Building energy consumption prediction method based on sub-metering time sequence, system and building

Abstract

The invention discloses a building energy consumption prediction method based on a sub-metering time sequence, a system and a building. The prediction method comprises steps of acquiring and storing data of energy consumption and temperature of the building; using the acquired and stored data of energy consumption and temperature as input parameters for a time sequence analysis method; according to sub-metering and correlation analysis, using the trends of the energy consumption and the temperature and time factors predicted by the time sequence analysis method as main influence factors of the energy consumption of the building; and using the determined main influence factors and acquired energy consumption as parameters in a built BP neural network model to predict the energy consumption of the building in the future. According to the invention, the BP neural network is low in learning efficiency, slow in the convergence speed and quite sensitive to parameter selection, and the building energy consumption prediction algorithm based on the sub-metering and the time sequence is added on the basis of the BP neural network, so precision of energy consumption prediction is improved, prediction time is shortened, and predicted data is quite precise.

Description

technical field [0001] The present invention relates to a method for predicting building energy consumption, which belongs to the technical field of building energy consumption prediction, and in particular relates to a method for predicting building energy consumption based on sub-item measurement time series, a system for implementing such a method, and a system equipped with such a method of buildings. Background technique [0002] With the continuous acceleration of the urbanization process, energy issues have become increasingly prominent. The proportion of building energy consumption in total social energy consumption has risen from 10% in the late 1970s to 28%. The annual energy consumption of state office buildings and various public buildings accounts for about 22% of the total urban energy consumption in the country. The power consumption per unit area is 10 to 20 times that of ordinary residential buildings, and 1.5 to 2 times that of similar buildings in develo...

AI Technical Summary

Problems solved by technology

At the same time, the current data collection methods mostly focus on the collection of energy consumption data itself, and less data collection on energy consumption factors

In addition, most of these energy consumption data acquisition devices measure and read various data of the equipment at regular intervals, and do not pay attention to the time and energy consumption of certain equipment when transitioning from one state to another.

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