A data-driven approach for steam load prediction in buildings

被引：174

作者：

Kusiak, Andrew ^{[1
]}

Li, Mingyang ^{[1
]}

Zhang, Zijun ^{[1
]}

机构：

[1] Univ Iowa, Dept Mech & Ind Engn, Seamans Ctr 3131, Iowa City, IA 52242 USA

来源：

APPLIED ENERGY | 2010年 / 87卷 / 03期

关键词：

Data mining; Building load estimation; Steam load prediction; Neural network ensemble; Energy forecasting; Monte Carlo simulation; Parameter selection; COOLING LOAD; ELECTRIC-LOAD; ENERGY;

D O I：

10.1016/j.apenergy.2009.09.004

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：

Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parameters used to develop models. A neural network (NN) ensemble with five MLPs (multi-layer perceptrons) performed best among all data-mining algorithms tested and therefore was selected to develop a predictive model. To meet the constraints of the existing energy management applications, Monte Carlo simulation is used to investigate uncertainty propagation of the model built by using weather forecast data. Based on the formulated model and weather forecasting data, future steam consumption is estimated. The latter allows optimal decisions to be made while managing fuel purchasing, scheduling the steam boiler, and building energy consumption. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：925 / 933

页数：9

共 36 条

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