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A Hybrid Model of AR and PNN Method for Building Thermal Load Forecasting

  • Tingzhang LiuEmail author
  • Kai Liu
  • Ping Fang
  • Jianfei Zhao
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 643)

Abstract

A hybrid method which combines time series model and artificial intelligence method is proposed in this paper to improve the prediction accuracy of building thermal load. Firstly, a simple auto regressive (AR) model is utilized to predict present load using previous loads, the order and the parameters of AR model are identified by the data produced by DeST. Then, a 3-layer back-propagation neural network optimized by particle swarm optimization (PSO) neural network (PNN) is set up to predict the error which is derived by comparing the precious AR predicting load. The error and its corresponding meteorological data generate the training sample data. At last, the hybrid model, named autoregressive and particle swarm neural network (APNN), is obtained. It uses historical load information and real-time meteorological data as input to predict a refined real-time load by adding error to preparative load. To evaluate the prediction accuracy, this hybrid model APNN is compared with several common methods via different statistical indicators, the result show the APNN hybrid method has higher accuracy in thermal load forecasting.

Keywords

Hybrid model Thermal load forecasting AR PSO APNN 

Notes

Acknowledgment

Thanks to the supports by National Natural Science Foundation (NNSF) of China under Grant 61273190 and Shanghai Natural Science Foundation under Grant 13ZR1417000.

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Copyright information

© Springer Science+Business Media Singapore 2016

Authors and Affiliations

  • Tingzhang Liu
    • 1
    • 2
    Email author
  • Kai Liu
    • 1
    • 2
  • Ping Fang
    • 1
    • 2
  • Jianfei Zhao
    • 1
    • 2
  1. 1.Shanghai Key Laboratory of Power Station Automation TechnologyShanghaiChina
  2. 2.School of Mechanical Engineering and AutomationShanghai UniversityShanghaiChina

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