An indoor personnel state feature and multiple energy demand estimation method based on information mining

By combining data-driven and physical models and using information mining techniques to characterize human behavior, a hybrid model was established, which solved the problem of low accuracy in building energy consumption prediction and achieved more accurate energy demand prediction and management.

CN116521759BActive Publication Date: 2026-02-13DALIAN UNIV OF TECH
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Patent Information

Application Number
CN202310493794.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-05-04
Publication Date
2026-02-13
Estimated Expiration
2043-05-04

AI Technical Summary

Technical Problem

Existing building energy consumption prediction models cannot accurately reflect the impact of individual differences and human factors, resulting in low accuracy in energy consumption prediction and an inability to achieve refined management.

Method used

By combining data-driven models and physical models, and using information mining techniques to characterize the impact of human behavior, a hybrid model is established to predict building energy demand.

Benefits of technology

It improves the accuracy and adaptability of building energy consumption forecasting, and supports the supply and demand matching of multi-energy supply systems and the refined control of HVAC systems.

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Abstract

The application discloses an indoor personnel state feature and a variety of energy demand estimation method based on information mining, and belongs to the field of building energy consumption accurate prediction.The application applies data mining technology, applies data expression to the influence of "people", and adds the simulation platform based on the traditional physical model, so that the problem that the traditional physical model cannot depict human behaviors is overcome.Meanwhile, the method can better perform model migration on different buildings, so that the data-driven model can better perform generalization.
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