Umeå University's logo

Demand response (DR) is a key strategy for enhancing energy flexibility, allowing buildings to dynamically adjust electricity demand and mitigate supply–demand mismatches—particularly in the context of rising renewable energy integration. Single-family houses (SFHs) are increasingly recognized as decentralized energy actors in advancing DR, owing to their suitability for integrating on-site generation systems such as photovoltaic (PV) panels. In such houses, an energy management system (EMS) coordinates local generation and consumption through DR optimization methods. Due to the high autonomy of single-family houses, effective DR optimization is critical for facilitating occupant participation, especially as thermal comfort significantly affects engagement. Although research in this domain is expanding, a systematic review focusing on DR optimization for SFHs with on-site generation and thermal comfort integration has yet to be conducted. To fill this gap, this review systematically synthesizes existing DR optimization methods in accordance with the PRISMA guidelines. DR optimization approaches are categorized into five groups: rule-based control, mathematical programming, metaheuristic optimization, model predictive control, and artificial intelligence-based methods. It also classifies thermal comfort integration approaches into four types: comfortable temperature zone (CTZ), comfortable temperature deadband (CTD), PMV–PPD, and adaptive thermal comfort models. A mechanistic framework integrating thermal comfort into DR optimization is developed, and a six-dimensional analysis reveals key methodological trade-offs and emerging trends. Finally, the review highlights key research gaps and outlines future directions, including refined thermal comfort metrics, occupant-centric and behavior-aware optimization frameworks, and uncertainty-aware strategies to ensure robust and scalable DR deployment in single-family houses.

Accelerating climate change is fundamentally transforming indoor thermal environments, intensifying heat exposure and variability that threaten occupant health, productivity, and well-being. In response to changing indoor conditions, occupants adopt adaptive behaviors such as adjusting clothing, opening windows, or operating HVAC systems that in turn reshape indoor environments. Understanding these complex human-environment interactions under future climate scenarios is critical for developing resilient and occupant-centric building strategies. This study proposes a hybrid experimental framework that integrates immersive virtual environments (IVEs) with a climate-controlled physical laboratory to investigate thermal-related occupant behaviors under future climate scenarios. The experimental setup combines visual immersion through virtual scenarios with precise control of thermal stimuli, enabling realistic simulation of future indoor conditions. Behavioral responses, physiological signals (e.g., heart rate, skin temperature), and psychological assessments (e.g., perceived thermal comfort, stress) were systematically collected from participants exposed to varied thermal scenarios. The collected multi-dimensional data provide a basis for modeling occupant behavior patterns and identifying the physiological and psychological factors that drive adaptive responses. The study further outlines the future integration of reinforcement learning-based occupant behavior models with building energy simulations via co-simulation, enabling closed-loop modeling of human-building interactions. This approach contributes to advancing climate-resilient building design, supporting the development of adaptive control strategies grounded in empirical occupant data.

The utilization of immersive virtual environments (IVEs) has emerged as a pivotal tool in enhancing observation of occupant-building interaction (OBI) in non-existing and pre-operational buildings (e.g., buildings under-designed, renovated, and retrofitted). The data derived from IVEs are critical in developing Building Predictive Models (BPMs) that prioritize occupant comfort and optimize building performance. Nevertheless, a persistent challenge is the collection of sufficiently large sample sizes from IVEs, often resulting in data sets inadequate for creating accurate and dependable BPMs. To address the gap, the generation of synthetic data is one promising solution. Mega-trend diffusion (MTD) is particularly adept at managing the nuances of small, mixed-type, and imbalanced data sets aligning with the natures of the IVE data sets. This study explores MTD-based algorithms such as baseline MTD, baseline MTD with class probability function, and k-Nearest Neighbors MTD (kNNMTD), all of which are adept at addressing the inherent data challenges. Various small data sets associated with OBI in IVEs were used to test these algorithms. The fidelity of the synthetic data sets is assessed using the Pairwise Correlation Difference (PCD) and accuracy of Artificial Neural Networks (ANNs) trained on the synthetic data sets with several modeling structures. A variety of findings indicated strength and limitations of the algorithms, where some areas need further investigation. At this stage, the evaluation based on this study found that the kNNMTD produced synthetic data sets that were closest to the experimental data set (i.e., the smallest PCD), contributing to the most accurate ANN models.

