• Energy Research

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A lowcost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions.

Abstract In this paper we present a methodology to map individual occupants' thermal preference votes and indoor environmental variables into personalized preference models. Our modeling approach includes a new Bayesian classification and inference algorithm that incorporates hidden parameters and informative priors to account for the uncertainty associated with variables that are noisy or difficult to measure (unobserved) in real buildings (for example, the metabolic rate, air speed and occupants’ clothing level). To demonstrate our approach, we conducted an experimental study in private offices by considering thermal comfort delivery conditions that are representative of typical office buildings. Personalized preference models were developed with the training dataset and the developed algorithms were used in a detailed validation process. The proposed model showed better prediction performance compared to previous methods. Towards realization of preference-based control systems, this study also addresses practical limitations associated with controlling model complexity and data efficiency as well as using effective model evaluation metrics to train reliable personalized preference models in the real world.

A comfortable outdoor environment can be achieved through well-designed physical planning, which includes the layouts of buildings, covering materials, grass, trees, water areas, etc. In this study, the outdoor environments of an apartment complex were measured quantitatively. The paving, grass and water areas were chosen to analyse the effects of ground cover on the outdoor environment. An unsteady-state computational fluid dynamics (CFD) simulation was also conducted with the same conditions as the measurement. In order to obtain the reliability and limitation of the simulation, the results of the measurement and the simulation were compared. The effects of changing the green area, including the grass and the trees, to 15% and 50% were analysed by the simulation method. Depending on the ground cover, the air temperature slightly changed. However, the mean radiant temperature (MRT) of the grass area was lower than that of other areas. Especially the shaded area showed a very low MRT and thus thermal comf...

Abstract This study presents the development of personalized models of occupant satisfaction with the visual environment in private perimeter offices. A set of experiments was designed and conducted to collect comparative preference data from four human test-subjects. A probit model structure, with assumed latent satisfaction utility model in the form of multivariate Gaussian function, was adopted for developing the preference model. Four distinctive satisfaction and preference models were trained with a variational inference algorithm using experimental data. The posterior estimations of model parameters, visual preference probabilities and inferred satisfaction utility functions were investigated and compared, with results reflecting the different characteristics of the subjects. The developed visual satisfaction utility function was designed for use in personalized control, where occupants could balance their own satisfaction expectation and energy considerations.

Abstract The objective of this paper is to understand the complex interactions related to visual environment control in private offices of perimeter building zones and to develop a new method for learning occupant visual preferences. In the first step of our methodology, we conduct field observations of occupants’ perception and satisfaction with the visual environment when exposed to variable daylight and electric light conditions, and we collect data from room sensors, shading and light dimming actuators. Consequently, we formulate a Bayesian classification and inference model, using the Dirichlet Process (DP) prior and multinomial logistic regression, to develop probability distributions of occupants’ preference, such as prefer darker, prefer brighter, or satisfied with current conditions. Based on field observations, we encode within the model structure that occupants’ visual preferences are influenced by a combination of measured physical and control state variables describing the luminous environment, as well as latent human characteristics. The latter represent hidden random variables used to determine the optimal number of possible clusters of individuals with similar visual preference characteristics in the studied office building population. In the final step, we learn the visual preferences of new occupants in the dataset, by inferring their cluster values, and we derive the personalized profiles, using a mixture of the general probabilistic sub-models.

Abstract This paper presents a new data-driven method for learning personalized thermal preference profiles, by formulating a combined classification and inference problem, without developing different models for each occupant. Different from existing approaches, we developed a generalized thermal preference model in which our main hypothesis, “Different people prefer different thermal conditions”, is explicitly encoded. The approach is fully Bayesian, and it is based on the premise that the thermal preference is mainly governed by (i) an overall thermal stress, represented using physical process equations with relatively few parameters along with prior knowledge of the parameters, and (ii) the personal thermal preference characteristic, which is modeled as a hidden random variable. The concept of clustering occupants based on this hidden variable, i.e., similar thermal preference characteristic, is introduced. The results, based on a dataset collected from a typical office building population, show clear evidence of the existence of multi-clusters; in particular, the 5-cluster model performed best compared to 2, 3 and higher cluster models using the studied dataset. Subsequently, the thermal preference of a new occupant in the dataset is inferred by using a mixture of the general sub-models for each cluster. The results show that the method developed in this study provides accurate predictions for personalized thermal preference profiles and it is efficient as it only requires a relatively small dataset collected from each occupant. The approach presented in this paper is a significant step towards personalized environments in office buildings using real-time feedback from occupants.

Abstract This paper presents the development of a self-tuned HVAC controller that provides customized thermal conditions to satisfy occupant preferences (i.e., online learning) while minimizing energy consumption, and the implementation of this controller in a real occupied office space. The evolution of personalized thermal preference models and the delivery of thermal conditions with model predictive control (MPC) form a closed-loop. To integrate these two parts, we propose a new method that always provides a set of lower and upper indoor temperature bounds. Different from ad hoc rules proposed in previous research, the control bounds are based on a decision-making method that minimizes the expected cost. We implemented the self-tuned controller in an actual open-plan office space conditioned with a radiant floor cooling system with eight independently controlled loops. Localized operative temperature bounds in each radiant floor loop were determined based on occupants’ feedback and personalized thermal preference models, developed using a Bayesian clustering and online classification algorithm. The self-tuned controller can decrease occupant dissatisfaction compared to a baseline MPC controller, tuned based on general comfort bounds. To generalize the findings of this work: (i) we integrated the self-tuned controller with local MPC into a building simulation platform using synthetic occupant profiles, and (ii) demonstrated a method for automatic system adjustment based on comfort-energy trade-off tuning. In this way, decisions resulting in energy waste or occupant dissatisfaction are eliminated, i.e., the energy is deployed where it is actually needed.

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.

Abstract This paper presents a new method for developing personalized visual satisfaction profiles in private daylit offices using Bayesian inference. Unlike previous studies based on action data, a set of experiments with human subjects and changing visual conditions were conducted to collect comparative preference data. The likelihood function was defined by linking comparative visual preference data with the visual satisfaction utility function using a probit model structure. A parametrized Gaussian bell function was adopted for the latent satisfaction utility model, based on our belief that each person has a specific set of neighboring visual conditions that are most preferred. Distinct visual preference profiles were inferred with a Bayesian approach using the experimental data. The inferred visual satisfaction utility functions and the model performance results reflect the ability of the models to discover different personalized visual satisfaction profiles. The method presented in this paper will serve as a paradigm for developing personalized preference models, for potential use in personalized controls, balancing human satisfaction with indoor environmental conditions and energy use considerations.