## 2 Related Works
(2021) [14] explored SMP forecasting using LSTM and SVM models for one- and two-day predictions in the Turkish electricity market. Key predictor variables include historical SMP data with selected time lags for LSTM and periodic lookback values for SVM, optimizing predictive accuracy. The results revealed that the LSTM models yield MAPE values ranging from 2.97% to 19.02%, while SVM models show values between 3.93% and 15.23%, illustrating the impact of time lag and periodic lookback selection on forecast precision. In a more recent study, [PERSON] et al. (2023) [15] utilized a two-step SMP forecasting process beginning with load forecasting implemented through XGBoost and supported by Random Forest (RF), Light Gradient Boosting Machine, and Linear Regression. The research emphasized feature selection and hyperparameter optimization, yielding MAPE values from 2.70% to 6.93%. Notably, ensemble models combining LightGBM and RF delivered an outstanding performance, especially across different seasons, highlighting the robustness and accuracy of these combined approaches. [PERSON] et al. (2023) [16] presented an online machine-learning model for real-time SMP forecasting in South Korea using multiple economic indicators, including data from the transportation, energy, and monetary sectors. SVM, simple deep neural networks (DNNs), and DNN models were tested, with simple DNN performing best. Key predictors include the Baltic Dry Index, natural gas, coal, and oil prices. The model's MAPE values averaged 5.2%, demonstrating high accuracy. Real-time model updates improved forecast precision, beneficial for energy planning, and decision-making. Finally, [PERSON] et al. (2023) [17] proposed a hybrid model combining Least Squares Support Vector Machines (LSSVMs) with a Genetic Algorithm (GA) for short-term System Marginal Price (SMP) forecasting in Peninsula Malaysia. This model leverages GA to optimize both the LSSVM parameters and forecast inputs, enhancing accuracy. Predictor variables include historical SMP values from the previous three days and a week earlier and demand forecasts and generation mix forecasts covering different energy sources. The results revealed that daily forecasts achieve a MAPE of around 9.18%, while weekly forecasts show improved accuracy with a MAPE of 8.55%, demonstrating the model's potential in refining SMP prediction. This work distinguishes itself from the existing body of the literature by employing a comprehensive methodological framework that integrates state-of-the-art feature selection techniques (i.e., mRMR and MLFS) with a diverse set of statistical and machine learning-based models. Unlike previous studies that often focus on a single approach or limited feature set, this research evaluates the effectiveness of various combinations of models and feature selection methods to determine the optimal strategy for forecasting SMP under different market conditions. Moreover, this study systematically investigates the influence of macroeconomic variables and renewable generation sources, which are frequently overlooked in the literature, on SMP dynamics. The unique contributions of this work are threefold:  
* Comprehensive Evaluation of Advanced Feature Selection Techniques: This study is among the first to systematically evaluate the impact of mRMR and MLFS on SMP forecasting, demonstrating how these methods can significantly improve predictive accuracy by refining the input space and enhancing model interpretability. This approach addresses a critical gap in the literature by showcasing the value of robust feature selection in electricity market forecasting;
* Detailed Comparison of Diverse Models and Market Scenarios: The research encompasses a broad range of models, including ELM, LSTM, Bi-LSTM, ConvLSTM, and traditional statistical approaches, applied to a complex dataset that spans regular days, public holidays, and periods of extreme market volatility.