Load prediction and room temperature feedback correction-based whole network balance adjusting method

Abstract

The invention provides a load prediction and room temperature feedback correction-based whole network balance adjusting method. Most conventional heat supply adjustment is adjustment of a heat sourceor a heating station, and the extensive adjustment mode often makes fluctuation of room temperature be larger and even wastes energy sources. The adjusting method provided by the invention increases adjustment research of whole network balance based on the heat source or the heating station. The target of whole network balance adjustment achieves supply and demand matching and pipe network stabilizing of primary and secondary networks by adjusting parameters of a heat supply system. In order to achieve the target, it is needed to determine an adjustment and control period and a time node to predict the period of heat supply parameters dynamically. Centered by a target value of room temperature of a user, feedback correction is carried out on the predicted water supply temperature by meansof room temperature, and finally, the load prediction and room temperature feedback correction-based whole network balance adjusting method is formed. Fluctuation of room temperature adjusted by the method is relatively gentle, and phenomena of overheating and undercooling are alleviated obviously, and the energy-saving effect is obvious.

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 suppl

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