Authors:
Juan Mahecha Zambrano. Politecnico di Milano, Department of Architecture, Built Environment and Construction Engineering
Ulrich Filippi Oberegger. European Academy of Bolzano/Bozen, Institute for Renewable Energy
The urgent need to reduce anthropogenic greenhouse gas emissions has raised the attention given to energy consumption in the building sector. Stringent policies have been placed around the world for promoting an energy-efficient, flexible, and resilient built environment.
In the European Union, the Energy Performance of Buildings Directive [1] established a pathway for achieving a low and zero-emission building stock by 2050. Furthermore, a paradigm shift is being encouraged towards occupant-centric building design and operation that acknowledges the value of healthy and comfortable indoor environments, as well as occupants as a key factor for low-energy buildings [2].
Building Performance Simulations (BPS) are extensively used to support the design and operation decision-making process. Nevertheless, it is often observed that there is a difference between the predicted and measured building performance, the so-called building performance gap [3].
Consequently, BPS studies could mislead practitioners towards choosing sub-optimal designs or retrofit options, overestimating saving benefits from energy conservation measures, etc. Weather deviations, differences between design versus ‘as-built’, and occupant behaviour are among the most important sources of uncertainty contributing to the building performance gap [4].
Regarding the occupants, they are traditionally represented in BPS using standard schedules and occupant-related power densities, which assumes they are passive and homogeneous agents. This oversimplification of the occupant diversity and complex stochastic nature of the human-building interaction has led the research community to focus on better understanding occupant behaviour (OB), improving modelling strategies, and integrating these into the building simulation workflow.
Programs such as the International Energy Agency – EBC Annex 66 [5] and Annex 79 [2] have driven an international collaboration that puts OB in a hot-spot of research, resulting in a variety of advanced modelling approaches and more than 300 OB models published [6]. It must be stressed that these advances are mainly constrained to the research domain and their application is limited.
This is because the stakeholders are not well informed about the added value of integrating advanced OB models so no additional resources are added to the projects, building standards and codes do not guide or motivate their implementation, and BPS tools are lacking a full integration of these advances [7].
This article aims to explore the opportunities that advanced occupant behaviour models give BPS users. From evaluating the design’s degree of flexibility and ability to adapt to changes in occupancy and OB, to facilitating the realisation of net-zero and low energy buildings that enhance the wellbeing of occupants.
In detail, this article presents fundamental concepts related to the human-building interaction and advanced modelling strategies, highlights and illustrates the added value of integrating OB models in the building design and operation decision-making process, summarises the main issues to be considered when applying such models, and presents an outlook for the field.
Fundamental concepts
OB can be divided into occupancy, adaptive, and non-adaptive behaviours. Occupancy relates to the presence of people in different building spaces, the number of people and their level of activity. Adaptive behaviours are those driven by a need for adjusting the indoor environment such as windows, lights, blinds, and thermostat operation. Non-adaptive behaviours are related to contextual factors and the activities occupants perform within the building such as the use of appliances [7].
Significant effort has been put into understanding the drivers motivating OB and their influential factors [8]. It has been found that not only environmental but time-related, contextual, physiological, psychological, social, cultural, and random factors influence OB. This has important implications on the modelling and integration of OB into BPS because it is virtually impossible to develop generalised OB models, thus the choice of the models become context- and case-specific [9].
OB models have been developed for optimizing the building design by including a representation of occupants in BPS and predicting OB to be included in control strategies. The first point requires models with high explanatory power, for example, the casual explanation behind the operation of blinds might be visual discomfort that can be correlated to solar radiation. In control strategy applications, it is necessary to have a high predictive power to accurately forecast OB related states and events [6].
Regarding modelling approaches, starting from fixed schedules and rule-based models, and increasing the level of model complexity, options such as data-based, stochastic, machine learning, and agent-based models have been explored [10]. Further, the models can be static or dynamic depending on the way they interact with building simulation. The former is set as a fixed input for the simulation, while the latter interacts continuously with the simulation in a bidirectional way.
It is important to stress that higher complexity models are not necessarily more accurate. The nature of the model, the amount of information required to develop or calibrate the model, and its complexity will serve a different purpose, so that its selection needs to be carefully assessed. For example, at early design stages where the amount of information regarding the occupants is generally scarce, low complexity models that tend to require fewer data could be more appropriate.
Conversely, when sizing building systems, agent-based models can provide the level of resolution required [7]. Ongoing research in the OB field has acknowledged that one of the most important gaps to be addressed is the lack of systematic guidelines and methods to support BPS users on the selection of the most suitable modelling approach. The nature of the occupant behaviour, the characteristics of the models, and the variety of applications and simulation purposes demand a fit-for-purpose strategy that balance complexity, accuracy, available information, and resources. Further information on this topic can be found in [7,11,12].
