Heat Load Prediction through Recurrent Neural Network in District Heating and Cooling Systems

被引：0

作者：

Kato, Kosuke ^{[1
]}

Sakawa, Masatoshi ^{[1
]}

Ishimaru, Keiichi ^{[2
]}

Ushiro, Satoshi ^{[2
]}

Shibano, Toshihiro ^{[3
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, Higashihiroshima 724, Japan

[2] Shinryo Corp, Urban Facil Div, Tokyo, Japan

[3] Director Informat Technol Infront Corp, Tokyo, Japan

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6 | 2008年

关键词：

district heating and cooling system; heat load prediction; recurrent neural network; data characteristics;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

As a heat load prediction method in district cooling and heating systems, the efficiency of a layered neural network has been shown, but there is a drawback that its prediction becomes less accurate in periods when the heat load is non-stationary. In this paper, we propose a new heat load prediction method superior to existing methods by using a recurrent neural network to deal with the dynamic variation of heat load and new input data in consideration of characteristics of heat load data.

引用

页码：1400 / +

页数：2

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