Data-Driven Smart Buildings
DDSB
The Museum of London (MoL) has embarked on an extraordinary journey to create a new museum for London (UK). This is a once-in-a-generation opportunity to reconceive what a museum for London can be and deliver the best City Museum in the world. Accordingly, we are designing the new museum as a smart building. To inform the design, implementation and operational use of the smart museum, we created a smart building prototype at one of our other museum properties and an additional office and storage facility — these are the Museum of London Docklands and Mortimer Wheeler House. This case study refers to this prototype.
The Museum of London is a public institution that collects, preserves and displays the history and culture of London from prehistoric times to the present. MoL currently manages and operates three buildings located in the Greater London Area: (1) the Museum of London Docklands, a 200-year-old warehouse close to the river Thames in Canary Wharf; (2) the Mortimer Wheeler House, the largest archaeological archive in the world; and (3) the Museum of London Wall, which closed in December 2022 to prepare for the move to a new smart Museum of London at West Smithfield, opening in 2026. Since 2020, the existing museums have been part of a major digital transformation project that aims to inform the development of the new smart museum at Smithfield, enabling MoL to evaluate energy, carbon, building performance and efficiencies across the entire portfolio, including the connectivity of the buildings with the grid.
The technology installed is to capture system data from the building management system (BMS) and the energy meters. The BMS is Trend 963. We used Tridium Jace devices and Tridium Niagara to collect and manage the data. We use Influx-DB as our time series database and a mosquito MQTT broker.
Data are confidential.
This project started from a desire to build a small prototype, get hands-on involvement and practical experience at a small manageable scale, and to gather learnings to feed into the design of the new Museum of London. When this project was started, the new museum design was already at RIBA Plan of Work stage 3 (i.e. spatial coordination) and in 2020 it was not designed as a smart museum. This changed between RIBA stage 3 and 4 (i.e. technical design completed) and so some of the thinking and planning had already begun. The asset naming convention, for example, had already been decided on the Smithfield project and so it was possible to apply and test this in the prototype to confirm its suitability in the real-world application.
We aimed to build a prototype and experience some of the design and implementation errors in a safe, less critical environment. Further, we wanted the system to work as a pilot, so that we could test the user interface and impact on the facility management team. Then, feedback and learnings could be gathered and fed into future decision making. Additionally, we wanted to engage with the smart building market, the master system integrators and other vendors, to understand the businesses in London operating in this field.
The motivation for the project is to make a significant difference in the way buildings are managed and operated. The aim is to use data to expose accurate understanding of the status of building performance and thereby using a shared understanding (amongst all involved in the building management and operation) to drive improvements across the client team and contractors working in the fields.
The project employs a variety of technologies to create an integrated smart building system. It utilises Trend’s 963 as a building management system and Tridium’s Jace and Niagara platforms for seamless building management and control, alongside the Mosquito Broker for efficient IoT messaging. The OnPoint software enhances user interaction with the building’s systems, providing a clear and intuitive interface. A LoRaWan network supports connectivity for 12 battery-operated IoT sensors, gathering crucial environmental data. To ensure consistency and interoperability, the project adopts building device naming standards (BDNS) for asset naming; aligns data models through initiatives such as the Buildings IoT ontology alignment project (OAP); employs Haystack tagging for semantic consistency; and uses Grafana for advanced data visualization. Infrastructure-wise, the system runs on two virtual machines hosted on existing on-site servers: one MS Windows machine for Niagara 4 and one Unix machine for the Influx DB, optimising resource use and data management in the smart building ecosystem.
