Geoffrey Kavulya

Towards Optimization of Building Energy and Occupant Comfort Using Multi-Agent Simulation

The primary consumers of building energy are heating, cooling, ventilation, and lighting systems, which maintain occupant comfort, and electronics and appliances that enable occupant functionality. The optimization of building energy is therefore a complex problem highly dependent on unique building and environmental conditions as well as on time dependent operational factors. To provide computational support for this optimization, this paper presents and implements a multi-agent comfort and energy simulation (MACES) to model alternative management and control of building systems and occupants. Human and device agents are used to explore current trends in energy consumption and management of a university test bed building. Reactive and predictive control strategies are then imposed on device agents in an attempt to reduce building energy consumption while maintaining occupant comfort. Finally, occupant agents are motivated by simulation feedback to accept more energy conscious scheduling through multi-agent negotiations. Initial results of the MACES demonstrate potential energy savings of 17% while maintaining a high level of occupant comfort. This work is intended to demonstrate a simulation tool, which is implementable in the actual test bed site and compatible with real-world input to instigate and motivate more energy conscious control and occupant behaviors.

Continuous Sensing of Occupant Perception of Indoor Ambient Factors

Ambient factors such as temperature, lighting, and air quality influence occupants’ productivity and behavior. Although these factors are regulated by industry standards and monitored by the facilities management groups, occupants’ perceptions vary from actual values due to various factors such as building schedules and occupancy, occupant activity and preferences, weather and climate, and the placement of sensors. While occupant comfort surveys are sometimes conducted, they are generally limited to one-time or periodic assessments that do not fully represent occupant experiences throughout building operations. This study proposes a new methodology for gathering real time data on a continuous basis through participatory sensing of occupant ambient comfort in indoor environments based on a smart phone application. The developed application is presented and validated by a pilot study in a university building. Occupant perceptions of temperature are compared to actual temperature records. No correlation is found between perceived and actual room temperatures demonstrating the potential of a participatory sensing tool for adaptively controlling building temperature ranges.

Human-Building Interaction for Energy Conservation in Office Buildings

Buildings are one of the major consumers of energy in the U.S. Both commercial and residential buildings account for about 42% of the national U.S. energy consumption. The majority of commercial buildings energy consumption is attributed to lighting (25%), space heating and cooling (25%), and ventilation (7%). Several research studies and industrial developments have focused on energy management based on maximum occupancy. However, fewer studies, with the objective of energy savings, have considered human preferences. This research focuses on office buildings’ occupants’ preferences and their contribution to the building energy conservation. Accordingly, occupants of selected university campus offices were asked to reduce lighting levels in their offices during work hours. Different types of information regarding their energy consumption were provided to the occupants. Email messages were used to communicate with the occupants. To monitor behavioral changes during the study, the test bed offices were equipped with wireless light sensors. The deployed light sensors were capable of detecting variations in light intensity, which was correlated with energy consumption. The impact of different types of information on occupant’s energy related behavior is presented.

Effects of Color, Distance, and Incident Angle on Quality of 3D Point Clouds

In laser scanning, the precision of the point clouds (PC) acquisition is influenced by a variety of factors such as environmental conditions, scanning tools and artifacts, dynamic scan environments, and depth discontinuity. In addition, object color, object texture, and scanning geometry are other factors that affect the quality of point clouds. These factors can affect the overall quality of point clouds, which in turn could result in a significant impact on the accuracy of as-built models. This study investigates the effect of object color and texture on the PC quality using a time of flight scanner. The effect of these factors has investigated through an experiment carried out on the Rosenblatt Stadium in Omaha, Nebraska. The outcomes of this ongoing research will be used to further highlight the parameters that must be taken into consideration in 3D laser scanning operations to avoid sources of errors that result from laser sensor, object characteristics, and scanning geometry.

'Designing in' Complex System Interaction: Multi-Agent Based Systems for Early Design Decision Making

This paper presents a novel framework for the integration and development of multi-agent based computation techniques to bridge the cyber/physical divide to enhance early stage design decision-making. Through the development of feedback loops of simulated occupancy behavior with design process, the framework presents a new paradigm for the integration of the building occupant and designer, and the building itself as agents for contributing to optimize building design including space use, visibility, adjacency, geometry and scale. The framework argues for the invention and value of linking of human agency, with computer agency and for incorporating emergent conditions and system interaction complexity in early design decision making. The paper focuses on an initial system design which focuses on transit users of multimodal facilities which brings feedback to the designer to optimize for a subset of identified design criteria. The framework shows that with the use of multi agent based simulations for incorporating more intelligently formalized occupant behavior, designers and engineers can ‘design in’ more realistic real world complexity and therefore generate design alternatives more rapidly while demonstrating more efficient spaces in terms of actual versus assumed complex natural and physical system interactions .