Method and system for online prediction on cooling load of central air conditioner in marketplace buildings
A technology of central air conditioning and prediction method, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as difficulty in meeting real-time requirements and increasing model training time.
Active Publication Date: 2015-06-03
SOUTH CHINA UNIV OF TECH
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Problems solved by technology
[0004] However, due to the continuous increase of air-conditioning load forecast samples over time, the model training time increases with the increase of the number of samples, and it is difficult to meet the real-time requirements of forecasting
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[0157] Such as figure 1 As shown, the hourly cooling load online prediction system for central air conditioners in shopping malls in this embodiment includes a cold source data acquisition system 1, an air conditioner terminal equipment intelligent node device 2, a passenger flow counting device 3, an outdoor meteorological data acquisition device 4, and a data transfer Server 5, air-conditioning cooling load prediction data server 6; air-conditioning terminal equipment intelligent node device, used to collect the average dry-bulb temperature of the building's air-conditioning open area, the average relative humidity of the building's air-conditioning open area, and the weighted opening rate of the air-conditioning terminal equipment; the air-conditioning terminal equipment intelligent node The device includes a fan coil intelligent node device, an air conditioning unit intelligent node device and a fresh air unit intelligent node device.
[0158] Such as figure 2 As shown, ...
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The invention discloses a method for online prediction on cooling load of a central air conditioner in marketplace buildings. The method comprises the following steps of: continuously acquiring various parameters; when the quantity of acquired data meets the needs, respectively building prediction models of the air conditioner cooling load and all input parameter values in three types including working day, weekend and festival and holiday by using an Online SVR (Support Vector Regression) method; then, predicting the input parameter value of 24 hours of that very day according to the historical data of outdoor meteorological parameter and air conditioner operation input parameter; finally, predicting the air conditioner cooling load within 24 hours of that very day by using the air conditioner load prediction model of corresponding date type and taking the predicted value of each input parameter within 24 hours of that very day as the input, and compensating by a residual error sequence of the actual value and the predicted value of the air conditioner cooling load in the previous day of the corresponding date type; and simultaneously, dynamically correcting the prediction model of the air conditioner cooling load with online addition of new samples. The method provided by the invention effectively realizes the dynamic and accurate prediction on the air conditioner cooling load.
Description
technical field [0001] The invention relates to an online central air-conditioning cooling load prediction method, in particular to a method and system for dynamically predicting the central air-conditioning cooling load of a shopping mall building based on online monitoring of passenger flow. Background technique [0002] The central air-conditioning system is a major energy consumer in shopping malls. Some large and medium-sized shopping malls with high energy consumption account for as much as 70% of the total building energy consumption. The energy consumption of air-conditioning directly affects the operating costs of shopping malls. Moreover, the central air-conditioning system has the characteristics of non-linearity, large time lag, and large inertia. Conventional control technology is likely to cause large temperature fluctuations in the air-conditioning area of commercial buildings, which affects the comfort of the human body. higher requirements. The online loa...
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IPC IPC(8): G06F19/00
Inventor 周璇杨建成闫军威
Owner SOUTH CHINA UNIV OF TECH