A whole network balance adjustment method based on load forecasting and room temperature feedback correction

Abstract

The invention proposes a whole network balance adjustment method based on load prediction and room temperature feedback correction. Most of the traditional heat supply adjustments are adjustments to heat sources or heat stations. This extensive adjustment method often causes large fluctuations in room temperature and even wastes energy. The control method proposed in this paper, on the basis of considering the adjustment of heat sources and heat stations, adds the adjustment research on the balance of the whole network. The goal of network-wide balance adjustment is to achieve the matching of supply and demand of the primary and secondary networks and the stability of the pipe network by adjusting the parameters of the heating system. In order to achieve this goal, it is necessary to determine the control cycle, adjust the time node, and perform periodic dynamic prediction of heating parameters, and center on the user's room temperature target value, and use the room temperature to make feedback corrections to the predicted water supply temperature, and finally form a model based on load forecasting and room temperature. The whole network balance adjustment method based on feedback correction. After the method is adopted, the fluctuation of the room temperature is relatively gentle, the phenomenon of overheating and overcooling is obviously reduced, and the effect of energy saving is remarkable.

Description

Technical field: [0001] The invention relates to the field of heat supply regulation, in particular to a whole network balance regulation method based on load prediction and room temperature feedback correction. Background technique: [0002] In order to meet the user's heating demand and save energy, the heating system needs to be dynamically adjusted as the load changes. Choi et al proposed a controller based on load forecasting model in "Mathematical modeling and control of thermal plant in the district heating system of Korea", with heat supply and boiler outlet temperature as output parameters. The results prove that the control algorithm can effectively save energy. Smrekar et al established a neural network model for coal-fired boilers in "Development of artificial neural network model for a coal-fired boiler using real plant data", which is used to predict the mass flow rate, pressure and temperature of boiler outlet steam. The above studies only consider the heat ...

AI Technical Summary

Problems solved by technology

In actual regulation, most of the heat sources or heat stations are adjusted solely based on the experience of technicians and feedback from heat users, but lack of consideration for the balance of supply and demand in the entire network, and most of them rely on experience when faced with "how to adjust and when to adjust" , there will often be insufficient heating or excessive heating, wasting energy

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