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Mathematical Problems in Engineering
Volume 2018, Article ID 2908608, 14 pages
https://doi.org/10.1155/2018/2908608
Research Article

Neural Network Based Central Heating System Load Prediction and Constrained Control

1School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
2Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576
3The Third Design Division, China Northeast Architectural Design and Research Institute Co., Ltd., Shenyang, Liaoning 110003, China

Correspondence should be addressed to Hongwei Wang; nc.ude.uzjs@whw_jh

Received 11 July 2017; Revised 7 January 2018; Accepted 10 January 2018; Published 7 February 2018

Academic Editor: Mauro Gaggero

Copyright © 2018 Hongwei Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.