An energy consumption prediction method for subway station air conditioning system based on isoa-lssvm

Abstract

The invention discloses an ISOA-LSSVM-based subway air-conditioning system energy consumption prediction method. The method includes the following steps: acquiring training data, standardizing the training data, using an improved population search algorithm to conduct parameter optimization on a least squares support vector machine, and establishing a prediction model; acquiring real-time measurement data and standardizing the real-time measurement data, inputting the standardized real-time measurement data to the prediction model and performing prediction, and finally performing reverse standardization and outputting a predicted energy consumption value. According to the invention, the method can predict ISOA-LSSVM-based subway air-conditioning system energy consumption; the improved population search algorithm uses a Gaussian membership function to represent a fuzzy variant of step size in search, reduces the times of iteration, and increases prediction precision of the model; the preliminary direction is obtained by comparing individual optimal fitness value and the fitness value of a current individual, and the obtained preliminary direction can better represent the preliminary action of the current individual and at the same time the iteration speed is increased.

Description

technical field [0001] The invention belongs to the field of HVAC energy consumption modeling, and in particular relates to the application of an ISOA-LSSVM-based energy consumption prediction method for a subway station air-conditioning system in the subway station air-conditioning system, which is used to predict the energy consumption value in a short period of time. Background technique [0002] The ventilation and air-conditioning system of subway stations is a major energy consumer of the entire subway system, accounting for 30%-50%. Therefore, the current operation of the air conditioning system should reduce the energy consumption of the system while the temperature, humidity and other indicators meet the control requirements. However, since there are many factors affecting energy consumption in the air-conditioning system, and the relationship between each factor is complex, the system presents a large lag, and it is difficult to establish an accurate energy consump...

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Problems solved by technology

[0005] Aiming at the problems of multi-variable coupling, large hysteresis and difficult establishment of energy consumption model of the subway station air-conditioning system, the present invention proposes an ISOA-LSSVM-based method for predicting the energy consumption of the subway station air-conditioning system, which solves the calculation of the traditional grid search LSSVM Large-scale problems, improving the prediction speed and accuracy of the model

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