DOCENT – Development of Occupant-centric Control for ENergy efficienT buildings
The building sector is facing a digital revolution that is bringing new opportunities towards smart and sustainable buildings. Occupants are no longer seen as mere recipients of the indoor climate but as active agents in the optimal operation of buildings to achieve better indoor climate and improved energy efficiency. Digitalization enables a better understanding of occupancy and behavioral patterns and their impact on energy use, but many challenges are still unsolved. This project will address them and develop occupant-centric control systems through an innovative hybrid research approach, using data-driven tools and digital twins. Environmental, societal, and economic benefits will be demonstrated on both standard and state-of-the-art smart building testbeds.
Background
The building sector is a major contributor to the overall energy demand and CO2 emissions, accounting for about one third of the overall energy use and CO2 emissions in Sweden and worldwide. In the last decades, the buildings sector has gone a long way towards efficiency through renovation of building envelopes and energy systems, and the European Energy policy is promoting the transformation of the buildings from consumer to prosumer; incentives are spent towards buildings that are potentially smart-grid ready, which facilitate local renewable energy generation. Digitalization has opened up new possibilities, boosting the pace towards energy efficiency in both existing and newly designed buildings, enabling at the same time improvements in the well-being of their occupants. Despite these efforts, the building sector is still falling behind desired sustainability targets and needs a renewed impulse to embrace new opportunities.
The understanding of building dynamics enabled by smart buildings has led to the acknowledgment that occupants are an increasingly critical factor for low-energy buildings and that proper occupant control can lead to increased energy efficiency. A surge of research on occupancy (i.e., human presence in buildings) and occupant behavior in the past decade has significantly advanced the state of knowledge in this field, but buildings are still operated based on outdated and simplistic assumptions about occupants. Building occupants can no longer be seen as recipients of indoor environmental conditions, for instance formally included as disturbances in the analysis, but instead, they should be included as decision-makers whose dynamics is affected by and affects the surrounding building conditions. In parallel, occupancy patterns need be taken into account for optimal building operation.
Project description
The main research objective is to advance the understanding of occupants’ behavior toward occupant-centric building automation, by comprehensively assessing its impact on residential and commercial buildings. Key aspects to achieve the research objective are the characterization models of human behavior in smart buildings, the development of occupant-centric control schemes and the implementation of developed techniques on real-time testbeds and estimation of the savings in energy use enabled by occupant-centric controls.
The project focuses on the KTH Live-In Lab building testbeds, exploiting the high-resolution, real-time data gathered through the advanced sensor and data infrastructure. This state-of-the-art building infrastructure has proved to overcome many past limitations to building innovation, with research projects motivated by current societal-scale challenges. The KTH Live-In Lab features two residential building testbeds (Testbed KTH and Testbed EM) and an office building (Testbed AH), equipped with high-resolution monitoring and control systems; in addition, close interaction with the building occupants is enabled.
Among the facilities, the Testbed KTH provides the most advanced monitoring system and will enable high resolution retrieval of behavioral data. The testbed EM features local energy generation (PV), an efficient energy generation system (ground source heat pumps), and storage systems (batteries and TABs). It will be used to monitor a large group of occupants and analyze the scaling properties of the approaches developed in the Testbed KTH, aiming at optimizing the operation and the economic profitability of large-scale building energy systems. The Testbed AH will be used to perform detection and model the dynamics of occupancy in shared spaces.
This project will, in order to evaluate the cost-effectiveness of smart buildings. As a result, the operative definition of smart building will be enabled. Sensor measurements will be used to identify common faulty settings in buildings’ ventilation and heating systems, estimating their impact on the energy use. Particular attention will be dedicated to the user experience, the impact of the users’ energy use, and visualization techniques to promote energy-efficient behaviours.
Project goal
The goal of the project is to improve the energy efficiency of buildings and the well-being of the building occupants by addressing critical features of building smartness. The goal will be achieved through three main objectives. First, the project will advance the knowledge of the complex interaction between building systems and building occupants with improved, scalable, and generalizable behavioral models derived from empirical data. Second, the behavioral models will be integrated into innovative adaptive control approaches tailored to the needs of the occupants, minimizing the risk of energy-inefficient behaviors and interference with the optimized operation and control; in parallel, occupancy-based controls will be designed and tested. Third, incentives in the form, e.g., of tailored and contextualized information, will be developed to increase the energy-awareness of building users.
Implementation
The project is organized in five work packages, described below, dealing with data collection, modeling of behavior and occupancy, design and implementation of occupant-centric control strategies, and dissemination of the results.
WP1 Data management and integration and sensor evaluation
WP2 Data-driven occupancy estimation, behavior analysis and modeling
WP3 Occupancy-based energy optimization
WP4 Occupant-centric control and operation optimization
WP5 Project management and dissemination
Time period:
July 2024- December 2027
Publications
M. Farjadnia, A. Fontan, A. Alanwar, M. Molinari, K. H. Johansson, Robust Data-Driven Tube-Based Zonotopic Predictive Control with Closed-Loop Guarantees, CDC2024, 63rd IEEE Conference on Decision and Control, December 16-19, 2024 (accepted)
Project team
Marco Molinari, Researcher, marcomo@kth.se Profile
Angela Fontan, Assistant Professor, angfon@kth.se Profile
Mahsa Farjadnia, Ph.D. candidate, mahsafa@kth.se Profile
Schools
School of Industrial Engineering and Management Department of Energy Technology
School of Electrical Engineering and Computer Science , Division of Decision and Control Systems
Project partners
Akademiska Hus, Myrspoven, Einar Mattsson, Schneider Electric
Research areas
Smart buildings, Advanced Controls for Buildings, Monitoring data, Data Analysis.
Project duration
42 months
Funding
This project is financed by the Swedish Energy Agency (Energimyndigheten) under the Resurseffektiv bebyggelse program.