Neural network based central heating system load prediction and constrained control
type: Item
instance of: scholarly article
title: Neural network based central heating system load prediction and constrained control
zbMATH Open document ID: 1426.93152
DOI: 10.1155/2018/2908608
author: Xianqiang Yang
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 7018724
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2018/2908608
OpenAlex ID: W2785904687
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description: scientific article; zbMATH DE number 7018724