This talk will present four deep learning-based models—Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a hybrid Convolutional Neural Network-Long Short-Term Memory (CNNLSTM)—to forecast electricity prices in the Nord Pool Spot Market. The approach utilizes historical electricity price data, making it a univariate time-series forecasting method. The performance of these models is assessed using various accuracy metrics and visualized through box plots. The simulation results indicate that the CNN-LSTM model consistently outperforms the other three models across most evaluation metrics.