Patterns of thermal preference and Visual Thermal Landscaping model in the workplace

The main purpose of research on occupant behaviour is to enhance building energy performance. However, it is difficult to reduce the energy use without understanding the occupant, their needs and preferences. Individual differences and preferences for the thermal environment in relation to the spatial context are overlooked in the main stream of research. This study investigates the patterns of occupant thermal preference based on individual differences in perceiving the thermal environment to enhance user comfort and energy performance. A novel method of Visual Thermal Landscaping is used, which is a qualitative method to analyse occupant comfort and user behaviour according to the spatial context. This method drives away from the notion of ‘thermal neutrality’ and generic results, rather it opens to details and meaning through a qualitative analysis of personal-comfort, based on individual differences and spatial context information. Field test studies of thermal comfort were applied in five office buildings in the UK, Sweden and Japan with overall 2,313 data sets. The primary contribution of the study was the recognition of four patterns of thermal preference, including consistent directional preference; fluctuating preference; high tolerance and sensitive to thermal changes; and high tolerance and not-sensitive to thermal changes. The results were further examined in a longitudinal field test study of thermal comfort. In several cases, occupant thermal comfort and preferences were observed to be influenced by the impact of outdoor conditions, when the windows were fixed. Practical solutions for research, practice and building design were recommended with direct implications on occupant comfort and energy use

A Data-driven Study of Connected Residential Thermostats to Investigate user Behavior, Thermal Modelling, and Optimal Control of HVAC Systems

Approximately a tenth of North America’s total energy usage is for space conditioning of residential buildings -- the majority of which is controlled by a thermostat. Despite these energy implications, thermostats have been difficult to study and their controls have remained reactive and heuristic-driven. Fortunately, with the latest generation of devices, connected thermostats, it is now possible to address these limitations. This thesis is among the first major bodies of research to exploit the emerging data from connected thermostats. Specifically, we seek to accomplish three main objectives: (1) extend the understanding of how users utilize these devices to manage their preferences, (2) develop predictive models of occupant behavior and thermal response of houses, and (3) optimize the control of residential HVAC systems using only existing available data.For objective (1), we determined that factors such as location and seasonality affected setpoint preferences, but that it was unclear if truly distinct user types emerged -- instead users appeared more on a spectrum of preferences. With schedule overrides, the behavior of users was found to be more complex than previously understood, with ultimately only a small group of users remaining in permanent and energy-intensive overrides. For objective (2), we found standard, well-tuned machine learning models (namely, random forest and ridge regression) were the most robust performers beating simple baseline and deep learning methods for predicting occupancy and thermal responses of the houses. Finally, for objective (3), we found that a data-driven model predictive controller outperforms a model-free reinforcement learning method and a standard deadband controller. In summary, this thesis makes multiple novel and significant contributions which improve the understanding of connected thermostat users, how to develop customized data-driven models, and how to improve the comfort and energy use associated with connected thermostats controlling the HVAC in our homes.Ph.D

A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data

While previous studies have attempted to understand and predict users' behaviors and preferences for residential thermostats, they have been restricted by a lack of available data. Because of practical constraints, researchers previously relied on short observation periods, small sample groups, and/or participants close in physical location. The advent of the connected thermostat and its inherent centralized data collection now allows for such studies to be performed without the onus of data collection. Specifically, in this article we focus on the ‘Donate Your Data’ dataset made available by the thermostat manufacturer ecobee Inc. The dataset, consisting of more than 10,000 connected thermostats installed across North America and spanning multiple years, was used to investigate how users' comfort decisions are affected by exterior stimuli such as climate regions, seasonal patterns, and utility rates. Our analysis indicates that seasonality and climate region affected user preferences while utility rates did not contribute to meaningful variation in behavior. Further investigation explored if behavioral user types could be identified based on variation in occupied and unoccupied setpoints, thermostat overrides with holds, or heating and cooling setpoint selection. We did not find distinct user clusters to be identifiable based on any of the metrics; rather, occupant behavior in the population appeared to span more of a continuum across each metric

Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data

Occupancy detection capabilities provided by modern connected thermostats enable adaptive thermal control of residential buildings. While this adaptation might simply consider the current occupancy state, a more proactive optimized system could also consider the probability of future occupancy in order to balance comfort and energy savings. Because such proactive control relies on accurate occupancy prediction, we comparatively evaluate a number of machine learning models for predicting measurements of the future occupancy state of homes that is critically enabled by thermostat data from real households in ecobee's Donate Your Data program. We consider a variety of models including simple heuristic and historical average baselines, traditional machine learning classifiers, and sequential models commonly used for time series prediction. We evaluate the performance of each model according to temporal, behavioural, and computational efficiency characteristics. Our key overall finding is that the random forest algorithm matched or outperformed the other candidate models, had consistently high accuracy predicting over a range of time horizons, and is relatively efficient to train for individual “edge” devices

