Search

In this paper, we introduce a real-time modelling approach to predict the heating and cooling energy consumption of each housing unit in multi-family residential buildings. We first present measure...

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.

This paper presents a method to incorporate personalized visual preferences in real-time optimal daylighting control without using general discomfort-based assumptions. A personalized shading control framework is developed to maximize occupant satisfaction while minimizing lighting energy use in daylit offices with automated shading systems. Personalized visual satisfaction utility functions were used along with model-predicted lighting energy use to form an optimization framework using two approaches. In the multi-objective optimization scheme, the satisfaction utility and predicted lighting energy consumption are used as parallel objectives to provide a set of Pareto solutions at each time step. In the single-objective optimization scheme, the satisfaction utility is converted into a constraint when minimizing lighting energy use. A simulation study with two distinct visual satisfaction models, inferred from experimental data, was conducted to evaluate the implementation feasibility and optimization effectiveness. Daily and annual simulation results are presented to demonstrate the different patterns of optimal points depending on preference profiles, occupant sensitivity to utility function, and exterior conditions. Finally, we present a new way to apply the multi-objective optimization without assigning arbitrary weights to objectives: allowing occupants to be the final decision makers in real-time balancing between their personalized visual satisfaction and energy use considerations, within dynamic hidden optimal bounds. A slider is introduced as a dynamic user interface with mapped and sorted optimal solutions.

In this paper, we explore intelligent operation strategies, based on stochastic model predictive control (SMPC), for optimal utilization of solar energy in buildings with integrated solar systems. Our approach takes into account the uncertainty in solar irradiance forecast over a prediction horizon, using a new probabilistic time series autoregressive model, calibrated on the sky-cover forecast from a weather service provider. In the optimal control formulation, we model the effect of solar irradiance as non-Gaussian stochastic disturbance affecting the cost and constraints, and the nonconvex cost function is an expectation over the stochastic process. To solve this complex optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process regression, and achieves good solution quality. In the final step, we use an emulator that couples physical system models in TRNSYS with the SMPC controller developed using Python and MATLAB to evaluate the closed-loop operation of a building-integrated system with a solar-assisted heat pump coupled with radiant floor heating. For the system and climate under consideration, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a baseline well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.

This paper introduces a new multi-agent system approach to optimal control of high performance buildings and presents algorithms for both distributed system identification and distributed model predictive control (DMPC). For the system identification, each thermal zone is divided into sub-systems, and a parameter set for each sub-system is first estimated individually, and then integrated into an inverse model for the whole thermal zone using the dual decomposition algorithm. For the DMPC, a distributed optimization algorithm inspired by the Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) is deployed and multiple MPCs run iteratively while exchanging control input information until they converge. The developed algorithms are tested using field data from an occupied open-plan office space with a radiant floor system with distributed sensing, control, and data communication capabilities for localized comfort delivery. With this tractable approach, agents solve individual optimization problems in parallel, through information exchange and broadcasting, with a smaller scale of the input and constraints, facilitating optimal solutions with improved efficiency that are scalable to different building applications. Using a data-driven model and weather forecast, the DMPC controller is implemented to optimize the operation of an air-cooled chiller while providing different operative temperature bounds for each radiant floor loop. The radiant comfort delivery system with predictive control is capable of providing localized thermal environments while achieving significant energy savings. For the system and climate under consideration, results from the building operation during the cooling season, show 27% reduction in electricity consumption compared to baseline feedback control.

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.

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.

In this paper, our goal is to develop user-interactive thermal environment control systems that aim to increase energy efficiency and occupant satisfaction in office buildings. Towards this goal, we present a new modeling approach of occupant interactions with a temperature control and energy use interface based on utility theory that reveals causal effects in the human decision-making process. The model is a utility function that quantifies occupants’ preference over temperature setpoints incorporating their comfort and energy use considerations. We demonstrate our approach by implementing the user-interactive system in actual office spaces with an energy efficient model predictive HVAC controller. The results show that with the developed interactive system occupants achieved the same level of overall satisfaction with selected setpoints that are closer to temperatures determined by the model-predictive control (MPC) strategy to reduce energy use. Also, occupants often accept the default MPC setpoints when a significant improvement in the thermal environment conditions is not needed to satisfy their preference. Our results show that the HVAC energy consumption with MPC can be underestimated by up to 55% without considering occupants’ overrides. The prototype user-interactive system recovered 36% of this additional energy consumption while achieving the same overall occupant satisfaction level. Based on these findings, we propose that the utility model can become a generalized approach to evaluate the design of similar user-interactive systems for different office layouts and building operation scenarios.

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.

