A building energy consumption prediction method based on a recurrent neural network and a multi-task learning model

Abstract

The invention discloses a building energy consumption prediction method based on a recurrent neural network and a multi-task learning model, relates to the technical field of comprehensive energy management, and solves the technical problems of parallel prediction of multiple types of energy consumption, guarantee of prediction precision and shortening of model training time. The method comprisesthe following steps: acquiring a building energy consumption data sample; The method comprises the following steps of: carrying out missing data processing by utilizing a plurality of similar time point data averaging, constructing a plurality of learning tasks according to an energy consumption type, a time step length and initial time, then normalizing a data set of each learning task, and measuring the similarity among a plurality of task training sets by using a Pearson correlation coefficient. After the similarity among multiple tasks is ensured, a neural network model is created and trained, and finally, a multi-task CIFG-LSTM neural network model is used for predicting composite energy consumption. According to the energy consumption prediction method, the multivariate energy consumption can be predicted at the same time, close connection and interaction between the energy consumption are fully utilized, and the prediction precision and speed are improved.

Description

technical field [0001] The invention belongs to the technical field of multiple load forecasting, and relates to a building energy consumption forecasting method based on a cyclic neural network and a multi-task learning model. Background technique [0002] The integrated energy management system is composed of multiple energy networks such as electricity, heat, cold, and gas. It is of great significance to give full play to the complementary advantages of various energy sources, promote the transformation of energy structure, and promote my country's energy revolution. Accurate and fast energy consumption prediction is an important part of the reliable and efficient operation of the integrated energy management system. In order to make reasonable planning and effective use of energy, it is necessary to accurately predict the energy consumption in the short term in the future. At present, the energy consumption prediction technology for a single type of energy is very mature...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a building energy consumption prediction method based on a cyclic neural network and a multi-task learning model, which solves the problem that multiple loads cannot be predicted uniformly, resulting in complicated prediction process and low precision

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