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Central air conditioner cooling load prediction method based on BP neural network

A BP neural network, central air conditioning technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as dependence and high historical data requirements, achieve control error range, ensure forecast accuracy, and timely cooling consumption The effect of abnormal warning

Inactive Publication Date: 2014-08-27
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

However, the expert system has high requirements for historical data and relies on the extracted rules, which has certain limitations.

Method used

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  • Central air conditioner cooling load prediction method based on BP neural network
  • Central air conditioner cooling load prediction method based on BP neural network
  • Central air conditioner cooling load prediction method based on BP neural network

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Embodiment

[0046] Such as figure 1 Shown, be the BP neural network structure that is used for cooling load forecasting in the present invention and construct: the number of layers of BP neural network is three layers, and hidden layer is 1 layer, is input layer respectively from left to right, hidden layer, The output layer is fully connected between each layer. 9 input layer nodes (X 1 -X 9 ), 1 output layer node (O), 11 hidden nodes (h 1 -h 11), the activation function uses the Sigmoid function, and the connection weight between neurons is random data between 0 and 1. The basic principle of BP neural network is to calculate the error between the output layer and the expected output through the learning and training process, and then reversely correct the weight and bias value of each neuron from the output layer until the error between the network output and the expected output less than the predetermined error. The learning process is divided into two stages: the first is forwar...

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Abstract

The invention discloses a central air conditioner cooling load prediction method based on a BP neural network. The method includes the following steps of firstly, selecting factors influencing the cooling loads of school buildings to serve as input parameters of the neural network; secondly, arranging and preprocessing data of building cooling load prediction samples; thirdly, designing the hierarchical structure of the BP neural network, and determining the hidden layers; fourthly, running BP neural network training till reverse convergence occurs, stopping learning, and outputting a prediction value. The method has the advantages of being high in accuracy and reliability and the like.

Description

technical field [0001] The invention relates to a forecasting technology of central air-conditioning cooling load of school buildings, in particular to a forecasting method of central air-conditioning cooling load based on BP neural network. Background technique [0002] The cooling consumption of central air-conditioning in school buildings has the following characteristics: ①The indoor population is dense, and the heat and humidity loads of the human body account for the vast majority of the air-conditioning load in summer. It accounts for a large proportion of the air-conditioning load; ③ Indoor people are concentrated, the air quality is poor, and long-term stays affect human health; ④ Even in the case of air-conditioning, due to the high indoor population density, the indoor relative humidity is high (generally higher than 60 %), so indoor people are likely to feel stuffy; ⑤The air-conditioning system of school buildings consumes a lot of power. According to statistics,...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08
CPCG06N3/08
Inventor 彭新一谢妍黄志炜李绵升
Owner SOUTH CHINA UNIV OF TECH
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