In this paper, we present a hierarchical Bayesian approach to model human interactions with motorized roller shades and dimmable electric lights. At the top level of hierarchy, Bayesian multivariate binary-choice logit models predict the probability of shade raising/lowering actions as well as the actions to increase the level of electric light. At the bottom level, Bayesian regression models with built-in physical constraints estimate the magnitude of actions, and hence the corresponding operating states of shading and electric lighting systems. The models are based on a dataset from a field study conducted in private offices designed to facilitate a large number of participants and to collect data on environmental parameters as well as individual characteristics and human attributes governing human-shading and – electric lighting interactions. In this study, models were developed only for arrival periods due to the low frequency of actions during intermediate time intervals with continuous occupation. Our modeling framework demonstrates the advantages of the Bayesian approach that captures the epistemic uncertainty in the model parameters, which is important when dealing with small-sized datasets, a ubiquitous issue in human data collection in actual buildings; it also enables the incorporation of prior beliefs about the systems; and offers a systematic way to select amongst different models using the Bayes factor and the evidence for each model. Our findings reveal that besides environmental variables, human attributes are significant predictors of human interactions, and improve the predictive performance when incorporated as features in shading action models.

The paper presents a general agent-based system identification framework as potential solution for data-driven models of building systems that can be developed and integrated with improved efficiency, flexibility and scalability, compared to centralized approaches. The proposed method introduces building sub-system agents, which are optimized independently, by solving locally a maximum likelihood estimation problem. Several models are considered for the sub-system agents and a systematic selection approach is established considering the root mean square error, the parameter sensitivity to output trajectory and the parameter correlation. The final model is integrated from selected models for each agent. Two different approaches are developed for the integration; the negotiated-shared parameter model, which is a distributed method, and the free-shared parameter model based on a decentralized method. The results from a case-study for a high performance building indicate that the model prediction accuracy of ...

The paper presents a field study on human interactions with motorized roller shades and dimmable electric lights in private offices of a high performance building. The experimental study was designed to (i) extend the current knowledge of human-building interactions to different and more advanced systems, including intermediate shading positions and light dimming levels, and (ii) reveal behavioral characteristics enabled through side-by-side comparisons of environmental controls ranging from fully automated to fully manual and interfaces with low or high level of accessibility (wall switch, remote controller and web interface). The research methodology includes monitoring of physical variables, actuation and operation states of building systems, as well as online surveys of occupant comfort and perception of environmental variables, their personal characteristics and attributes (non-physical variables). The analyzed datasets provide new insights on the dynamics of interdependent human interactions with shading and electric lighting systems. Higher daylight utilization was observed in offices with easy-to-access controls, which implies less frequent use of electric lights and less energy consumption accordingly. Analysis of occupant satisfaction, in terms of comfort with the amount of light and visual conditions, based on datasets from offices with variable accessibility to shading and lighting control, reveals a strong preference for customized indoor climate, along with a relationship between occupant perception of control and acceptability of a wider range of visual conditions.

Previous studies show differences in thermal comfort among individual occupants and suggest solutions that incorporate building occupants in sensing and control frameworks (a.k.a., human-in-the-loop) and tune heating, ventilation, and air conditioning (HVAC) systems based on their preferences to enable self-tuned thermal environments. The objective of the review presented in this paper is to discuss two key aspects of self-tuned thermal environments: (i) learning individual occupants’ thermal comfort; (ii) HVAC control based on the learned comfort profiles. The review is conducted considering practical issues associated with the implementation of such modeling and control approaches in real buildings. We found that research on learning personalized comfort profiles has rather focused on developing and testing the adopted methods assuming that it is feasible to collect a large amount of training data in real buildings. In addition, previous research has given less attention to the validity of methods for collecting occupants’ feedback responses. Hence, we focus our discussion on data collection, input variable selection, and performance evaluation considering the data efficiency. Regarding HVAC systems control, we found that arbitrary rules have been used to operate the systems with the learned occupant comfort profiles, and we discuss their validity and consistency for different occupants and buildings.

The goal of this paper is to study the potential applicability of a stochastic approximation-based policy gradient method for optimal office building HVAC (Heating, Ventilation, and Air Conditioning) control systems. A real-world building thermal dynamics with occupant interactions is the main focus of this paper. It is a complex stochastic system in the sense that its statistical properties depend on its state variables. In this case, existing approaches, for instance, stochastic model predictive control methods, cannot be applied to optimal control designs. As a remedy, we approximate the gradient of the cost function using simulations and use a gradient descent type algorithm to design a suboptimal control policy. We assess its performance through a simulation study of building HVAC systems.

The goal of this paper is to study potential applicability and performance of approximate dynamic programming (ADP) for building control problems. It is well known that occupants' stochastic behavior affects the thermal dynamics of building spaces. Incorporating occupant interactions in building control system designs is the main focus of this work. We apply ADP to stochastic optimal control designs for illustrative scenarios of occupant-building interactions and demonstrate its validity through a simulation study.