Short-term air conditioner load prediction method and system based on sparrow optimization algorithm

Abstract

The invention discloses an air conditioner load short-term prediction method and system based on a sparrow optimization algorithm. Historical data of six factors including cold load, outdoor temperature, wet bulb temperature, relative humidity, solar radiation intensity and outdoor wind speed at different moments are selected as input variables; and a grey correlation degree analysis method improved by an entropy weight method is used for analyzing the weighted correlation degree between the input variables and the output variable of the air conditioner cooling load at the current moment, the input variables with the weighted correlation lower than 0.02 are removed, and the remaining variables are reserved. An SVM is established according to the number of the reserved input variables and the air conditioner cooling load at the current moment; then, the optimal hyper-parameter of the SVM is optimized by using a sparrow algorithm to obtain an SSA-SVM prediction model; and finally, load prediction is carried out on the SSA-SVM prediction model to obtain a prediction value. According to the method, the defect that the SVM depends on artificial experience to obtain the optimal hyper-parameter is overcome, the prediction precision of the SVM is improved, and the problem that the energy consumption is too high due to large prediction deviation of the cooling load of the air conditioner is solved.

Description

technical field [0001] The invention belongs to the technical field of air-conditioning load forecasting, and in particular relates to a short-term air-conditioning load forecasting method and system based on a sparrow optimization algorithm. Background technique [0002] Air-conditioning load forecasting is the basic condition for the operation of chillers and the necessary basis for the formulation of control strategies for refrigeration stations. At present, the selection of chillers is based on the maximum cooling capacity, that is, the selection is based on the maximum building load. But in general, the full-load running time of the chiller is less than 3% of the total running time, and according to the actual measurement of foreign scholars, more than 80% of the time of the air-conditioning unit is to run under the partial load of less than 60%, so the chiller The energy consumption is mainly its energy consumption under partial load operating conditions. The energy ...

AI Technical Summary

Problems solved by technology

The traditional air-conditioning cooling load prediction methods are mainly support vector machines, BP neural networks, etc., but the disadvantages of traditional methods are that they are easy to fall into local minimum, slow convergence speed, low matching degree of input and output, resulting in large deviation of air-conditioning cooling load prediction

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