## 1 Introduction
### Literature Review
(2018) forecasted solar power using the GRU-Temporal Fusion Transformer Model and the DILATE loss function. They also compared their results with different deep learning-based and traditional machine learning algorithms such as XGB Boost, N-BEATS, and N-HiTS. [PERSON] et al. (2018) used graph convolutional networks for forecasting locational marginal prices to make bidding strategies. [PERSON] et al. (2018) proposed a forecasting model of four successive stages based on neural networks and an optimization framework for battery energy storage systems. Their focus was the Single Electricity Market in the island of Ireland. [PERSON] et al. (2018) conducted an empirical study on Turkish markets for electricity price estimation, focusing on normal and COVID-19 pandemic periods. They used the Transformer Encoder-Decoder with Self-Attention (TEDSE) model. [PERSON] et al. (2018) discussed transfer learning opportunities for artificial neural networks to predict the next 24 steps of hourly data. [PERSON] et al. (2018) compared various machine learning approaches including deep neural networks (DNNs) and convolutional neural networks (CNNs) on day-ahead electricity markets. [PERSON] et al. (2018) proposed a model using a combination of bi-directional LSTM and CNNs to improve predictions. They used data from Canada, France, Italy, Japan, Brazil, Mexico, and Turkiye. [PERSON] et al. (2018) suggested a novel methodology for probabilistic energy price prediction in day-ahead markets based on Bayesian deep learning techniques. [PERSON] et al. (2018) presented the usage of attention-based LSTM networks trained by crisscross optimization for electricity price prediction. [PERSON] et al. (2018) proposed a hybrid approach integrating LSTM-based deep learning with feature selection algorithms to forecast day-ahead electricity prices considering market coupling effects. Their study underscored the significance of accurate prediction in the context of European market harmonization and demonstrated the effectiveness of their approach, particularly highlighting the impact of features from integrated markets, with the German market playing a prominent role in Nord Pool price generation. [PERSON] et al. (2018) put forward a straightforward method, leveraging copula techniques, to generate multivariate probabilistic forecasts for day-ahead electricity prices based on univariate point forecasts. Their approach accounted for the dependence structure across hours and demonstrated applicability across five benchmark datasets, showcasing its potential for constructing realistic prediction intervals for various energy market scenarios. [PERSON] et al. (2018) proposed a novel hybrid deep learning-based model, combining convolutional neural networks and stacked sparse denoising auto-encoders, along with an improved decomposition method called complete ensemble empirical mode decomposition with adaptive noise, to enhance day-ahead electricity price forecasting accuracy. Their experimental results on the Australian national electricity market demonstrated superior prediction performance, particularly in forecasting price spikes, while also offering faster convergence speed compared to traditional neural network models, thus providing valuable insights for electricity market participants.