Building energy consumption prediction method based on RBF neural network

Abstract

The invention discloses a building energy consumption prediction method based on an RBF neural network. The method comprises the steps of: selecting historical data of to-be-selected influencing factors of building energy consumption to generate an input vector and the history data of the corresponding building energy consumption value as the output, to obtain training samples; selecting the influencing factors of the building energy consumption by using the improved genetic algorithm; selecting the data of the to-be-predicted date of the building energy consumption influencing factors and inputting into the RBF neural network, to obtain the predicted value of the building energy consumption. The problem of generalization of the RBF neural network is solved; the building energy consumptionprediction is realized by using the RBF neural network based on an L-GEM, and the accuracy of the neural network prediction result is improved.

Description

technical field [0001] The present invention relates to a method for predicting building energy consumption, in particular to a radial basis function (RBF for short) RBF neural network based on a localized generalization error model (Localized Generalization Error Model, L-GEM) L-GEM Parameter selection and prediction methods affecting building energy consumption. Background technique [0002] With the continuous acceleration of the urbanization process, the energy problem has become increasingly prominent. Building energy conservation has become a research hotspot in today's social development. A comprehensive evaluation and comprehensive analysis of building system energy consumption is the premise and basis for energy-saving renovation or energy-saving design. The establishment of a prediction model that reflects changes in energy consumption is an effective way and an important means to analyze and understand the changes and development characteristics of building energy...

AI Technical Summary

Problems solved by technology

[0004] In order to solve the deficiencies in the prior art, the present invention provides a kind of building energy consumption influence parameter selection and prediction method based on the RBF neural network of L-GEM (Local Generalized Error Model), which solves the problem of neural network in the training data set. The problem of producing small errors on the test data set but not always performing well on the test data set solves the problem of low accuracy of building energy consumption prediction results

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