Research On Auto-control Method For Electric Heatng Building Under Power Shortage

With the in-depth reform of the national power system and the promotion and popularization of building energy efficiency,the share of intelligent electric heating systems in the Chinese heating market has gradually increased.Electric heating will eventually develop into another major form of heating following centralized heating and gas heating.There are still many problems in the automatic control and heating distribution in China's existing electric heating operation mode.In order to solve these problems,according to the characteristics of electric heating buildings,this paper proposes an automatic control operation method for electric heating under under-power conditions,and validates the method using the data simulated based on the actual operating conditions.The paper's research on the automatic control operation method mainly includes the following four aspects:1.In order to accurately describe the temperature variation characteristics of the room during heating,this article analyzes the room temperature change curve in practical application,finds the law of temperature change,and establishes the heating model.Electric heating The heating device in the building is a thermostat,combined with features such as adjustable temperature setting of the thermostat and ability to keep the temperature within a certain range,etc.,to establish a mathematical model of the room thermostat,thereby establishing a heating control system.2.Based on fuzzy reasoning,neural network algorithms and other intelligent computing methods,a neural network fuzzy reasoning algorithm for calculating temperature variation constants is constructed.The algorithm collects the natural influence factors such as orientation,height and position,and sets the temperature constant of each room according to the expert knowledge and the experience of the actual heating building room.The model is trained on the basis of empirical data,and the effect of the model is tested using new data.The simulation results show that the temperature variation constant algorithm can accurately calculate the temperature variation constant value of each room.3.Decision Control Algorithm Design.According to all the factors affecting heating buildings,a decision-making control algorithm is established.Due to the lack of electric power in the electric heating building,it is not possible to satisfy the simultaneous heating of all rooms.The purpose of the decision-making control algorithm is to control the opening and closing of the thermostats of all rooms in real time under the condition of satisfying the electric power tolerance,not only achieving the power requirements,but also maximizing the user's heating experience.The algorithm uses the room's real-time temperature,real-time power to calculate the heating frequency during heating,the temperature rise(cooling)time,and the difference between the real-time temperature and the set temperature,using the heating frequency,temperature(cooling)time and difference to calculate the priority of the room level.According to the ranking of priorities,the order of turning on or off the thermostats of all rooms in the whole electric heating building room under real-time working conditions is analyzed.By analyzing the overall heating system to reach the set temperature of the consistency,stability,power range and other evaluation indicators,the control effect of the proposed method to achieve the desired goal.4.Predicting the design of room heating priority algorithms.Considering that the temperature variation constant of the room is time-varying during the actual heating process,it may be inaccurate to use the linear combination of the heating frequency and the real-time temperature and the set temperature difference as the real-time priority of the room.Based on historical data,the algorithm predicts future heating patterns in rooms and corrects room priorities to optimize the overall electric heating control effect.This paper selects NAR neural network as a method to predict the heating priority,which can accurately predict the change of priority.By comparing the evaluation indicators of the system before and after adding the prediction results,the analysis shows that the algorithm after adding the prediction results has better performance in terms of temperature consistency,stability,and power variation range.