Big Data Value Chain: Multiple Perspectives for the Built Environment

Abstract

Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area.

The BD4NRG Reference Architecture for Big Data Driven Energy Applications

“…In the field of energy efficiency measures it is absolutely critical to measure and integrate diverse heterogeneus sources of data [20]. Moreover, those sources must have a high degree of confiability with a focus on providing evidence to stakeholders, enabling both the analysis of actual consumption and energy efficiency and the prediction of the best pathways for investments with the maximum returns in terms of reduced energy costs and sustainability [21].…”

MATRYCS—A Big Data Architecture for Advanced Services in the Building Domain

“…Batch data instead generally consist of large datasets that need to be processed a posteriori all at once. Examples of these data are large sets of cadastral data or of energy performance certificates (see [36] for an exhaustive list of building-related data repositories). Due to the diverse nature of the data, different solutions are typically employed for their communication.…”

“…These data are largely heterogeneous and they come with very diverse formats, sizes, varying granularity, quality, etc., thus posing serious obstacles to the effective handling and exploitation of data. Furthermore, data are typically dispersed in different locations and non-interoperable platforms [36] , which prevents extracting their full value, using them for multiple use cases and creating advanced applications based on cross-domain information. As data may contain sensitive information, ensuring data privacy is also a main concern, similar to providing data sovereignty and cybersecurity to protect the business value of the data [37].…”

Co-designing an interactive data platform for contextualizing the role of citizens on energy and low-carbon transitions

“…In terms of architecture, the platform was designed to handle dataintensive API transactions while keeping high usability standards. While the output data of energy and climate change mitigation models is not approaching the volume or velocity of big data in other fields (e.g., data from sensors and services related to the built environment [33], [34]), they still are challenging to handle, especially by non-modelers. In order to achieve both fast interaction and high customizability in terms of the type and format of results that are available, I 2 AM PARIS relies on three components.…”