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

Abstract

The building sector is undergoing a deep transformation to contribute to meeting the climate neutrality goals set by policymakers worldwide. This process entails the transition towards smart energy-aware buildings that have lower consumptions and better efficiency performance. Digitalization is a key part of this process. A huge amount of data is currently generated by sensors, smart meters and a multitude of other devices and data sources, and this trend is expected to exponentially increase in the near future. Exploiting these data for different use cases spanning multiple application scenarios is of utmost importance to capture their full value and build smart and innovative building services. In this context, this paper presents a high-level architecture for big data management in the building domain which aims to foster data sharing, interoperability and the seamless integration of advanced services based on data-driven techniques. This work focuses on the functional description of the architecture, underlining the requirements and specifications to be addressed as well as the design principles to be followed. Moreover, a concrete example of the instantiation of such an architecture, based on open source software technologies, is presented and discussed.

A Next Generation Library of AI-Based Data-Driven Services for the Built Environment

“…In certain cases, the results of these services may be accessible via web-based applications or APIs. However, there are other instances where the output of analytics services can serve as an input to more sophisticated systems, such as building digital twins [18]. Building digital twins can be perceived as virtual replicas of physical buildings that integrate data from various sources, including energy and building analytics, to provide real-time insights into the building's performance and operations.…”

"Bd4nrg Query Engine – Intuitive, Efficient and Federated Querying on Big Data"

“…Similarly, in (Pau et al, 2022) and (Alexakis et al, 2022) another big data system for querying analytics on buildings sector is analyzed. The first step is data preprocessing to remove incomplete or inaccurate data.…”

"Twinp2g: A Digital Twin Architecture for Optimal Power-to-Gas Planning"

“…Specifically, Streamlit (Streamlit, 2022) is a candidate high-level technology for serving this purpose. However, more advanced technologies, such as Apache Superset (Superset, 2022) and MATRYCS (Pau et al, 2022) Visualization Engine (Kormpakis et al, 2022) are also under investigation. The second persona is the "Energy Engineer" who is considered an end-user with knowledge and understanding of energy systems but limited coding and modelling skills.…”