Factsheet: Data-Driven Smart Buildings (PDF 0.32MB) 
November 2019

Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives 
August 2023
Author(s): H. Li, H. Johra, F. de Andrade Pereira, T. Hong, J. Le Dréau, A. Maturo, M. Wei, Y. Liu, A. Saberi-Derakhtenjani, Z. Nagy, A. Marszal-Pomianowska, D. Finn, S. Miyata, K. Kaspar, K. Nweye, Z. O'Neill, F. Pallonetto, B. Dong
Editor(s): Applied Energy
Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 48 data-driven energy flexibility KPIs from 87 recent and relevant publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, standardizing data-driven energy flexibility quantification and verification processes; and (4) curating and analyzing datasets with proper description for energy flexibility assesment.

Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate 
June 2023
Author(s): I. Sartori, H.T. Walnum, K.S. Skeie, L. Georges, M.D. Knudsen, P. Bacher, J. Candanedo, A.M. Sigounis, A.K. Prakash, M. Pritoni, J. Granderson, S. Yang, M.P. Wan
Publisher: Data in Brief
This article presents datasets suitable for advanced control applications relating to indoor climate and energy use in buildings.
Data was independently collected from 6 real buildings by Annex81 researchers. The data includes energy meter data, both for consumption and PV generation, and sensors of technical installation and indoor climate variables, such as temperature, flow rate, relative humidity, CO2 level, illuminance. Weather variables were either acquired by local sensors or obtained from a close by meteorological station.

A Data Sharing Guideline for Buildings and HVAC Systems (PDF 2.17MB) 
March 2023
Author(s): Stephen White, Anna Marszal-Pomianowska, Guang Yu Jin, Henrik Madsen, José Candanedo, Mirjam Harmelink, Sofia Stensson and Virginia Gori
The benefits of digitalisation are somewhat predicated on access to relevant data, in a way that is cost-effective, trustworthy, flexible, and consistent with obligations to manage privacy and commercial rights.
Applying the FAIR data principles, this guide discusses concepts and language relevant to data-quality, data-management, and data-governance. It aims to help building owners and government policy makers to obtain greater access to, and control over their data, to unlock markets and to source competitive smart building services.

Data-Driven Smart Building Case Studies 
November 2022
Author(s): P. Ruyssevelt, D.V. Rovas, V. Gori, G. Chen, and H. Jatkar
The work presented on the website (https://datasmartbuildings.org/) aims to gather and report on the technical details, business cases, and stakeholder stories associated with examples where data-driven solutions have been implemented in the real world. Each case study focuses on a particular building, technology, or dataset. Individually, the case study descriptions aspire to highlight a specific facet of applying data-driven smart building technologies. Collectively, the case studies help garner an understanding of the current state of practice and possibly help identify a path forward to critically understanding some of the benefits and challenges associated with data-driven smart buildings.

Survey of metadata schemas for data-driven smart buildings (PDF 2.38MB) 
June 2022
Author(s): Gabe Fierro and Pieter Pauwels
Publisher: CSIRO
A survey of metadata schemas for data-driven buildings is presented. The aim of this document is to provide insight and clarity into the overall structure, as well as trade-offs and arguments behind each of the major metadata schemas available for data-driven smart buildings. This includes context relating to how those metadata schemas are applied in practice.
Annotating sensor, meter, IoT and other building device data, so that it can be re-used as effectively and meaningfully as possible (regardless of the building typology, location or fabric), is a task best achieved by a common/standardised metadata schema. Creating portable applications that use metadata, to mask the inherent complexities of each building, allows the proliferation of value-adding applications and services.
The intended audience of this paper includes building owners, commissioning agents, system integrators and software developers, who are in a decision-making position for choosing or requiring a metadata standard for enabling analytics.

A Reference Architecture for Data-Driven Smart Buildings Using Brick and LBD Ontologies 
May 2022
Author(s): P. Pauwels and G. Fierro
Publisher: Clima Conference
With the increasing adoption of sensors, actors and IoT devices in existing buildings, the real estate sector is becoming increasingly automated. Not only do these devices allow to monitor these buildings (energy use, occupancy, indoor air quality, etc), they also enable modelpredictive control (MPC) through building automation and control systems (BACS). A critical feature to enable these is the metadata associated to data streams obtained from the building. Such metadata allows building operators to assess what these data streams are, what they are measuring and how. This can be achieved using metadata schemes and vocabularies, such as Brick, Haystack, Linked Building Data, Industry Foundation Classes. Merging these model-based metadata schemas (semantics) with data-driven monitoring and control (machine learning) into a functional system architecture is a considerable challenge. In this paper, we review the mentioned technologies and propose a draft reference architecture based on state-of -the-art research. This reference architecture is evaluated using a set of predefined criteria.