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Precise and quick thermal load prediction for buildings is imperative in realizing the flexibility of building energy systems. Operation optimization based on the prediction results can effectively mitigate the energy consumption and operation cost. Here, an artificial neural network (ANN) model is built to predict the load demand and energy consumption of an office building. Thermal zones division is considered to improve the prediction accuracy. Measured data are collected to establish and validate the ANN model. The total number of samples for training, validation and testing are 13674, 3978 and 3977, respectively. A rule-based control optimization model is introduced and combined with ANN model to optimize the operation of heating, ventilation and air-conditioning system. Building energy flexibility is activated by optimization model in a time-of-use electricity price scenario. The results indicate that ANN model has high precision and the coefficient of variation of root mean squared errors corresponding to the load demand prediction and energy consumption prediction are 10.76% and 15.59%, respectively. Furthermore, the optimization results can reduce the operation cost by 39.22% and 44.41% in the heating and cooling season, respectively.

The flow stress, microstructure and softening mechanism of NM550 wear-resistant steel were investigated via hot compression test by using Gleeble-1500 thermal simulator in a temperature range from 800 to 1150 °C and strain rate range from 0.1 to 10s−1. With optical microscopy (OM), the effects of temperature, strain rate and true strain on the flow behavior and microstructure of the steel were discussed. The results show that the dynamic recrystallization occurs at higher temperature and lower strain rate. Hot deformation behavior of the steel specimens is sensitive to temperature and strain rate, while the strain rate affects the hot deformation behavior greatly. The apparent stress exponent and the apparent activation energy of the specimens are 7.395 and 389.611 kJ/mol, respectively. With the experimental data, the hot deformation constitutive equation and processing map of the NM550 wear-resistant steel are given. Finally, a combination of the constitutive equation and processing map provides a method for comprehensively investigating the hot deformation behavior of the steel under different conditions, and provides theoretical basis for the determination of rolling procedure in industrial production.

Practice is one of the effective ways to develop innovate ability of students. In order to solve the gap between professional theoretical knowledge in the teaching process and ability of analyzing and solving practical problems, changing the traditional teaching mode, adopting case teaching method, highlighting a problem in the case of multiple solutions, and integrating multiple knowledge related can broaden the depth and breadth of knowledge of students and truly improve students' thinking skills and the ability of innovation.