Air conditioner cooling load prediction method and system considering data characteristics after frequency domain decomposition

Abstract

The invention discloses an air conditioner cooling load prediction method and system considering data characteristics after frequency domain decomposition. The method comprises the following steps: taking factors of which the load sequence importance is greater than a set threshold value as input variables; decomposing an original load sequence into two intrinsic mode functions IMF1 and IMF2 and an error sequence ERR by using a variational mode decomposition technology; establishing a least square support vector machine prediction model for the intrinsic mode function IMF1 to obtain a prediction component of an IMF1 subsequence; establishing an extreme gradient boosting decision tree prediction model for the intrinsic mode function IMF2 to obtain a prediction component of an IMF2 sub-sequence; performing normal fitting on the probability distribution of the error sequence ERR showing the Gaussian noise part to obtain a prediction component of a sub-sequence of the error sequence ERR; and superposing the prediction component of the IMF1 sub-sequence, the prediction component of the IMF2 sub-sequence and the prediction component of the error sequence ERR sub-sequence, and outputting to obtain a final cold load prediction value. According to the method, the prediction precision under the environment noise condition is improved, and the method has the practical engineering application background and important practical significance.

Description

technical field [0001] The invention belongs to the technical field of load forecasting in large-scale public building systems, and in particular relates to an air-conditioning cooling load forecasting method and system considering data characteristics after frequency domain decomposition. Background technique [0002] Among the main sources of building energy consumption, air conditioning system energy consumption accounts for more than 40% of building energy consumption, and is one of the important energy consumption systems. It is particularly important to improve energy utilization and save resources, and air conditioners, as one of the main equipment for cooling (heating) in life, have huge potential for energy saving. By providing the required cooling load in advance and adjusting the dynamic operating parameters of the HVAC system, it is possible to solve the problem of high energy consumption of the system caused by the mismatch between the operating state of the equ...

AI Technical Summary

Problems solved by technology

These models have a fast calculation speed and can reflect the recent continuous changes in load, but they have high requirements for the stability of the original time series, while the actual air-conditioning load generally does not meet the strict stability requirements, and the regression method has certain defects in solving nonlinear problems , so the prediction effect is not ideal

ANN also has certain defects. When the number of learning samples is limited, the accuracy is difficult to guarantee. When the dimension of learning samples is high, there are often many local extremums with large differences in the high-dimensional space, which will make the learning results appear greater randomness

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