Building energy consumption prediction method based on longicorn beard optimization algorithm and neural network

Abstract

The invention discloses a building energy consumption prediction method based on a longicorn beard optimization algorithm and a neural network, and the method comprises the following four main steps:1), collecting building energy consumption related data, and carrying out the principal component analysis and normalization preprocessing of the data; 2) determining input and output items and a network structure of a multilayer feedforward neural network model with an error back propagation learning function; 3) optimizing the connection weight and threshold of the BP network by using a longicorn beard algorithm; and 4) performing short-term prediction on the building power consumption by utilizing the optimized BAS-BP prediction model. According to the method, principal component analysis is carried out on the pre-input variables by utilizing a principal component analysis algorithm, and the variables meeting the principal component extraction requirement are selected, so that the inputdimension is reduced; and the structure and parameters of the neural network model are optimized by using the global optimization capability of the longicorn beard algorithm. Compared with an existing building energy consumption prediction method, the prediction model has the advantages of being simple in structure, high in prediction precision, short in operation time and the like.

Description

technical field [0001] The invention relates to a method for predicting building energy consumption based on a beetle's beetle optimization algorithm and a neural network, and belongs to the field of building energy management. Background technique [0002] Building energy system is a complex system with multivariable and distributed parameters. Accurately predicting the energy consumption level of buildings is an important basis and premise for analyzing the energy-saving potential of buildings and guiding future energy use. At the same time, it has important practical significance for improving the efficiency of building energy-consuming equipment and reducing energy waste. In recent years, with the proposal and wide application of various intelligent optimization technologies, the prediction methods of building energy consumption have been developed rapidly. In the field of building energy, how to improve and perfect the current building energy consumption prediction me...

AI Technical Summary

Problems solved by technology

[0003] Over the years, researchers at home and abroad have conducted extensive research on building energy consumption prediction methods, and formed two types of analysis methods: forward method (forward modeling method) and reverse method (data-driven method). Among them, the forward method is often Detailed building parameters, energy equipment systems, and meteorological data are required, and a large amount of expert knowledge is required, which makes energy consumption calculation time-consuming and is not conducive to online operation analysis and control

In the reverse method, if the historical energy consumption data of the building is known, the BP neural network method becomes one of the typical prediction methods because of its strong self-learning and self-adaptive capabilities, but as a gradient-based adaptive algorithm , the learning process of BP neural network has defects such as easy to fall into local extremum, slow convergence speed, etc.

Images