Research On Modeling And Optimal Control Of Heating Substation Based On Deep Learning

With the advancement of science and technology and the improvement of living standards,people are paying more attention to the improvement of the quality of life while pursuing the material foundation.Therefore,the requirements for the heating quality of buildings are also increasing in the winter.China's heating technology started late.Although heating systems have been popularized in most urban areas in the north,there are still some gaps in control technology compared with other developed countries.The system still has a supply-demand mismatch on the user's heat supply,making the energy utilization rate not high.Aiming at the problem of uneven distribution of heat supply for multiple heat substations,this paper proposes a control scheme for heat distribution of multiple heat substations based on deep reinforcement learning.This paper mainly focuses on the research of the primary network of the heating system.Firstly,based on the historical data of the ten heat substations on a heat source main line of a heating company in Baotou,the temperature data of the China Meteorological Website is collected and the heat supply prediction model is established.BP neural network and long-term memory neural network are used to establish the short-term heat load forecasting model for heat substations.By comparing the heating value,the prediction model of the heat substation based on the long-term memory neural network is more accurate.At the same time,the heat output value of the model output is also used as part of the performance function of the subsequent optimization.Then the LSTM neural network is used to build the heat substation model,and the generalization ability test is carried out on the heat substation model.The results show that the model works well.Finally,the depth deterministic strategy gradient control algorithm is used to achieve optimal heat distribution.The heat substation model provides the environment model for the optimal control system,and the heat supply value and the predicted heat supply calculated by the heat supply and actual working conditions output by the control algorithm.The quantity is compared,and the relevant parameters in the control algorithm are corrected to optimize the solution,and then the optimized primary water supply flow sequence is solved.In this paper,the deep reinforcement learning algorithm is used to study the heat distribution optimization problem of the heat substation.The machine learning is referenced to the control field,which lays a foundation for the further development of the control field and provides reference experience for the control research of other similar scenes.