The quest for energy-efficient buildings necessitates a deeper understanding of how occupants interact with energy-efficient scenarios such as renovations and retrofits, designed to reduce energy consumption. Understanding occupant interaction prior to implementing such scenarios is crucial to achieving building efficiency. Traditional methods, such as survey, field studies, and simulations face challenges in accurately investigating and eliciting occupant interaction to energy-efficient scenarios due to significant limitations of engaging occupants. In response to limitation, this paper explores a capability to engage occupants of a novel integrated method that leverages Immersive Virtual Environments (IVEs), creating realistic simulations of building energy-efficient scenarios within controlled environments. It integrates a thermal-controlled environment providing thermal stimuli, allowing for examination of occupant interaction, fostering robust data collection under realistic conditions. This method aims to enhance understanding of the relationship between occupant behavior, building energy-related scenarios, particularly by emphasizing realism and engagement. Case studies simulated building and functions for studying occupant interaction to energy-efficient scenarios, exploring links to thermal perceptions. It investigated occupants’ engagements using post-interviews, in which the results highlight strong potential of the method to elicit truthful occupants’ perception and interaction, and enhance occupant engagements. Overall, this IVE-integrated method paves the way for further exploration of immersive technologies to enhance understanding of occupant-building interaction dynamics, leading to potentially improved interactions and sustainable building practices.

The successful achievement of demand flexibility relies heavily on effective building energy management (BEM) to manage users' diverse needs and priorities. Building on the day-Ahead demand response program, this paper proposes a many-objective BEM framework that supports user-centric decision-making by considering electricity cost, thermal comfort, inconvenience, and CO2 emissions objectives. The energy modelling of photovoltaic (PV), battery storage system (BSS), ground source heat pump (GSHP), and the flexible loads is first developed. A case study is conducted on a typical Swedish detached house, where the non-dominated sorting genetic algorithm (NSGA)-III is applied to solve this many objectives optimization problem and obtain the Pareto frontier set. When all objectives are weighed equally (0.25 each), results show the proposed BEM strategy helps save 43.8% of daily electricity cost and reduces 36.3% of CO2 emissions compared to the scenario with no PV or BSS used; 29.8% of daily electricity cost and 14.5% of CO2 emissions are lowered when compared with the scenario with PV and BSS applied but without management. These findings demonstrate the proposed BEM can effectively support users' different priorities for decision-making in practical applications to enhance demand flexibility.

Energy-efficient renovations significantly affect how people use buildings, and these occupant behaviors, in turn, influence the effectiveness of building renovations. Exploring interactions between occupants and renovations is essential for implementing building energy-efficient renovation. However, physical experiments for this purpose require extensive setups in the laboratory to observe occupant behaviors under various renovations. Immersive virtual environment (IVE) experiments as an emerging method still need to adequately incorporate thermal stimuli, essential for studying occupant behaviors, building renovations, and related interactions. Therefore, this study proposes a novel approach that integrates virtual and physical environments in experiments to explore occupant behaviors and their interactions with building renovations. The interactive and immersive capabilities of IVE experiments allow for effective simulation of various renovations and occupant behaviors. By incorporating thermal stimuli from physical experiments, this approach overcomes previous limitations in studying thermal-related occupant behaviors. In a field study, an office building looking for renovation is used to explore occupant behaviors and their interactions with building renovations. It is found that energy-efficient renovation impacts personal heater use and door opening behaviors, but not clothing behaviors; such changes in heater use subsequently impact the energy performance of building renovation. In further analysis, obvious correlations are revealed between personal heater use and renovation scenarios, thermal perception, and times of day. The proposed approach is validated as a novel method to engage the occupants in achieving occupant-centric building energy-efficient transitions.

