PVT heat pump multi-source energy management method and system based on AI intelligence

Through the AI intelligent multi-source energy management method of PVT heat pump, the multivariate linear regression and time series prediction model is used, combined with the integrated heat storage/electricity storage buffer device, the problem of unreasonable resource allocation in the PVT heat pump energy regulation system is solved, and the energy utilization efficiency and system stability are improved.

CN120506737APending Publication Date: 2025-08-19华春新能源股份有限公司
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Patent Information

Application Number
CN202510612005.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-13
Publication Date
2025-08-19

AI Technical Summary

Technical Problem

The existing energy regulation system based on PVT heat pumps is difficult to allocate resources reasonably, resulting in overuse of some components, while insufficient utilization of some components reduces the comprehensive utilization efficiency of energy.

Method used

Using AI-based intelligent multi-source energy management method, the multi-source linear regression model and preset time series prediction model are used to analyze the ambient temperature and energy requirements, determine the priority coefficient, and dynamically adjust the heat storage/electricity storage integrated buffer device to achieve accurate energy allocation.

Benefits of technology

It improves the performance and stability of the energy system, can better cope with complex and changing energy demands and environmental conditions, and avoid waste of energy during transmission and conversion.

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Abstract

The embodiment of the invention discloses a PVT heat pump multi-source energy management method and system based on AI intelligence, belongs to the technical field of photovoltaic thermoelectricity, and solves the problem that an existing PVT heat pump energy adjusting system is low in energy comprehensive utilization rate. Collecting environment temperature data and energy demand information; determining a predicted energy demand value based on a multiple linear regression model; predicting the output energy of the PVT assembly and the thermoelectric module in a future period of time through a preset time sequence prediction model to obtain a predicted energy output value; according to the current environment temperature data and the predicted energy demand value, priority coefficients corresponding to the PVT assembly and the thermoelectric module are determined; determining a difference value between the predicted energy demand value and the predicted energy output value, and obtaining to-be-adjusted energy information based on the difference value and a priority coefficient; and matching the to-be-adjusted energy information with a preset heat storage / electricity storage integrated buffer device to realize dynamic adjustment of output energy.
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