Department of Civil and Environmental Engineering, Portland State University, Portland, OR, United States of America.
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL, United States of America.
PLoS One. 2024 Nov 4;19(11):e0309509. doi: 10.1371/journal.pone.0309509. eCollection 2024.
The current research effort is focused on improving the effective use of the multiple disparate sources of data available by proposing a novel maximum likelihood based probabilistic data fusion approach for modeling residential energy consumption. To demonstrate our data fusion algorithm, we consider energy usage by fuel type variables (for electricity and natural gas) in residential dwellings as our dependent variable of interest, drawn from residential energy consumption survey (RECS) data. The national household travel survey (NHTS) dataset was considered to incorporate additional variables that are not available in the RECS data. With a focus on improving the model for the residential energy use by fuel type, our proposed research provides a probabilistic mechanism for appropriately fusing records from the NHTS data with the RECS data. Specifically, instead of strictly matching records with only common attributes, we propose a flexible differential weighting method (probabilistic) based on attribute similarity (or dissimilarity) across the common attributes for the two datasets. The fused dataset is employed to develop an updated model of residential energy use with additional independent variables contributed from the NHTS dataset. The newly estimated energy use model is compared with models estimated RECS data exclusively to see if there is any improvement offered by the newly fused variables. In our analysis, the model fit measures provide strong evidence for model improvement via fusion as well as weighted contribution estimation, thus highlighting the applicability of our proposed fusion algorithm. The analysis is further augmented through a validation exercise that provides evidence that the proposed algorithm offers enhanced explanatory power and predictive capability for the modeling energy use. Our proposed data fusion approach can be widely applied in various sectors including the use of location-based smartphone data to analyze mobility and ridehailing patterns that are likely to influence energy consumption with increasing electric vehicle (EV) adoption.
目前的研究工作集中在通过提出一种新的基于最大似然的概率数据融合方法来改进对多种不同来源数据的有效利用，以对住宅能源消耗进行建模。为了展示我们的数据融合算法，我们将住宅能源消耗调查 (RECS) 数据中作为感兴趣的因变量的住宅燃料类型变量（电力和天然气）的能源使用情况作为示例。我们还考虑了国家住户出行调查 (NHTS) 数据集，以纳入 RECS 数据中不可用的其他变量。我们的研究重点是改进燃料类型的住宅能源使用模型，为了实现这一目标，我们提出了一种概率机制，以便将 NHTS 数据中的记录与 RECS 数据适当融合。具体来说，我们不是仅通过仅具有公共属性的记录进行严格匹配，而是根据两个数据集的公共属性之间的属性相似性（或差异性）提出一种灵活的差分加权方法（概率）。我们使用融合数据集来开发一个更新的住宅能源使用模型，该模型增加了来自 NHTS 数据集的额外独立变量。我们将新估计的能源使用模型与仅使用 RECS 数据估计的模型进行比较，以确定新融合变量是否提供了任何改进。在我们的分析中，模型拟合度量为模型通过融合以及加权贡献估计的改进提供了强有力的证据，从而突出了我们提出的融合算法的适用性。分析通过验证练习进一步得到增强，该练习提供了证据表明，所提出的算法为能源使用建模提供了增强的解释力和预测能力。我们提出的数据融合方法可以广泛应用于包括使用基于位置的智能手机数据在内的各个领域，以分析可能随着电动汽车 (EV) 采用率的增加而影响能源消耗的移动性和叫车模式。