Temperature extreme weather is rapid changes in heat or cold temperatures that occur suddenly and persists for days to weeks, having an important impact on inhabitants' indoor living comfort and health. During temperature extreme weather, inhabitants may experience indoor overheating or overcooling and engage in self-adaptation to build their own resilience against these threats. However, self-adaptation alone may not adequately address the climate change challenge, external support is necessary in this process. This study aims to configure an experimental platform in the Intelligent Human-Buildings Interactions lab (IHBI) at Umeå University, to create a well-controlled experimental environment to investigate how inhabitants adapt to temperature extreme weather. The IHBI lab is developed by a hybrid virtual-physical framework, with the ability to simulate extreme weather and observe inhabitants' adaptive behaviors, facilitating the development of behavioral guidelines, interventions, and other external supports for inhabitants' adaptations. A pilot study validated the accuracy and authenticity of the developed experimental platform.

There is a lot of confusion and ambiguity regarding the quantification of the Quality of Service (QoS) of a system, especially for cyber-physical systems (CPS) involved in automating or controlling the operations in built environments and critical urban infrastructures, such as office buildings, factories, transportation systems, smart cities, etc. In these cases, the QoS, as experienced by human users, depends on the context in which they (i.e., humans) interact with these systems. Traditionally, the QoS of a CPS has been defined in terms of absolute metrics. Such measures are unable to take into account the variations in performance due to contextual factors arising out of different kinds of human interactions. Further, the QoS of a CPS has typically been evaluated by comparing the performance of the actual, fully realized system with the given QoS constraints only after the actual system has been completely developed. In the case of faults in the design exposed by observed deviations from the QoS constraints due to unpredicted variations in the contextual factors, the system needs to be re-designed and re-developed from scratch. Due to the above-mentioned reason, the validation approach associated with the traditional QoS makes the design of CPS systems prohibitively expensive, impractical, as well as infeasible in numerous application areas, such as civil and engineering works, since it may not be possible to modify the system once developed beyond a certain extent. To that end, we propose a context-aware definition of QoS of a CPS which facilitates the design of robust systems as elaborated below. In this paper, we define QoS as a function of contextual factors. A CPS designed according to our QoS specifications would always satisfy the QoS irrespective of any possible changes in contextual factors resulting from many different human interactions that may occur during operation of the system. We also present QACDes - a novel framework that provides a formal mechanism for validating the design of a CPS with respect to the specified QoS constraints at the design phase as well as after the realization of the actual system. QACDes can validate any given CPS, irrespective of its application domain, against a QoS guarantee: (A) as early as even before the design phase by comparing the proposed model with a baseline model, or (B) after the realization of the actual system based on logs collected from running the actual system. We consider a lighting control system that manages the light switches - switching it on/off depending on contextual factors, such as the presence of occupants and time of the day. Using the lighting control system in a building as a use case, we analyze and demonstrate the effectiveness of our QoS definition as well as the QACDes framework against the performance metric measured in an actual fully-realized CPS.

Scientific experiments significantly enhance the understanding of human-building interactions in building and engineering research. Recently, conducting virtual reality (VR) experiments has gained acceptance and popularity as an approach to studying human-building interactions. However, little attention has been given to the standardization of the experimentations. Proper standardization can promote the reusability, replicability, and repeatability of VR experiments and accelerate the maturity of this emerging experimentation method. Responding to such needs, the authors proposed a virtual human-building interaction experimentation ontology (VHBIEO). It is an ontology at the domain level, extending the ontology of scientific experiments (EXPO) to standardize virtual human-building interaction experimentation. It was developed based on state-of-the-art ontology development approaches. Competency questions (CQs) were used to derive requirements and regulate the development. Semantic Web technologies were applied to make VHBIEO machine-readable, accessible, and processable. VHBIEO incorporates an application view (APV) to support the inclusion of unique information for particular applications. The authors performed taxonomy evaluations to assess the consistency, completeness, and redundancy, affirming no occurrence of errors in its structure. Application evaluations were applied for investigating its ability to standardize and support generating of machine-readable, accessible, and processable information. Application evaluations also verified the capability of APV to support the inclusion of unique information.