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Time-of-use pricing of electric energy, as an important part of the national policy of energy conservation and emission reduction, requires accurate electric energy data as support. However, due to various reasons, the electric energy data are often missing. To address this thorny problem, this paper constructs a CNN and GRU-based recovery model (RCNN-BiGRU) for electric energy data by taking the missing data as the output and the historical data of the neighboring moments as the input. Firstly, a convolutional network with a residual structure is used to capture the local dependence and periodic patterns of the input data, and then a bidirectional GRU network utilizes the extracted potential features to model the temporal relationships of the data. Aiming at the difficult selection of network structure parameters and training process parameters, an accelerated adaptive differential evolution (AADE) algorithm is proposed to optimize the electrical energy data recovery model. The algorithm designs an accelerated mutation operator and at the same time adopts an adaptive strategy to set the two key parameters. A large amount of real grid data are selected as samples to train the network, and the comparison results verify that the proposed combined model outperforms the related CNN and GRU networks. The comparison experimental results with other optimization algorithms also show that the AADE algorithm proposed in this paper has better data recovery performance on the training set and significantly better performance on the test set.

The support theory of the excavation disturbance zone (EDZ) cannot provide an accurate physical explanation or theoretical description of the time-dependent properties required for the development of an EDZ. Therefore, the primary factors that cause the formation of an EDZ should be determined to further improve the support theory of the EDZ and grasp the principle underlying the control of the long-term stability of rock masses. Considering the headrace tunnel and nuclear waste repository as the research background, this study aimed to understand the deformation damage evolution process of the surrounding rock after tunnel excavation under different working conditions using the self-developed realistic failure process analysis (RFPA2D) code. The simulation revealed the following. First, an EDZ is formed, although the deformation damage to the surrounding rock is relatively small under the action of environmental factors. Second, under the action of stress in the abovementioned case, the deformation speed, damage degree, and scope of the surrounding rock significantly increase, accelerating the formation and development of the EDZ. Therefore, the boundary of the EDZ expands significantly. Third, when environmental factors are blocked, the range of the EDZ is small due to the small deformation damage to the surrounding rock. Thus, the main factors responsible for the formation of the EDZ are environmental factors, whereas stress is only an auxiliary factor. A numerical simulation method that considers environmental factors can more accurately reproduce the formation of an EDZ. Therefore, a study of the internal mechanism of the EDZ phenomenon can provide a more in-depth understanding of the essential characteristics of an EDZ at the macro level. Furthermore, it can provide a scientific basis and method for the construction and support designs of underground excavation projects and widen the possibilities for further improving the support theory of the EDZ.

Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation’s energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data.

This paper is concerned with the output synchronization problems for a class of delayed complex dynamical networks. Based on the invariant principle of functional differential equations and Lyapunov stability theory, the feedback controller and parameter update laws are constructed for a large-scale network with uncertainties. The general complex delayed network can achieve synchronization by adaptively adjusting their feedback gains. Numerical examples are presented to further verify the effectiveness of the proposed control scheme.