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首页> 外文期刊>ASHRAE Transactions >A Unified Inverse Modeling Framework for Whole-Building Energy Interval Data: Daily and Hourly Baseline Modeling and Short-Term Load Forecasting
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A Unified Inverse Modeling Framework for Whole-Building Energy Interval Data: Daily and Hourly Baseline Modeling and Short-Term Load Forecasting

机译：整个建筑物能源间隔数据的统一逆建模框架：每日和每小时基线建模和短期负荷预测

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摘要

A considerable amount of literature on the application of inverse methods to building energy data has been published in the last three decades. These inverse models serve a variety of purpose such as baseline modeling for monitoring and verification (M&V) projects at monthly, daily, and hourly time scales; condition monitoring; fault detection and diagnosis; supervisory control; and load forecasting, to name a few. Usually, these models have distinct model structures, and separate models are developed depending on the specified need. This paper proposes a novel inverse modeling framework that attempts to unify some of the above application-specific models by allowing a single model to be incrementally enhanced. Specifically, we begin by clustering the energy interval data of a building, identifying scheduling day types and removing any outliers. This aspect of the analysis is described in the companion paper by Jalori and Reddy (2015). Subsequently, the first level is to identify models for each day type using daily average values of energy use and climatic variables; this is adequate in many M& V projects. These models are then extended to hourly time scales by including additional terms in the model that capture diurnal variations of the climatic variables and the building hourly scheduling about the daily mean value; this level is appropriate for M& V and for condition monitoring. Finally, periodic autoregressive terms are added to the model to enhance prediction accuracy for short-term load forecasting, useful for demand response programs, or for short-term supervisory control. The application of the proposed methodology is illustrated with year-long data from two different buildings, one synthetic (the Department of Energy medium-office prototype) building and an actual office building.
机译：在过去的三十年中，已经发表了大量有关将反方法应用于建筑能源数据的文献。这些逆模型可用于多种用途，例如用于在每月，每天和每小时的时间尺度上监视和验证（M＆V）项目的基线模型；状态监测;故障检测与诊断；监督控制；和负载预测，仅举几例。通常，这些模型具有不同的模型结构，并且根据指定的需求开发单独的模型。本文提出了一种新颖的逆建模框架，该框架试图通过允许单个模型逐渐增强来统一上述某些特定于应用的模型。具体来说，我们首先对建筑物的能量间隔数据进行聚类，确定日程安排类型并消除任何异常值。分析的这一方面在Jalori和Reddy（2015）的伴随论文中进行了描述。随后，第一级是使用日均能耗和气候变量来确定每种类型的模型；这在许多M＆V项目中就足够了。然后，通过在模型中包括捕获气候变量的日变化和建筑物每日日平均值的日程安排的附加术语，将这些模型扩展到每小时的时间尺度。此级别适用于M＆V和状态监视。最后，将周期性自回归项添加到模型中，以提高短期负荷预测的预测准确性，这对于需求响应程序或短期监督控制很有用。两年中来自两个不同建筑物的数据说明了所提出方法的应用，其中一个是综合性建筑物（能源部中型办公室原型），另一个是实际办公楼。

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  • 来源
    《ASHRAE Transactions》 |2015年第2期|156-169|共14页
  • 作者

    Saurabh Jalori; T. Agami Reddy;

  • 作者单位

    Atelier Ten, New York, NY;

    Design School and the School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ;

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