Bayesian network-based regional heat supply model predictive control system and method

Abstract

The invention discloses a Bayesian network-based regional heat supply model predictive control system and method. The method includes the following steps that: step S1, physical layer heat network data sensing is performed: historical data are acquired from a source side, a network side and a building side in real time and are updated; step S2, a Bayesian network is constructed according to the historical data and on the basis of prior knowledge, and the load demand of a thermal station and the building side are predicted through the Bayesian network; step S3, the real-time control parametersof a secondary side, a primary side and the source side are obtained through the inference of the Bayesian network according to the load demand of the building side and on the basis of the historicaloperation data and real-time data; and step S4, time characteristic curves of source side adjustment, network side adjustment and building side adjustment are established according to the historical operation data and pipe network topology structures, and source side adjustment, network side valve and building side electric valve adjustment strategies are determined, control operations are performed according to the strategies, so that the hysteresis of heat network adjustment can be eliminated, real-time supply and demand balance can be met, and the accurate on-demand heat supply of a heat user side can be realized.

Description

technical field [0001] The invention belongs to the advanced control field of heating systems, and is one of the main foundations for realizing intelligent heating. It specifically relates to a district heating model predictive control system and method based on Bayesian networks. The models established based on historical large data sets on the source side, heat station, and network side are used to achieve the supply and demand balance of the heating system and the heat users according to their needs. Precise control is required. Background technique [0002] The traditional central heating system has the characteristics of strong coupling, large lag, and thermal inertia, as well as the two core problems of imbalance between supply and demand, that is, under the traditional "source-grid-load" heating framework, the fluctuation of building heat load and the behavior of indoor residents are different. Unbalanced matching of supply and demand caused by determinism and single...

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Problems solved by technology

[0003] The purpose of the present invention is to provide a district heating model predictive control system and method based on Bayesian network, which can predict the future short-term heat load on the source side and the building side of the thermal station based on historical big data and control the regulation parameters of the source side and the thermal station , to solve the problem of unbalanced supply and demand of the heating network and inflexible regulation, and realize on-demand and precise heating at the user side

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