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Urban gas pipeline network short-term load forecasting is important for the security and stability of gas distribution dispatching system. In order to improve the forecast precision, this study adopts an integrated model of empirical mode decomposition, phase space reconstruction and least squares support vector machine, i.e., EMD-PSR-LSSVM, for urban gas pipeline network short-term load forecasting. Firstly, EMD is used to decompose the original nonlinear time series into several uncoupling intrinsic mode functions. Then, PSR is used to make the selection of LSSVM input/output-layer units. Furthermore, particle swarm algorithm is used to optimize the model parameters and train LSSVM with temporal sequence samples, the trained LSSVM will be used for regression forecasting in advanced. Finally, the original loading data of Zhengzhou is adopted as example for empirical analysis. Results indicate that the EMD-PSR-LSSVM model has a higher outcome as compared to BP neural network and LSSVM regression, which has demonstrated the proposed integrated model is efficient and consistent. [ABSTRACT FROM AUTHOR]

This study introduces the multi-agent modeling method to establish five categories of agents, i.e. government agent, customer agent, gas enterprise agent, real time monitoring agent and human-computer interaction agent. Using this multi-agent system, we simulate the dynamic change process of demands and running states caused by the price changes of natural gas in the urban natural gas pipeline network. Moreover, Zhengzhou is taken as a case to simulate the hourly gas-usage behavior of industrial and commercial users under TOU pricing policy. The results show that: 1) An optimal peak-valley price relationship allows both the gas operator and users to benefit at the same time, and the peak-valley load difference can be reduced effectively. 2) With the increase of the peak price, the benefit of industrial and commercial users increases, while the gas operator's benefit decreases firstly and then increases. And only when the peak price reaches a threshold value, the gas operator can benefit. [ABSTRACT FROM AUTHOR]

Based on coal mine hydrogeological conditions, the causes and evolution mechanism of coal mine water hazards were presented. By using complex systems theory and multi-agent modeling method, an evolutionary model of coal mine water hazards was developed. Then simulated the evolution process with different system conditions by Netlogo platform, and the coal mine water hazards process and vulnerability relationship were demonstration dynamical. The research shows that, the coal mine water hazards, influenced by multiple factors, is a complex and adaptive phenomenon that possesses nonlinear dynamic characteristics. Only by understanding the mine hydrogeological conditions, use complex systems theory and multi-agent modeling method, the mechanism of coal mine water hazards can be uncovered in nature. [ABSTRACT FROM AUTHOR]