Neural network and particle swarm optimization algorithm-based building energy consumption predicting method

Abstract

The invention discloses a neural network and particle swarm optimization algorithm-based building energy consumption predicting method. The method comprises the following four main steps of collecting related data of building energy consumption , and pre-processing the data; determining an input output item and a network structure of a multi-layer feedforward neural network model, wherein the multi-layer feedforward neural network model has an error back-propagation learning function; optimizing the connection weigh value and threshold value of a BP network by using a particle swarm algorithm; performing short-term prediction on the building energy consumption by using the neural network model which is obtained through optimization. According to the predicting method, pre-input variables are subject to main component analysis by using a statistical product and a service resolve scheme software, and the variable according with a main component extraction requirement is selected, so that the input dimension is reduced; the structure and parameters of the neural network model are optimized through the overall optimization ability of the particle swarm algorithm, so that compared with the current building energy consumption predicting method, the predicting model provided by the invention has the advantages of simple structure, high predicting precision and the like.

Description

technical field [0001] The invention relates to a building energy consumption prediction method based on a neural network and a particle swarm optimization algorithm, belonging to the field of building energy management. Background technique [0002] With the continuous development of social productivity and the steady improvement of people's material living standards, building energy consumption will continue to grow rapidly, which poses a great challenge to the supply of energy and the maintenance of the ecological environment. Combined with the actual situation in our country, to realize the scientific and optimal management of building energy, it is necessary to take the scientific prediction of building energy consumption as the premise and basis. In the past ten years, with the continuous proposal and wide application of various intelligent optimization technologies, the prediction methods of building energy consumption have developed rapidly. In the field of building...

AI Technical Summary

Problems solved by technology

As a gradient-based adaptive algorithm, the learning process of BP network has defects such as easy to fall into local extremum and slow convergence speed, and cannot guarantee the global optimum of network parameters.

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