9.22 \\ [PERSON] et al. [14] & 2021 & Historical SMP & LSTM, SVM & 2.97–19.02 \\ [PERSON] et al. [15] & 2023 & Forecasted load, date, fuel price, fuel capacity, Historical SMP & XGBoost, RF, LightGBM, LR & 2.70–6.93 \\  & & Baltic Dry Index, natural gas, coal, crude oil, US dollar index, exchange rate & SVM, DNN & 4.70–5.20 \\ [PERSON] et al. [17] & 2023 & Historical SMP, demand forecast, generation mix forecast & LSSVM, GA & 8.55–9.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of the literature on SMP forecasting in recent years. forecasted NVI were employed to enhance the accuracy and reliability of the predictions. The approach included enhancements like volatility analysis and the application of the Bienayme-Chebyshev inequality, allowing for probabilistic forecasting within specified boundaries, which resulted in MAPE values ranging from 9.77% to 21.81%. [PERSON] et al. (2017) [11] developed an ensemble learning model utilizing AdaBoost, enhanced with SVM and Back Propagation Neural Networks for short-term SMP forecasting. This method focused on analyzing primary factors affecting short-term electricity prices and proved to be more effective than traditional models, evidenced by MAPE values between 3.84% and 8.43%, demonstrating the method's substantial improvements in predictive accuracy. The 2019 study by [PERSON] et al. [12] featured an ANN tailored for day-ahead SMP forecasting that integrated a combination of long-term and short-term historical data. Optimization was achieved through k-fold cross-validation. Input variables were meticulously selected using the Pearson correlation coefficient and discrete Frechet distance, leading to a notably low MAPE of 5.36%, which marked an improvement over standard forecasting models. [PERSON] and [PERSON] (2019) [13] explored the potential of Feedforward Neural Networks (FFNNs) to forecast the System Marginal Price in the Greek energy market. Their research involved testing various FFNN configurations to understand the influence of network parameters on prediction outcomes. Their results indicated a MAPE between 7.42% and 9.22%, with Adaptive Neuro-Fuzzy Inference System models demonstrating particularly low error rates, suggesting their effectiveness for this application. [PERSON] et al.