The potential
The application of OB models goes beyond estimating Key Performance Indicators (KPI) with more accuracy. They unlock vast potential for promoting buildings that better adapt to changes in occupancy, people’s lifestyles, and preferences. Occupant-centric building design and operation strategies can be better designed and implemented to successfully fulfil environmental requirements while promoting the wellbeing of occupants.
During the building design process, dynamic models capture the two-way human-building interaction and can thus be included to assess how different design alternatives influence OB. When evaluating occupant comfort, the type, number, and frequency of interactions with the building systems could be used as a proxy for comfort. Furthermore, when using stochastic models, instead of informing design decisions with a single value for the KPI, its probability distribution can be estimated. In other words, it the range, variability and expected value of the building performance considering the occupant’s diversity can be estimated.
Finally, how robust the building performance is against OB, or which design alternative better answers the design and operational targets can be assessed. For critical aspects, strategies such as control logic, engagement programs, etc. can be put in place to reduce the variability. Illustrative examples considering different stages of the building life cycle can be found in [7].
Regarding building operation, models with high predictive power such as machine learning models can be used to learn from the occupants for minimising energy consumption while enhancing the Indoor Environmental Quality (IEQ). This field has great potential with the advances of the Internet of Things (IoT) and highly monitored buildings that make a significant amount of information available for continuously training models.
As a result, occupant-centric control strategies are emerging that include occupant-centric and occupant behaviour-centric control logics. The former focuses on the presence/absence of occupants, while the latter focuses on occupant preferences inferred from occupants’ interaction with the building systems [13]. This brings great opportunities to study smart, flexible building systems, control logics and human-building interaction strategies that adapt to the lifestyles of occupants, towards energy saving and high IEQ.
The application
The literature on the state-of-the-art OB research field shows significant advances in a fundamental knowledge domain. This includes advances in OB data collection techniques, sensing technologies and privacy and ethical issues; advances in the understanding of drivers influencing OB, OB modelling approaches and models; advances in simulation strategies for design and control application.
The application of OB models is at an early stage, and its potential is demonstrated mainly as proofs-of-concept. To scale the application and integration of OB models into the building design and operation process, it is urgent to advance the protocols and guidelines for supporting the process and tools that facilitate its integration. Additionally, real case studies are required to demonstrate the proofs-of-concept [7,14].
At Eurac Research, several projects have been used as scenarios for better understanding the human-building interaction, integrating advanced OB modelling strategies and developing occupant-centric design and control strategies. For example, in the EU ERDF project E2I@NOI - Definition of a Laboratory System for the development, characterization and transfer of technologies enabling building ‘Energy-Intelligent Buildings’, a set of occupant behaviour profiles were developed to be used in the laboratory when testing the novel solutions for the Italian context.
As a result, the performance of the solutions can be assessed considering typical occupant scenarios and better optimised for the application context. In the EU ITA-CH project BIPV Meets History, the feasibility of building integrated photovoltaic solutions was evaluated for the region of Como, Italy. Using a stochastic model of occupancy, lighting use and appliance use, synthetic electricity demand profiles were generated for selected building archetypes. Using the Italian Time Use survey established models for different contexts (i.e. UK) were calibrated and adapted to the Italian reality.
This strategy allowed the reduction of uncertainty when estimating the solar potential and performing socio-economic analysis. In the EU ERDF project SINCRO, machine learning models were trained from monitored data of occupants’ presence to adjust the control logic of heating systems. These models could be used in BPS during the design phase of similar and related buildings. Additional topics that are being explored include occupant behaviour-centric logic for smart thermostats that are based on occupant actions that infer their preferences.
Finally, acknowledging the lack of tools for supporting practitioners when choosing OB models, within the EU H2020 project Cultural-E an atlas that provides several layers of information including published OB models for different European contexts has been created.
Outlook
This article briefly discussed the fundamental concepts regarding human-building interaction, its integration into simulation-aided building design and operation, and the potential for developing occupant-centric design and operation strategies. Ultimately, advanced OB models and occupant-centric strategies unlock great potential for promoting a flexible built environment that focuses on people’s wellbeing, that can adapt to occupancy and occupants’ preferences and changes, and reduce energy consumption. Nevertheless, despite the field gaining maturity, an effort is required to promote its application, in particular:
In parallel, advances in the field should be better integrated with the smart buildings field, understanding the smartness of buildings as an organic network of data, building systems and the people that can learn and adapt to fulfil the needs of occupants.
References
