Structuring heterogeneous urban data: A framework to develop the data model for energy simulation of cities

Globally, there is accelerating interest in cities and the analysis of their underlying systems to understand, project, and propose sustainable energy transformations. These urban areas represent a complex combination of heterogeneous data, which complicates the process of engineering modeling and predicting changes that could occur. Identifying the data sources related to the urban objects and their associated characteristics is essential. While some schemas are developed to collect, consolidate, and organize the required data, such as CityGML, to describe urban geometry semantically, they lack the capability to include different use cases and objectives to adequately parametrize simulation models. There is a lack of a well-structured framework for data modeling to efficiently organize the dispersed data needed for urban energy simulation. This article proposes a framework to develop urban data models connecting with urban energy modeling tools for energy assessments in the city context. The paper's main contribution is presenting a novel implementation methodology for processing, modeling, capturing the results, and populating other existing standard data formats. The data model represents a key element in a novel urban simulation platform and aims at real-time capturing, storing, and investigating the various aspects of city-scale behavior and performance.

Automated summary

Globally, there is increasing interest in understanding and proposing sustainable energy transformations in cities. However, the complexity of urban areas and the lack of a well-structured framework for data modeling make engineering modeling and predicting changes challenging. This article presents a framework for developing urban data models connected to energy modeling tools, aiming to assess energy in the city context. The main contribution is an implementation methodology for processing, modeling, and capturing results, as well as populating existing data formats.