The transformation of the electricity market structure from a monopoly model to a competitive market has caused electricity to be exchanged like a commercial commodity in the electricity market. The electricity price participants should forecast the price in different horizons to make an optimal offer as a buyer or a seller. Therefore, accurate electricity price prediction is very important for market participants. This paper investigates the monthly/seasonal data clustering impact on price forecasting. To this end, after clustering the data, the effective parameters in the electricity price forecasting problem are selected using a grey correlation analysis method and the parameters with a low degree of correlation are removed. At the end, the long short-term memory neural network has been implemented to predict the electricity price for the next day. The proposed method is implemented on Ontario—Canada data and the prediction results are compared in three modes, including non-clustering, seasonal, and monthly clustering. The studies show that the prediction error in the monthly clustering mode has decreased compared to the non-clustering and seasonal clustering modes in two different values of the correlation coefficient, 0.5 and 0.6.

Variational graph auto-encoders (VGAEs) are a key tool for node clustering, but existing models face several significant challenges. These challenges include a mismatch between inference and generative models after incorporating the clustering inductive bias, as well as posterior collapse (PC), where latent representations become overly influenced by the prior distribution. In addition, in existing VGAEs, noisy clustering assignments lead to the feature randomness (FR) challenge, while the strong tradeoff between clustering accuracy and reconstruction quality results in the feature drift (FD) problem. To address these issues, we propose a multiscale contrastive VGAE (MCVGAE). This multiscale model combines cluster-level and graph-level contrastive learning with proximity-level and cluster-level self-supervised methods. MCVGAE improves the alignment between the hidden space and the data distribution and prevents PC. Moreover, it reduces FR and FD more effectively than existing techniques. Achieving impressive accuracy scores of 79.09% on Cora, 90.04% on ACM, 75.12% on Pubmed, 72.7% on Citeseer, 74.11% on DBLP, and 59.79% on Wiki clearly demonstrates the superiority of MCVGAE over 30 state-of-the-art methods.

Deep neural networks (DNNs) are significant tools for solving different problems in abundant fields. However, DNNs have large-scale parameters and weight redundancy and require high resources. Therefore, using DNNs is limited to devices such as wearable devices, mobile phones, and other edge devices that do not have enough resources to run. Using neural network pruning techniques, neural network models can be accelerated, and deep neural networks can be deployed on edge devices. However, the presence of filters with similar feature maps in each convolution layer increases redundancy, parameters, and the count of floating-point operations (FLOP) of deep models. In this paper, by combining the filter similarity algorithm and Bayesian optimization, the filters with a high percentage of similarity between their output feature maps and have a trivial effect on the model accuracy are automatically pruned. The proposed method has been applied to VGG16, ResNet20, and ResNet39 models on CIFAR10 and CIFAR100. Based on the best results, the FLOP and the count of parameters of the VGG16-CIFAR10 have decreased by 92.53% and 54.98%, and in the VGG16-CIFAR100 by 92.26% and 55.3%, respectively. Also, in the ResNet20 model, the count of parameters and FLOP operations have decreased by 31.48% and 13.89%, and in the ResNet39 model by 41.07% and 17.87%.