The Great Energy Predictor Shootout II : Measuring Retriofit Savings-Overview and Discussion of Results
The Great Energy Predictor Shootout II : Measuring Retriofit Savings-Overview and Discussion of Results
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伟大的能源预测大战 II：衡量改造节省 - 结果概述和讨论
DOI：
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
S. Thamilseran
中科院分区：
文献类型：
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作者：
J. Haberl;S. Thamilseran
为了衡量节能改造所节省的能源，开发并开展了第二次预测者点球大赛，以评估最有效的建模整栋建筑每小时能源基线的经验模型或逆模型。第二次竞赛使用了两组改造前和改造后的每小时测量数据，这些数据来自德克萨斯州参与循环贷款计划的建筑。通过确定参赛者预测数据的能力来评估参赛者模型的准确性，这些数据是从训练(或改装前)期间仔细删除的。文中还对两种模型预测的节余进行了比较。大赛的结果表明，神经网络再次提供了建筑能耗的最准确模型。然而，与第一场比赛相比，第二场比赛的结果显示，巧妙组合的统计模型似乎也与一些神经网络参赛作品一样准确，甚至在某些情况下，比一些神经网络参赛作品更准确。当使用这些模型预测改装后期间的基准使用量时，各模型之间的预测节余差异很大，特别是其中一个案例研究大楼的制冷节余。这些差异似乎是由于模型的能力(或更多)无法捕捉到某些能源性能特征，以及建模者对改造后能源使用的假设。
A second predictor shootout contest has been developed and conducted to evaluate the most effective empirical or inverse models for modeling hourly whole-building energy baselines for purposes of measuring savings from energy conservation retrofits. This second contest utilized two sets of measured hourly pre-retrofit and post-retrofit data from buildings participating in a revolving loan program in Texas. The accuracy of the contestants` models was evaluated by determining their ability to predict data that were carefully removed from the training (or pre-retrofit) period. A comparison of the savings predicted by the models is also presented. The results from the contest show that neural networks again provide the most accurate model of a building`s energy use. However, in contrast to the first contest, the second contest`s results show that cleverly assembled statistical models also appear to be as accurate or, in some cases, more accurate than some of the neural network entries. When these models were used to forecast the baseline use into the post-retrofit periods, large variations in the predicted savings occurred among the models, particularly for the cooling energy savings in one of the case study buildings. These variations appear to be due to the ability of the models (ormore » inability) to capture certain energy performance characteristics and the modeler`s assumptions about the post-retrofit energy use.« less