Intelligent temperature control method and system based on human thermal sensation

Abstract

The invention discloses an intelligent temperature control system and method based on human thermal sensation. The method includes the steps of: quantifying the comprehensive heat history temperature value according to user preset information and indoor and outdoor air temperature historical data; calculating the CaPMV model value according to the heat history temperature value; The indoor temperature is regulated according to the CaPMV model value. The present invention comprehensively considers the quantitative index and method of natural ventilation heat history, heating and air conditioning heat history, and outdoor air temperature heat history, and makes up for the fact that the existing intelligent control air-conditioning system based on PMV index only considers objective thermophysical parameters and does not consider the influence of crowd heat history Insufficient, it can more accurately evaluate the thermal environment comfort of heating and air-conditioning under the conditions of diverse thermal experiences of the population, and provide a scientific basis for the control of heating and air-conditioning systems with decentralized and intermittent characteristics in hot summer and cold winter regions.

Description

technical field [0001] The invention relates to the technical field of air conditioner temperature control, in particular to an intelligent air conditioner temperature control method and system based on human thermal sensation. Background technique [0002] Since modern humans spend most of their time indoors, the thermal comfort level in a building is closely related to the good state of people's life and work. The thermal comfort of the human body is not only affected by temperature, but also by other factors such as humidity, wind speed, and radiation temperature. Therefore, as early as around 1960, Fanger proposed the PMV (Predicted Mean Vote) index model, which was used to predict the thermal comfort level of the average population based on physical parameters such as thermal environment. Building control system. Due to the diversity of indoor and outdoor thermal environments in different climatic regions and seasons, people have formed corresponding different degrees...

AI Technical Summary

Problems solved by technology

[0003] Traditional indoor temperature intelligent control methods based on thermal sensation prediction, such as PMV indicators, do not consider the influence of human thermal experience factors, and are only suitable for thermal comfort evaluation and temperature control in central cooling and heating environments.

Although the existing adaptive thermal comfort model method considers the thermal experience of the human body, it only evaluates and regulates the indoor temperature based on the outdoor temperature value, without considering the influence of the thermal experience of indoor heating and air conditioning, and is only suitable for the thermal environment evaluation of naturally ventilated buildings.

For non-centralized heating and air-conditioning environments, the thermal experience of the human body is varied due to the influence of indoor and outdoor diverse thermal environments, but the existing technology still lacks accurate thermal comfort evaluation and system control methods for this

Specifically, for people with different heating, air-conditioning and outdoor comprehensive thermal experiences, the current technology lacks scientific quantification methods of human thermal experience, and corresponding indoor cooling and heating control system products and control methods based on quantified values ​​of human thermal experience.

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