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Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods

机译:住宅建筑能耗:能源数据可用性,特性和能量性能预测方法综述

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Purpose of ReviewResidential energy performance prediction has historically received less attention, as compared to commercial buildings. This likely is in part due to the limited availability of residential energy data, as well as the relative challenge of predicting energy consumption of buildings that are more highly dependent on occupant behavior. The purpose of this effort is to assess the types and characteristics of energy and non-energy data available for algorithm developed and methods that have been developed to predict residential consumption.Recent FindingsWhile there are several large residential building energy datasets, data availability is still generally very limited. Most energy prediction methods used recently include data-driven approaches, as well as combinations of multiple methods; however, many methods have not been tested for residential buildings, or at a range of energy data frequencies.SummaryThe literature points to the need for the availability of more residential building data sources to be able to assess and improve models, and further testing is needed including those models that have not yet been significantly used for residential buildings.
机译:与商业建筑相比,审查期内能源性能预测的目的历史上受到更少的关注。这可能部分是由于住宅能源数据的可用性有限,以及预测建筑物能耗的相对挑战,这些建筑物的能耗更加依赖于占用行为。这项努力的目的是评估可用于算法的能量和非能源数据的类型和特征,该算法开发和用于预测住宅消费的方法。特征在于多个大型住宅建筑能源数据集,数据可用性仍然仍然存在非常有限。最近使用的大多数能量预测方法包括数据驱动的方法,以及多种方法的组合;然而,许多方法尚未测试住宅建筑物,或者在一系列能量数据频率下进行测试。ummary将文献指出需要更多的住宅建筑数据来源,以便能够评估和改进模型,并且需要进一步测试包括这些模型尚未显着用于住宅建筑。

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