H. Burak Gunay

Coupling stochastic occupant models to building performance simulation using the discrete event system specification formalism

When applying occupant models to building performance simulation (BPS), it is common practice to use a discrete-time approach requiring fixed time steps. Consequently, a simulated occupants decisions do not increase in frequency in response to rapid changes in environmental conditions. Furthermore, as illustrated in this study through the analysis of a discrete-time EnergyPlus simulation, changing the time step between simulation runs may have a dramatic effect on BPS predictions. It is therefore necessary to adhere to a prescribed time step, which may complicate the synchronization of events when models of different domains are coupled. The main contribution of this study is an investigation of the viability of employing the discrete event system specification (DEVS) formalism to represent occupant behaviour without fixed and prescribed time steps. Results indicate that using an adaptive time advancement scheme, the DEVS formalism permits realistic patterns of decision-making while facilitating the coupling of stochastic occupant models with thermal and heating, ventilation and air-conditioning models.

Implementation and comparison of existing occupant behaviour models in EnergyPlus

To incorporate occupant behaviour in building performance simulation (BPS), researchers have developed a number of occupant behaviour and presence models based on long-term field observations. This paper describes the implementation of models from the literature for predicting occupancy and use of operable windows, blinds, lighting, and clothing for offices in the BPS tool EnergyPlus. In order to make the occupant models from the literature more widely available to researchers and practitioners, the EnergyPlus energy management system scripts are made publicly available. The paper then presents a comparison of the model predictions based on a typical office and demonstrates how differences in these models influence BPS results. In general, the window, blinds, and lighting use model predictions vary significantly from model to another. Despite these significant variations, the BPS models with different occupant behaviour model combinations provided consistent load reduction predictions in response to design changes.

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 occupants 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 of representing the inter-occupant diversity in occupant modelling

Significant diversity between occupants and their presence and actions results in major uncertainty with regard to predicting building performance. However, many current occupant modelling approaches – even stochastic ones – suppress occupant diversity by focusing on developing representative occupants. Accordingly, existing approaches tend to limit the ability of stochastic occupant models to provide probabilistic building performance distributions. Using occupancy data from 16 private offices, this paper evaluated three hypotheses: (1) occupant parameters have a continuous distribution rather than discrete; (2) modelling occupants from aggregated data suppresses diversity; and (3) randomly selecting occupant traits exaggerates synthetic population diversity. The paper indicates that samples sizes for the studied occupants would have more appropriately been an order of magnitude higher: hundreds. This introductory paper shows that there are many future research needs with regard to modelling occupants.

Kinetics of Passivation and Chloride-Induced Depassivation of Iron in Simulated Concrete Pore Solutions Using Electrochemical Quartz Crystal Nanobalance

Kinetics of passivity and chloride-induced depassivation of iron exposed to simulated concrete pore solutions were studied using electrochemical quartz crystal nanobalance (EQCN), electrochemical impedance spectroscopy (EIS), and open circuit potential (OCP) monitoring. Passivation followed a two-stage logarithmic film formation process: protective film mostly formed within the first 10 min to 20 min of exposure to the passivating solutions as indicated by a sharp mass increase accompanied by impedance and phase angle data showing trends toward passivation. After this initial passivation period, mass continued to increase, albeit at a significantly slower rate. Electrochemical indicators during this period remained relatively constant and stable, suggesting that the iron remained passive. The mass increase during the post-passivation period was indicative of the formation of additional oxides, while relative stability of the OCP, impedance and phase angle measurements suggested that these oxides were like...

Model-based predictive control of office window shades

In the automation of interior window shading devices, a control system that relies on a prediction of environmental conditions and a buildings thermal response can provide savings to space-conditioning loads beyond what can be achieved using a reactive approach. The development of these control strategies can be difficult because of the uniqueness of each building. A simplified model-based predictive control (MPC) method for window shades is proposed. To this end, a control-oriented model representing the heat transfer problem in a perimeter office space was developed. The parameters of the model were estimated using the ensemble Kalman filter (EnKF). The energy-savings potential of the EnKF-based MPC approach for window shades was investigated using EnergyPlus simulations. This was accomplished by implementing the control-oriented model into the energy management system application of EnergyPlus. Simulations were conducted to assess the energy saving potential of using the EnKF-based MPC for roller blinds in a south-facing perimeter office space in Ottawa, Canada. The simulation-based results indicate the potential for about 35% reduction in electricity usage for space conditioning over manually operated interior roller blinds.

Control-oriented inverse modeling of the thermal characteristics in an office

This article puts forward 12 graybox models at varying sensory model inputs and parameters. The parameters of each model were recursively estimated using the extended Kalman and the particle filtering methods. A systematic way to evaluate the predictive performance and the appropriateness of the models was introduced by using the data gathered in three perimeter offices. The simplest feasible models that can capture the timing and magnitude of local extrema were the models with five parameters and six inputs collected with low-cost sensors. They could robustly predict the indoor temperature at less than ±0.6°C mean absolute error over a 2-day horizon.

Development and implementation of a thermostat learning algorithm

In the current article, the thermostat keypress actions with concurrent occupancy, temperature, and relative humidity data in private office spaces were analyzed. The authors observed that the occupants interact with their thermostats infrequently (on average once every 56 h of occupancy); and when they did so, occupants changed the temperature set-point, on average, by 1°C. The authors observed that about one-third of the thermostat overrides were either to decrease the set-points during the heating season, or to increase the set-points during the cooling season. The temperatures leading to the thermostat overrides changed by ∼3°C seasonally. It was noted that the frequency of thermostat interactions can be approximated as a univariate logistic regression model that inputs the indoor temperature as the predictor variable. A recursive algorithm was formulated to develop univariate thermostat use models inside building controllers. It was implemented inside four commercial controllers serving seven private office spaces to choose operating set-points. One year after the implementation, the iterative learning process resulted in a 2°C–3°C reduction in the set-points during the heating season and a 2°C–3°C increase in the set-points during the cooling season with respect to the default 22°C set-point in both seasons.

Heat budget for a waste lift placed under freezing conditions at a landfill operated in a northern climate

A landfill operated in Ste. Sophie, Québec, Canada was instrumented to better understand the waste stabilization process in northern climates. Instrument bundles were placed within the waste to monitor temperature, settlement, oxygen, moisture content, total load, mounding of leachate and electrical conductivity. A finite element model was developed to simulate the heat budget for the first waste lift placed in the winter months and was calibrated using the first 10.5 months of collected temperature data. The calibrated model was then used to complete a sensitivity analysis for the various parameters that impact the heat budget. The results of the analysis indicated that the heat required for phase change to thaw the liquid fraction within frozen waste had a significant impact on the heat budget causing sections of waste to remain frozen throughout the simulation period. This was supported by the data collected to date at Ste. Sophie and by other researchers indicating that frozen waste placed during the winter months can remain frozen for periods in access of 1.5 years.

Sensing and Data Acquisition

Occupant sensing and data acquisition are essential elements for occupant behavior research. A wide range of different types of sensors has been implemented to collect rich information on occupants and their interactions with the built environment, such as presence, actions, power consumption, etc. This information establishes a foundation to study the physiological, psychological, and social aspects of occupant behavior. This chapter summarizes existing occupancy and occupant behavior sensing and data acquisition technologies in terms of field applications, and develops nine performance metrics for their evaluation. The reviewed technologies focus on both occupants’ presence and interactions with the built environment, and are grouped into six major categories: image-based, threshold and mechanical, motion sensing, radio-based, human-in-the-loop, and consumption sensing. This chapter provides an overview and discussion of different current state-of-the-art and future sensing technologies for researchers.