Locational marginal pricing (LMP) in a deregulated electricity market is a result of bidding competition from nodes (locations). This paper presents a deep learning neural network for the 24 h-ahead LMP forecasting of a specific node in a zone at the PJM power market using other zonal prices. The presented method uses a Convolutional Neural Network (CNN), which is a well-known architecture for deep learning that has been applied successfully for image classification and pattern recognition. The CNN is used herein to extract features of real-time electricity prices by convolution, kernel, and pooling operations using spatiotemporal electricity prices in various zones and locations. The optimal parameters of the model were determined using a step-by-step manual approach to minimize the burden of brute force trial and error methods. Datasets from the PJM power market are used in simulations in which the proposed method is applied using Tensorflow and Keras Library. Various approaches are compared, and the results thus obtained demonstrate that the proposed method has higher accuracy than traditional methods in 24 h-ahead LMP forecasting.