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Annex 81 Publications
Factsheet: Data-Driven Smart Buildings
Data-Driven Smart Buildings: State-of-the-Art Review
Author(s): David Blum, José Candanedo, Zhelun Chen, Gabe Fierro, Virginia Gori, Hicham Johra, Henrik Madsen, Anna Marszal-Pomianowska, Zheng O’Neill, Ojas Pradhan, Dimitrios Rovas, Francesco Sacco, Sofia Stensson, Christian A. Thilker, Charalampos Vallianos, Jin Wen, Stephen White
Editor(s): José Candanedo
This report reviews the state-of-the-art related to Data-Driven Smart Buildings research and technologies. It explores issues relating to IT infrastructure and data management procedures necessary for streamlining the deployment of data-driven software solutions. It also examines recent developments in data-driven approaches for optimising building energy performance, including fault detection and diagnosis, advanced control strategies and the interaction between buildings and the electric grid. The report covers the following aspects of the utilisation of data in building operation: It includes an Annex81 definition of a data-driven smart building, and learnings from various case study deployments in buildings.
Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives
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.
A review of data-driven fault detection and diagnostics for building HVAC systems
Author(s): Z. Chen, Z. O’Neill, J. Wen, O. Pradhan, T. Yang, X. Lu, G. Lin, S. Miyata, S. Lee, Ch. Shen, R. Chiosa, M.S. Piscitelli, A. Capozzoli, F. Hengel, A. Kührer, M. Pritoni, W. Liu, J. Clauß, Y. Chen, and T. Herr
Publisher: Applied Energy
With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained from data via machine learning or multivariate statistical analysis methods. Following this definition, this paper reviews and summarizes the literature on data-driven FDD from three aspects: process, systems studied (including the systems being investigated, the faults being identified, and the associated data sources), and evaluation metrics. A data-driven FDD process is further divided into the following steps: data collection, data cleansing, data preprocessing, baseline establishment, fault detection, fault diagnostics, and potential fault prognostics. Literature reported data-driven methods used in each step of an FDD process are firstly discussed. Applications of data-driven FDD in various HVAC systems/components and commonly used data source for FDD development are reviewed secondly, followed by a summary of typical metrics for evaluating FDD methods. Finally, this literature review concludes that despite the promising performance reported in the literature, data-driven FDD methods still face many challenges, such as real-building deployment, performance evaluation and benchmarking, scalability and transferability, interpretability, cyber security and data privacy, user experience, etc. Addressing these challenges is critical for a broad real-building adoption of data-driven FDD.
Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate
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
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
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
Author(s): Gabe Fierro and Pieter Pauwels
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
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.