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Data Science approach for a cross-disciplinary understanding of urban phenomena: Application to energy efficiency of buildings

机译:对城市现象跨学科理解的数据科学方法:应用于建筑物的能源效率

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Our goal is to develop theoretical and practical tools to model, explore and exploit heterogeneous data from various sources in order to understand a phenomenon. We focus on a generic model for data acquisition campaigns based on the concept of generic sensor. The concept of generic sensor is centered on acquired data and on their inherent multi-dimensional structure, to support complex domain-specific or field-oriented analysis processes. We consider that a methodological breakthrough, based on Data Science as a pivot for interdisciplinary dialog, may pave the way to deep understanding of voluminous and heterogeneous scientific data sets. Our use case concerns energy efficiency of buildings to understand the relationship between physical phenomena and user behaviors. This multidisciplinary project involves computer scientists, social and urban scientists, and thermal scientists. The aim of this paper is to give a synthetic presentation of our methodology, and an overview of our main results.
机译:我们的目标是开发理论和实用的工具来模拟,探索和利用各种来源的异构数据,以了解现象。我们专注于基于通用传感器概念的数据采集活动的通用模型。通用传感器的概念以所获取的数据和固有的多维结构为中心,以支持复杂的域或现场的分析过程。我们认为,基于数据科学作为跨学科对话的枢轴的方法突破可能会为大量和异构科学数据集提供深入理解的方式。我们的用例涉及建筑物的能源效率,了解物理现象与用户行为之间的关系。这个多学科项目涉及计算机科学家,社会和城市科学家和热科学家。本文的目的是提供我们的方法论的合成介绍,以及我们主要结果的概述。

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