期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2017
卷号：13
期号：5
页码：1
DOI：10.1177/1550147717704933
出版社：Hindawi Publishing Corporation
摘要：This research develops a multi-layer hybrid soft-sensor model to improve the accuracy of building thermal load prediction using integrated data. The multi-layer hybrid model (autoregressive and particle swarm optimization neural network) hybridizes an autoregressive model with exogenous inputs and a particle swarm optimization neural network. The distributed sensors’ experimental scenario was set in a medium-sized office building located in Shanghai, which has applied this multi-layer hybrid model to evaluate the prediction accuracy, meanwhile its performance was also compared with several commonly used methods under different evaluation criteria. Through frequency-domain decomposition, the heat balance equation is used to validate the autoregressive and particle swarm optimization neural network model. Both the simulation of building thermal load and experiment results demonstrate that the proposed autoregressive and particle swarm optimization neural network method can recognize soft sensing of the building thermal load much more quickly and efficiently, and achieve higher accuracy in both cooling load and heating load prediction.
关键词：Autoregressive model with exogenous inputs; particle swarm optimization neural network; multi-layer hybrid model (autoregressive and particle swarm optimization neural network); soft sensor; thermal load
