Big data heat storage heating control method and system based on autonomous learning

Abstract

The invention discloses a big data heat storage heating control method based on autonomous learning. The method comprises the following steps: (S1) with one period of time as unit, a heating station influence parameter information acquiring point is set; (S2) a weather state curve is built; and actually acquired weather environment temperature information is input in the weather state curve; (S3)a PID adjusting model is set for adjusting a water outlet temperature of a heating station; and a pipe flow of the heating station is set as a fixed value; and (S4) an autonomous learning model is set; and the data information acquired in the steps (S1-S3) is input in the autonomous learning model for real-time control of a heat release quantity Q of the heating station.

Description

technical field [0001] The present invention relates to the field of distributed heating control, in particular to a large data heat storage heating control method based on autonomous learning, which is jointly affected by supply and return water temperature, ambient temperature and humidity, water supply flow, and uncertain factors. Background technique [0002] The heating system first requires that on the premise of meeting the minimum heat demand of all end users, we hope that the heating station can minimize energy consumption, so as to achieve the purpose of saving energy. If we can always satisfy that the room temperature of the end user is higher than the set value, the total heat absorbed by the user side is the smallest, which not only meets the heat demand of the user, but at the same time the indoor temperature is suitable, and can save the heat consumption of the heating station to the greatest extent, saving energy save electricity. However, since the heating ...

AI Technical Summary

Problems solved by technology

Method 1. By designing the return water temperature at different time periods and adjusting the return water temperature to achieve the effect of constant temperature and room temperature. However, due to the serious hysteresis of the return water temperature, the control algorithm is complicated, and the expected control results cannot be achieved.

The second method is to design the water supply temperature in different time periods and adjust and control the water supply temperature. This kind of control is relatively easy. Through conventional PID and some auxiliary algorithms, the water supply temperature can be well controlled to the set value, but the water supply temperature and room temperature are not corresponding. , under the same heating temperature, the room temperature can change in a wide range, but the purpose of adjustment cannot be achieved

The current two methods cannot control the set value of the end user's room temperature very well, and the set value of the outlet water / return water temperature is based on the experience of the process operator, and some heating places require the end user's temperature to be different for a period of time There are different values ​​or heat supply is required in different time periods. In this case, the calculation is more complicated. It is more difficult for the conventional method to control the real-time room temperature of the end user, which often results in a large waste of heat energy.

Images