BUILDING ENERGY USE PREDICTION AND SYSTEM-IDENTIFICATION USING RECURRENT NEURAL NETWORKS

被引：54

作者：

KREIDER, JF ^{[1
]}

CLARIDGE, DE ^{[1
]}

CURTISS, P ^{[1
]}

DODIER, R ^{[1
]}

HABERT, JS ^{[1
]}

KRARTI, M ^{[1
]}

机构：

[1] TEXAS A&M UNIV,DEPT MECH ENGN,COLLEGE STN,TX 77843

来源：

JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME | 1995年 / 117卷 / 03期

关键词：

D O I：

10.1115/1.2847757

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Following several successful applications of feedforward neural networks (NNs) to the building energy prediction problem (Wang and Kreider, 1992; JCEM, 1992, 1993; Curtiss et al., 1993, 1994; Anstett and kreider, 1993; Kreider and Haberl, 1994) a more difficult problem has been addressed recently: namely, the prediction of building energy consumption well into the future without knowledge of immediately past energy consumption. This paper will report results on a recent study of six months of hourly data recorded at the Zachry Engineering Center (ZEC) in College Station TX. Also reported are results on finding the R and C values for buildings from networks trained on building data.

引用

页码：161 / 166

页数：6