The forecasting of electricity sales is directly related to the power generation planning of power enterprises and the progress of the generation tasks. Aiming at the problem that traditional forecasting methods cannot properly deal with the actual data offset caused by external factors, such as the weather, season, and spatial attributes, this paper proposes a method of electricity sales forecasting based on a deep spatio-temporal residual network (ST-ResNet). The method not only relies on the temporal correlation of electricity sales data but also introduces the influence of external factors and spatial correlation, which greatly enhances the fitting degree of each parameter of the model. Moreover, the residual module and the convolution module are fused to effectively reduce the damage of the deep convolutional process to the training effectiveness. Finally, the three comparison experiments of the ultra-short term, short term and medium term show that the MAPE forecasted by the ST-ResNet model is at least 2.69% lower than that of the RNN and other classical Deep Learning models, that its RMSE is at least 36.2% lower, and that its MAD is at least 34.2% lower, which is more obvious than the traditional methods. The effectiveness and feasibility of the ST-ResNet model in the short-term forecasting of electricity sales are verified.

In this study, we study the load frequency control (LFC) problem for interconnected multiarea power systems (IMAPSs) with quantization and actuator failure. To effectively reduce the amount of data in the channel, input signals will be quantized before being transmitted from a controller to a system through the digital communication channel. To reveal the asynchronous phenomenon between the original plant and LFC with actuator failure, a hidden semi-Markov model is formulated. In addition, the stability of the jump system under network attack is discussed. On the basis of the Lyapunov theory, sufficient conditions are derived to ensure the stochastic stability of IMAPSs. Finally, the validity of the theoretical results is tested via a simulation example.

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