Building energy consumption prediction method and system

Abstract

The invention relates to a building energy consumption prediction method and system. The method comprises the following steps of firstly, extracting the characteristic values of all influence factorsof the energy consumption; solving the building energy consumption similarities of the historical days and the prediction days, and selecting the historical day with the highest building energy consumption similarity as a similar day; carrying out wavelet decomposition on the building energy consumption data of the selected similar day to obtain a low-frequency sequence and a high-frequency sequence; then simulating the low-frequency sequence by adopting LSSVM-GSA, and processing the high-frequency sequence by adopting a mean square weighting method; and finally, carrying out the wavelet reconstruction based on the processing results of the low-frequency sequence and the high-frequency sequence, so that the wavelet reconstruction result is the building energy consumption prediction value of the prediction day. The method and the system can be effectively applied to the building energy consumption prediction, and have the good prediction precision and robustness.

Description

technical field [0001] The invention relates to the field of architectural design, in particular to a method and system for predicting building energy consumption. Background technique [0002] The proportion of building energy consumption in my country's energy consumption is increasing day by day, and building energy conservation is imminent. Accurate prediction of building energy consumption is not only a prerequisite for building energy consumption analysis and energy-saving assessment, but also one of the important basis for building energy-saving optimization design. [0003] As a typical complex dynamic system with multivariable, strongly coupled and uncertain characteristics, building energy consumption system still faces great difficulties in establishing its accurate prediction model. On the one hand, due to the existence of personnel changes in the building or equipment start-up and stop, the energy consumption data samples used for modeling inevitably contain ou...

AI Technical Summary

Problems solved by technology

On the one hand, due to the existence of personnel changes in the building or the start and stop of equipment, the energy consumption data samples used for modeling inevitably contain outliers, which will inevitably affect the reliability of the model prediction results

On the other hand, due to the complex non-linear relationship between building energy consumption and ambient temperature, meteorological conditions, building envelope and building energy-saving parameters, etc., and there is a strong correlation between these factors, During the modeling process, the modeling accuracy will be reduced and the computational complexity will be increased

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