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During product development it is very important to do research in comparing the system functionality throughout its working operation and when unexpected parameters occur. In modelling of the system behaviour, the uncertain effects of the system due to the influence of the changing working environment was also considered here. The observed system was presented with its two models: structure model and behavioural model. The system structure model was represented in matrix notation with a Design Structure Matrix (DSM). The second domain of the system, i.e. its behaviour, was modelled by the Model Predictive Control (MPC) method and by studying the system stability using the second Lyapunov method. The mutual transfer of information between these two domains allows the creation of system variants suitable for working in uncertain situations. By combining the developed environment (Behaviour Prediction Framework) and its algorithm, and using existing computer tools (©LOOMEO and ©MATLAB), the research results were verified. This was done with a case study of an air handling unit used for air treatment in the food industry and clean rooms. While carrying out the simulation of the behaviour of the air conditioning system, the following facts about influential parameters were established. The most influential parameter for the systems stability was the environment temperature from −40 to −25 °C. The reason was that heat transfer coefficient was then very large. In addition, high air flow rate in range from 3.6 to 7.8 m3/s, was had also a great influence on stability. System instability was happened with smaller geometries (the cross-sectional area – height ×weight) of the air handling unit. [ABSTRACT FROM AUTHOR]

Under the trend of global low-carbon development, reasonable and accurate prediction of electricity consumption plays an essential role in vigorously adjusting power system structure, promoting electrification, and other energy-saving and emission reduction measures. Considering the development trend of energy consumption, this paper introduces the Logistic model of energy structure into the system structure, and establishes a novel grey prediction model with system structure. According to the division of energy factors with similar attributes, this model seeks the internal relationship of the development of electricity consumption and describes the interaction between related factors and multiple main factors in the form of equations, which makes the model have better applicability and stability. In the validation part, the ten types of energy are divided according to their attributes, and the main factor group and the related factor group are distinguished. The model proposed in this paper is used for simulation and prediction, and is compared with the three types of models (six models). In the two cases, the simulation error of the new model is as low as 3.9790%, and the prediction error is 0.5645%. Compared with other models, the new model has shown good performance in the case of electricity consumption forecasting in China. The effectiveness of the optimization of the model in structure, background, and application is verified. At the same time, based on the analysis and prediction of China's consumption data, this paper gives relevant policy suggestions for developing China's power structure. • A novel model based on energy background (EPMGM(1,M,N)) is established. • The relationship of factors in the system are characterized by the equations. • A Logistic model based on energy structure is introduced to the new model. • China's electricity consumption is effectively predicted by the model. [ABSTRACT FROM AUTHOR]