In this work, energy time series forecasting competitions from the Schneider Company, the Kaggle Online platform, and the American society ASHRAE were considered. These competitions include power generation and building energy consumption forecasts. The datasets used in these competitions are based on reliable and real sensor records. In addition, exogenous variables are accurately added to the dataset. All of these ensure the richness of the information contained in the dataset, which is crucial for energy management. Therefore, (1) We choose to study forecast models suitable for energy management on these energy datasets; (2) Forecast models including popular algorithm structures such as neural network models and ensemble models. In addition, as an innovation, we introduce the Explainable AI method (SHAP) to explain models with excellent performance indicators, thereby strengthening its trust and transparency; (3) The results show that the performance of the integrated model in these competitions is more stable and efficient, and in the integrated model, the advantages of LightGBM are more obvious; (4) Through the interpretation of SHAP, we found that the lagging characteristics of the building area and target variables are important features.

Direct ESS has some disadvantages, which are seen even in the case of repeated games when the sequence of stage ESSs may not constitute the direct ESS in the repeated game. We present here the refinement of the ESS definition, which eliminates these disadvantages and represents the base for the definition of ESS in games in extensive form. The effectiveness of this approach for multistage n-person games is shown for metagame (this notion is used for the first time), in which under some relevant conditions, the existence of ESS is proved, and ESSs are constructed using threat strategies.

Based on the SEIRD model, we consider that when multiple viruses of different virulence coexist, the more virulent virus will reinfect nodes already infected by the less virulent virus, which we call here Superexposed. Based on the state transitions, the corresponding differential equations and cost functions are established, then building the corresponding optimal control problem, where the vaccine efficiency and drug efficiency are controlled variables. This nonlinear optimal control problem is solved by Pontryagin’s maximum principle to finding the structure of the optimal control strategies. Based on the definition of the basic regeneration number, yielding the R0 value for the model, then discussed the final epidemic size. In the numerical analysis section, we validate the accuracy of the structure, fitting the behavior of each state and the effect of different parameter values.

In this paper, we consider two independent systems based on epidemics. The evolutionaryepidemic system focuses on human decision behavior, while the impulse control-epidemic system focuses on allocation management in government departments. Evolutionaryepidemic system contains two procedures corresponding to human protection decisions during the period without vaccine and vaccination decisions during the period with vaccine. Factors such as the ability of the virus to spread, the cost of taking protective measures, and the side effects of vaccination all influence people’s decisions. Factors that artificially influence epidemic behaviour are not only the individuals decision, but also the involvement of the Epidemic Prevention and Control Centre. Although medicine can be effective in suppressing the spread of epidemics, mass delivery of medicine depletes enormous resources. We use regular treatment as a means of control, attenuating infection rates and transmission, thus slowing the progression of the epidemic. Based on the maximum principle of pulse control, we derived the optimal set of solutions for discrete-time impulse control. Finally, numerical simulations are used to confirm the theoretical results.

This study is a survey of several extended models based on SEIRD epidemics. The main contribution of the review is a modification of the classical SEIRD epidemic model, from single-layer to multi-layer to super-exposure, and from general vaccine to pre-emptive vaccine to twostage evolutionary-epidemic model. Assuming the presence of two viruses in a population, simultaneous and non-simultaneous occurrence of the two viruses was compared; assuming super-exposure between multiple viruses, general and pre-emptive vaccines were compared; and assuming that individuals have decision-making power over vaccination, the effect of the basic number of infections on the evolutionary stabilisation strategy was investigated. A series of numerical experiments support the theoretical results obtained.