A heating adaptive control method and system based on microclimate prediction

By adopting a heating adaptive control method based on microclimate prediction, future microclimate data and building heat information are obtained to generate an operation plan for the heating unit and make real-time corrections. This solves the problems of high heating energy consumption and decreased control performance in prefabricated buildings in cold regions, and achieves high efficiency, energy saving and stable heating.

CN122305535APending Publication Date: 2026-06-30HEBEI CONSTR GRP
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HEBEI CONSTR GRP
Filing Date
2026-05-28
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing heating control methods for prefabricated buildings in cold regions cannot adjust heating strategies in advance based on future microclimate changes and building heat storage characteristics, resulting in high heating energy consumption and decreased model control performance.

Method used

By acquiring microclimate prediction data for future periods, and combining it with the predicted heat storage and energy efficiency coefficient sequence of buildings, an operation plan for the heating unit is generated. An adaptive control algorithm is used to make real-time corrections, and a closed-loop feedback update mechanism is set up to correct the model parameters.

Benefits of technology

It achieves the goal of fully utilizing natural heat sources, reducing energy consumption, improving control precision and stability, and maintaining high energy efficiency in the long term, while ensuring indoor thermal comfort.

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Abstract

This application discloses a heating adaptive control method and system based on microclimate prediction, relating to the field of heating control technology. The method includes: acquiring microclimate prediction data for a future period; acquiring a predicted stored heat sequence based on predicted solar radiation intensity and outdoor temperature sequences; acquiring a predicted energy efficiency coefficient sequence based on predicted outdoor temperature and relative humidity sequences; calculating the target heat to be supplemented by a second heating unit based on the predicted stored heat sequence; converting the target heat into an operating plan for the second heating unit in the future period; making real-time adjustments to the operating plan; and generating execution commands to control the second heating unit to provide heating. This solves the technical problem of existing technologies being unable to adjust heating strategies in advance, leading to high heating energy consumption and decreased model control performance.
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