###### Abstract  
The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply-demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods. system marginal price; machine learning; feature selection; xgboost; extreme learning machine; ARIMA; SARIMA; predictive modeling +
Footnote †: journal: Applied sciences  
by a complex web of factors, including fluctuations in energy demand, variability in supply sources, policy interventions, and broader macroeconomic conditions. Such factors introduce significant volatility into SMP values, making precise forecasting a challenging yet indispensable task for energy market participants [3,4]. Accurate SMP forecasting is crucial for several reasons. For energy producers, it enables optimal resource allocation by determining when to deploy different generation units, thereby maximizing profitability. Producers can schedule their lower-cost baseload plants during periods of stable demand and activate higher-cost peaking units only during anticipated price surges, based on SMP predictions [5]. Similarly, distributors and consumers can leverage SMP forecasts to adjust their procurement strategies, hedging against price volatility and reducing overall energy costs. From a regulatory perspective, SMP serves as a barometer of market efficiency and competitiveness. Deviations in SMP can signal market inefficiencies or the need for policy interventions, such as capacity payments or infrastructural upgrades, to maintain system stability. Thus, accurate SMP forecasting not only supports operational decisions but also aids in the strategic planning and regulation of the electricity market [6,7]. In the Turkish electricity market, SMP holds strategic importance due to the unique characteristics of the market structure and the rapid integration of renewable energy sources. With its growing reliance on imported natural gas and the ongoing transition toward a more sustainable energy mix, the Turkish electricity market is particularly susceptible to volatility in fuel prices and exchange rates [8]. Additionally, the implementation of policy measures such as the Renewable Energy Support Mechanism (YEKDEM) has introduced further complexities into SMP calculations.