Research On Heat Supply Predictive Control Of Heating Station Based On Neural Network Method

In north areas of China, heating energy consumption has achieved 27.2 percent of total national energy consumption. In fact, energy consumption is not only huge, but also inefficient. Nowadays, the heating energy consumption per unit area is 2 to 3 times than developed countries. In order to implement national policies of energy-saving, it is very important to study the energy-saving monitoring system. Based on above reasons, the heating energy-saving control strategy and corresponding monitoring system are studied in this dissertation. Two projects are taken as background, which are"National Eleventh Five-year Plan Key Project of Ministry of Science and Technology in 2006: Research of Key Technology for Building Energy-saving and Its Engineering Demonstration"(Grant No. 2006BAJ01A04) and"Heilongjiang Province Research Project in 2005: Research of Heat Supply Field Control System Based on LonWorks Technology and Predictive Control Theory"(Grant No. GC04A104). There are some problems to be considered: ensuring the high-quality heating while taking energy-saving as target; Heat supply system is a complex dynamics process; Reducing the product costs when applying advanced technology.As accurate mathematical models are foundations of heat supply system analysis and control, the heat supply process is dynamically divided into the certain part and the random part, which models are established separately. The model of the certain part is established by combining mechanism analysis and experimental methods, while the random part of heat supply process model is fitted with ARMA model. The heat supply process modeling is the premise of heat supply decoupling and predictive control.Heat load forecasting provides the basis for heating energy-saving. According to the characteristics of nonstationary, nonlinear, time-varying of heat load, several forecasting methods are proposed in this dissertation, which based on time series method, maximum entropy method and neural network theory. The random series stabilized is forecasted by time series method, the random series unstabilized is forecasted with the maximum entropy method, and the nonlinear characteristic is matched with neural network method. To improve the accuracy, crossover forecasting theory is introduced into heat load forecasting. With this method, the household demands are tracked by vertical forecasting, and the outdoor temperature is tracked by the horizontal forecasting. Finally, the performances of above algorithms are analyzed by comparing the simulation results. The coupling effect exists between quality-adjust and quantity-adjust channels. Firstly, judgment methods of coupling degree are given to analysis the static and dynamic coupling. Then, two non-interference SISO independent systems are obtained by using conventional decoupling method and decoupling method based on recurrent RBF neural network with delays respectively. The input dimensions of neural network are estimated by modified false neighborhood method. In addition, the static and dynamic performances of the methods are validated by simulation.As predictive control can meet the heating characteristics of nonlinear, changeful, time-delay, uncertain, the predictive control is applied in heat supply. The basic DMC and GPC algorithms are improved: improved DMC algorithm combined with model simplified and predictive error correction algorithm is applied to decrease computational complexity, and solve the model mismatch problem; Besides this, control algorithm of implicit adaptive GPC with improved identification algorithm is given to improve real-time ability. Further more, an intelligent predictive control method based on neural network is studied, and its deviation control algorithm along with control rate solving algorithm are presented.Finally the engineering application is studied. Through software and hardware design, scheme of heat supply monitoring system based on GPRS and PLC is given. The stability and reliability of control system is guaranteed by advanced technology and components selection. The device prototype achieved the design indexes and passed the technical inspection of national quality inspection department by tested in a substation.