A bp neural network model configuration method for building energy consumption prediction

Abstract

The invention discloses a BP neural network model configuration method oriented to building energy consumption prediction. The method comprises the steps that A, a configurable BP neural network model is constructed; B, the BP neural network model with an energy consumption mode embedded is trained, and a prediction training model is formed; C, building energy consumption data prediction is carried out. According to the BP neural network model configuration method for building energy consumption prediction, complex building energy consumption prediction related influence factors, parameters and algorithms and the building energy consumption actual prediction function are subjected to decoupling separation, configurable construction of any prediction model is achieved, and the development and maintenance cost of a building energy consumption prediction system is reduced, so that an energy consumption prediction system of a large-scale building is easy to achieve, and good application prospects are achieved.

Description

technical field [0001] The invention relates to a BP neural network model configuration method for building energy consumption prediction, belonging to the technical field of building energy consumption prediction. Background technique [0002] With the rapid growth of China's population and the continuous improvement of people's living standards, the building area in my country has doubled, and the cost of building energy consumption has also continued to increase. According to statistics, my country's building energy consumption accounts for about 1 / 3 of the total energy consumption of the whole society. At present, building energy consumption prediction is mainly based on historical energy consumption information and related environmental information to predict the possible energy consumption of buildings in the future. Building energy consumption forecasting can not only help managers to arrange the operation mode of the system reasonably, but also can use the compariso...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the problem that the existing single forecasting model is difficult to meet the requirements of the actual complex and changeable forecasting system

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