Building energy load real-time prediction method based on neural network elastic weight solidification

Abstract

The invention provides a building energy load real-time prediction method based on neural network elastic weight solidification. The building energy load real-time prediction method comprises two steps of neural network model offline training and neural network model real-time fine adjustment. The model offline training step comprises six sub-steps of building energy load historical data acquisition, data preprocessing, model input selection, model hyper-parameter optimization, model parameter training and parameter importance calculation in sequence. The model real-time fine adjustment step comprises six sub-steps of building energy load real-time data acquisition, data preprocessing, model input consistency keeping, model hyper-parameter consistency keeping, fine adjustment of the model by using elastic weight solidification and parameter importance updating. Particularly, fine adjustment of the model is periodically carried out in the form of a sliding window, so that the model can adapt to the change of building energy consumption rules. According to the method, the neural network model is subjected to regular fine adjustment by using an elastic weight solidification technology, so that accurate and reliable real-time prediction of the building energy consumption load is realized.

Description

technical field [0001] The invention belongs to the field of building energy load forecasting, and relates to a neural network-based black-box modeling technology and a continual learning technology based on elastic weight consolidation (elastic weight consolidation), in particular to a building based on neural network elastic weight consolidation A method for real-time forecasting of energy loads. Background technique [0002] Accurate building energy load forecasting is crucial for optimal operation and fault diagnosis of building electromechanical systems such as central air-conditioning systems and lighting systems. With the popularization of building automation systems, a large amount of building energy load data can be stored offline and acquired in real time, thus providing a reliable data source for building energy load forecasting technology based on neural networks. A large number of studies have shown that neural networks (BP neural network, recurrent neural netw...

AI Technical Summary

Problems solved by technology

This method can greatly reduce data storage costs and neural network training time, but due to limited training data, the neural network will not be able to learn the historical energy consumption laws of buildings outside the sliding window

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