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Automated measurement and verification: Performance of public domain whole-building electric baseline models

机译：自动化的测量和验证：公共领域整体建筑电气基准模型的性能

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We present a methodology to evaluate the accuracy of baseline energy predictions. To evaluate the predictions from a computer program, the program is provided with electric load data, and additional data such as outdoor air temperature, from a "training period" of at least several months duration, and used to predict the energy use as a function of time during the subsequent "prediction period." The predicted energy use is compared to the actual energy use, and errors are summarized with several metrics, including bias and mean absolute percent error (MAPE). An important feature of this methodology is that it can be used to assess the predictive accuracy of a model even if the model itself is not provided to the evaluator, so that proprietary tools can be evaluated while protecting the developer's intellectual property. The methodology was applied to evaluate several standard statistical models using data from four hundred randomly selected commercial buildings in a large utility territory in Northern California; the result is a statistical distribution of errors for each of the models. We also demonstrate how the methodology can be used to assess the uncertainty in baseline energy predictions for a portfolio of buildings, which is an issue that is important for the design of utility programs that incentivize energy savings. The findings of this work can be used to (1) inform technology assessments for technologies that deliver operational and/or behavioral savings; and (2) determine the expected accuracy of statistical models used for automated measurement and verification (M&V) of energy savings. (C) 2015 Elsevier Ltd. All rights reserved.
机译：我们提出了一种方法来评估基线能量预测的准确性。为了评估来自计算机程序的预测，该程序将从至少几个月持续时间的“训练期”中获得电负载数据和其他数据（例如室外空气温度），并用于预测能源使用情况时间在随后的“预测期”中。将预测的能源使用量与实际能源使用量进行比较，并使用几种度量标准汇总误差，包括偏差和平均绝对百分比误差（MAPE）。该方法的一个重要特征是，即使未将模型本身提供给评估者，也可用于评估模型的预测准确性，从而可以在保护开发人员知识产权的同时评估专有工具。该方法被用来评估几个标准统计模型，使用的是来自北加利福尼亚大型公用事业领地的四百个随机选择的商业建筑物的数据。结果是每个模型的误差统计分布。我们还演示了如何使用该方法来评估建筑物组合的基线能耗预测中的不确定性，这对于设计激励节能的实用程序非常重要。这项工作的发现可用于（1）为实现运营和/或行为节省的技术提供技术评估信息； （2）确定用于节能的自动测量和验证（M＆V）的统计模型的预期准确性。 （C）2015 Elsevier Ltd.保留所有权利。

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