Preliminary results of model predictive control of shading systems

Shades in buildings are widely installed and are an effective technique for managing solar gains and occupant comfort. A model of a typical office space located in Ottawa, Ontario has been created and the model was developed for analysis under variable conditions. Analysis has resulted in the generation of an advanced reactive system facilitated by the use of the energy management system (EMS) built within EnergyPlus along with a predictive control system optimized for the minimizing of the energy demand by the office space. The approach to optimization is done through the use of a basic model predictive control facilitated by the use of MATLAB (Mathworks 2011) and EnergyPlus (DOE 2012). The predictive system at this stage is delivering reductions of 5% during shoulder season over its reactive counterpart but this work is still on-going

On adaptive occupant-learning window blind and lighting controls

Occupants have a significant impact upon building energy use, e.g. through the actuation of window blinds and switching off lights. Automation systems with fixed set points for controlling blinds and lights have been used in some applications as an attempt to mitigate the impact of occupant behaviour upon energy consumption. A conceptual framework of an alternative control method is presented, one in which the control system adapts control set points in real time to each occupant's preferences. The potential of this hypothesis is demonstrated through a simulation-based study focused on a hypothetical south-facing office with existing empirical models that predict occupant behaviour regarding the control of window blinds and lights. The performance of a proposed adaptive automation system is simulated, one in which window-blind and lighting control set points are adapted in real time to learn the modelled occupant preferences using a Kalman filter. The performance of this alternative occupant-learning method of control is contrasted to that of two conventional control methods, one in which occupants have manual control over window blinds and lights, and the other that employs an automation system with fixed set points. The simulation results indicate that such an adaptive occupant-learning control method could lead to substantial energy savings

A preliminary study on text mining operator logbooks to develop a fault-frequency model

Textual data in operator logbooks represent an untapped opportunity to retrieve information about the maintenance routines of HVAC equipment and control infrastructure. This paper presents a case study in which seven years' worth of work order logs from 44 buildings on a university campus were analyzed. After extracting HVAC-related terms such as fan, AHU, VAV, stuck, and leak from custom operator descriptions, the apriori algorithm was used to derive association rules that define the coexistence tendencies of the terms in a work order (e.g., coexistence of the terms radiator and leak). Based on this analysis, a preliminary HVAC work order frequency model was put forward. The results indicate that the annual work order intensity per 1000 m2 (10,764 ft2) was about 4. More than 70% of the HVAC-related work orders were issued to address zone-level problems. Among the AHU-level work orders issued for a physical subcompo-nent, more than 90% were related to fans. Future work is planned to analyze the HVAC-related work order patterns with numeric data from automation and controls networks

Shortest-prediction-horizon model-based predictive control for individual offices

When employing model-based predictive control (MPC) for zone level heating and cooling systems, in many cases weather forecasts were imported to predict a thermal zone's temperature response over a time horizon. However, an office's thermal response is strongly influenced by an occupant's presence and behaviours. As illustrated through the analysis of an EnergyPlus simulation, propagation of the uncertainty introduced by an occupant's presence and behaviours into the temperature response of a thermal zone can result in suboptimal control decisions when the prediction time horizon extends beyond one hour. Results indicate that modest, yet robust to occupant behaviour, energy savings can be achieved by limiting the prediction time horizon to one hour in zone level MPC implementations. Choice of this prediction time horizon also eliminated the need for importing weather forecasts. In an effort to discuss the implementation challenges, this MPC algorithm has been implemented in a commercial controller to automate a c

Energy and comfort performance benefits of early detection of building sensor and actuator faults

This paper presents a building performance simulation-based investigation to better understand the energy and comfort performance benefits of early detection of common sensor and actuator faults. Five types of air-handling unit faults and four types of zone-level faults were implemented to the energy management system application of the building performance simulation tool EnergyPlus. During 50-year simulation periods, the faults were randomly permitted to affect 75 different components of an archetype medium-sized office building model. The sensitivity of the simulation results with respect to three variables was studied: fault recurrence period, fault repair period, and discomfort threshold for simulated complaints. The results indicate that the energy use intensity and the predicted percentage of dissatisfied exhibit a power–law relationship with time, in which most of the performance reductions occur in the first 10 years. If the work-orders are issued only upon occupant complaints, the faults were estimated to cause a 16–62% increase in the energy use intensity for heating, ventilation, and air-conditioning and a 11–38% increase in the predicted percentage of dissatisfied at the end of the 50-year simulation periods. The results indicate that if the faults can be detected within a month after their first appearance, almost all their detrimental effects on a building’s energy and comfort performance can be mitigated. Practical application: The methodology and results presented in this article are of practical use for those who study on-going commissioning, fault detection and diagnostics, and energy management systems in buildings. The simulation-based parametric analysis approach can be used to estimate the range of energy and comfort savings expected through early detection of common sensor and actuator faults in commercial buildings. Insights gathered from such an analysis can be used in planning the frequency of retro-commissioning and investments for automated fault detection and diagnostics systems