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首页> 外文学位 >Development of Fourier series and artificial neural network approaches to model hourly energy use in commercial buildings.
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Development of Fourier series and artificial neural network approaches to model hourly energy use in commercial buildings.

机译：傅立叶级数和人工神经网络方法的开发，用于对商业建筑中的小时能耗进行建模。

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摘要

This dissertation develops Fourier series and artificial neural network (ANN) approaches to model hourly energy use in commercial buildings and illustrates application to data-screening.;The procedure for modeling hourly energy use has two steps: (i) Day-typing and (ii) Model development. The mean diurnal energy use and the diurnal profile may be different during working weekdays, weekends, holidays and Christmas due to major changes in mode of operation. The first step, known as day-typing, is important for removing such effects. The second step is to develop models for each day-type.;Fourier series analysis is eminently suitable for modeling strongly periodic data. Energy use in commercial buildings being strongly periodic, is appropriate for Fourier series treatment. Generalized Fourier series (GFS) model equations, developed for both weather independent and weather dependent energy use, give a set of parameters involving time and/or weather variables. Stepwise regression is performed to select the important parameters and a final model for each day-type is developed using the selected parameters.;There are situations when only temperature data is available. A temperature based Fourier series (TFS) equation for modeling heating and cooling energy use has been developed to deal with such cases. Two important advantages of TFS are that it (i) represents nonlinear variation of energy use in a linearized functional form and (ii) can indirectly account for humidity and solar effect in the cooling energy use.;ANNs with back propagation algorithms give high prediction accuracy and have been applied by many researchers to model hourly energy use in commercial buildings. However, the training of Back Propagation Network (BPN) algorithms is a long, uncertain process. ANNs with local basis functions require significantly shorter training times than conventional BPNs. A methodology has been developed to model heating and cooling energy use in commercial buildings using a one-hidden-layer ANN with two dimensional wavelet basis functions derived from cubic splines.;A suitable prediction interval can be generated and used to perform data-screening. Application of the TFS approach to data-screening is illustrated with monitored data.
机译：本文开发了傅立叶级数和人工神经网络（ANN）方法来对商业建筑中的小时能耗进行建模，并说明了其在数据筛选中的应用。;小时能耗的建模过程包括两个步骤：（i）日类型化和（ii ）模型开发。由于工作方式的重大变化，在工作日，周末，节假日和圣诞节期间，平均每日能源使用量和每日概况可能会有所不同。第一步，即日间打字，对于消除这种影响很重要。第二步是为每种日类型开发模型。傅立叶级数分析非常适合于对强周期数据进行建模。商业建筑中的能源使用具有强烈的周期性，适用于傅里叶级数处理。针对天气独立和天气依赖的能源开发而开发的广义傅里叶级数（GFS）模型方程式提供了一组涉及时间和/或天气变量的参数。进行逐步回归以选择重要参数，并使用所选参数为每个日类型开发最终模型。;在某些情况下，只有温度数据可用。为了解决这种情况，已经开发了一种基于温度的傅里叶级数（TFS）方程，用于模拟加热和冷却能量的使用。 TFS的两个重要优点是：（i）以线性函数形式表示能量使用的非线性变化;（ii）可以间接考虑冷却能量使用中的湿度和太阳效应。;具有反向传播算法的人工神经网络具有很高的预测精度并已被许多研究人员用来对商业建筑中每小时的能耗进行建模。但是，反向传播网络（BPN）算法的训练是一个漫长而不确定的过程。具有本地基础功能的人工神经网络比传统的BPN所需的训练时间短得多。已经开发出一种方法来对商业建筑中的供暖和制冷能源使用进行建模，该方法使用具有一层从三次样条导出的二维小波基函数的单层ANN .;可以生成合适的预测间隔并将其用于执行数据筛选。使用监视的数据说明了TFS方法在数据筛选中的应用。

著录项

  • 作者

    Dhar, Amitava.;

  • 作者单位

    Texas A&M University.;

  • 授予单位 Texas A&M University.;
  • 学科 Engineering Mechanical.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 207 p.
  • 总页数 207
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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