A multi-class energy consumption forecasting method based on circulating neural network

Abstract

The invention discloses a multi-class energy consumption prediction method based on a circulating neural network. A circulating neural network model is formed by pre-training, and the method comprisesthe following steps: step S1, loading original energy consumption data on the basis of the circulating neural network model, judging missing value and abnormal value from the original energy consumption data, and detecting and processing the missing value and abnormal value; step S2, extracting the time series characteristic data from the original energy consumption data on the basi of the original energy consumption data, establishing a characteristic set of the circulating neural network model, and normalizing the characteristic set; step S3, batch training being carried out on the featureset after the normalization processing, a multi-class output neural network being established by combining the non-time series feature data, and the multi-class energy consumption data output by the multi-class output neural network being predicted. The utility model has the advantages that a multi-class output neural network is established by using the time series characteristic data of the original energy consumption data and the non-time series characteristic data to predict the energy consumption.

Description

technical field [0001] The invention relates to the technical field of building energy consumption prediction, in particular to a multi-category energy consumption prediction method based on a cyclic neural network. Background technique [0002] With the rapid advancement of the urbanization process, large buildings in cities such as hotels and shopping malls are generating more and more energy consumption, and the proportion of this type of energy consumption in the total energy consumption of the country is also increasing. In response to the national requirements for energy conservation and emission reduction, building energy conservation has become an important focus in the development and planning of major cities. As an important part of building energy consumption analysis, building energy consumption prediction is conducive to timely discovery of various problems in the process of building energy use, and can also help guide the building's environmental protection and...

AI Technical Summary

Problems solved by technology

Common energy consumption prediction methods include linear regression prediction, support vector machine regression prediction, regression prediction based on BP neural network, etc., but these methods usually do not make good use of the time series characteristic data of historical energy consumption data, especially in the In consumption prediction scenarios, time series feature data are more obvious and important

In addition, most of the energy consumption prediction models mentioned in the literature can only predict the energy consumption of one node or one category. If it is necessary to predict the energy consumption of multiple categories, multiple models need to be established and trained. Conducive to the rapid deployment and efficient operation of the energy consumption prediction system

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