In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

In this thesis, research efforts are dedicated towards the development of practical adaptable techniques to be used in Smart Homes and Buildings, with the aim to improve energy management and conservation, while enhancing the learning capabilities of Programmable Communicating Thermostats (PCT) – “transforming” them into smart adaptable devices, i.e., “Smart Thermostats”. An Adaptable Hybrid Intelligent System utilizing Wireless Sensor Network (WSN) and Artificial Intelligence (AI) techniques is presented, based on which, a novel Adaptive Learning System (ALS) model utilizing WSN, a rule-based system and Adaptive Resonance Theory (ART) concepts is proposed. The main goal of the ALS is to adapt to the occupant’s pattern and/or schedule changes by providing comfort, while not ignoring the energy conservation aspect. The proposed ALS analytical model is a technique which enables PCTs to learn and adapt to user input pattern changes and/or other parameters of interest. A new algorithm for finding the global maximum in a predefined interval within a two dimensional space is proposed. The proposed algorithm is a synergy of reward/punish concepts from the reinforcement learning (RL) and agent-based technique, for use in small-scale embedded systems with limited memory and/or processing power, such as the wireless sensor/actuator nodes. An application is implemented to observe the algorithm at work and to demonstrate its main features. It was observed that the “RL and Agent-based Search”, versus the “RL only” technique, yielded better performance results with respect to the number of iterations and function evaluations needed to find the global maximum. Furthermore, a “House Simulator” is developed as a tool to simulate house heating/cooling systems and to assist in the practical implementation of the ALS model under different scenarios. The main building blocks of the simulator are the “House Simulator”, the “Smart Thermostat”, and a placeholder for the “Adaptive Learning Models”. As a result, a novel adaptive learning algorithm, “Observe, Learn and Adapt” (OLA) is proposed and demonstrated, reflecting the main features of the ALS model. Its evaluation is achieved with the aid of the “House Simulator”. OLA, with the use of sensors and the application of the ALS model learning technique, captures the essence of an actual PCT reflecting a smart and adaptable device. The experimental performance results indicate adaptability and potential energy savings of the single in comparison to the zone controlled scenarios with the OLA capabilities being enabled.

Through the Smart Metering Initiative in the Canadian province of Ontario, all residential electricity customers will be converted from a tiered rate regime to a time-of-use (TOU) rate regime by the year 2010. Although TOU rates are designed to be cost-neutral for the average consumer, research suggests that TOU rates may affect consumers differently depending on their socioeconomic characteristics. In an effort to better understand the effects of TOU rates on lower-income and senior-headed households, a case-study in Milton was conducted between June and December of 2007. The overarching thesis question is: What are the behavioural responses to, and financial impacts of, TOU electricity rates on lower-income and senior-headed households? Nine expert interviews were conducted with Ontario professionals working in government, environmental non-profit groups, citizen advocacy organizations and affordable housing associations in order to provide context for the study. Time-differentiated electricity consumption data were then collected from 199 households from two senior housing complexes and two affordable housing complexes in Milton, Ontario between June and December 2007. A questionnaire was also sent to each household to determine some socio-economic and structural characteristics of the households. The electricity consumption data collected from the four sites suggest that the households would not benefit financially from TOU rates given electricity consumption behaviour during the period prior to the implementation of TOU rates in June 2007. Thus, they would have to change their behaviour in order to benefit financially from TOU rates. During this pre-TOU period, Site A, Site B and Site C would have paid more, on average, for their electricity under TOU rates than on tiered rates ($0.34, $0.61 and $0.15 per week, respectively). While Site D, on average, would have seen no change under TOU rates. A conservation effect was detected by comparing the electricity consumption from billing periods in 2006 to corresponding billing periods in 2007 after the implementation of TOU rates. Site A saw a conservation effect during the first corresponding billing period (35%); while Site B saw a conservation effect for three corresponding billing periods (21%, 24% and 9%). Site C saw a conservation effect for the first five corresponding billing periods (ranging from 8% to 21%), while Site D saw a conservation effect for all corresponding billing periods (ranging from 10% to 34%). The presence of a conservation effect at Site D was unexpected, particularly because households at Site D are not responsible for paying their own electricity bills. Although a conservation effect was observed after the implementation of TOU rates, the extent to which it could be attributed to the implementation of TOU rates is unclear, and should be investigated further. There was no considerable shift in the proportion of electricity consumed during each of the peak periods during the summer TOU period for Site A and Site D after the introduction of TOU rates. There was, however, a slight reduction in the portion of electricity consumed during the summer TOU period for Site B and Site C (0.2% and 0.1% per week, respectively). Due to the change in the on-, mid- and off-peak schedule from the summer TOU period to the winter TOU period, the households consume more electricity during the off-peak periods in the winter than they do during the off-peak periods in the summer (even though their patterns of consumption do not change). Similar to the pre-TOU period, during the summer post-TOU period, Site A and Site B, and Site C, on average, paid more for electricity (commodity) under TOU rates than they would have paid if they had continued on tiered rates ($0.38, $0.51 and $0.16 more per week, respectively), while Site D would have seen no change in their electricity costs. In contrast, during the winter post-TOU period several sites paid less for electricity on TOU rates than they would have if they had continued on tiered rates. Site B, Site C and Site D paid, on average, $0.78, $0.16 and $1.76 less per week, respectively. Although Site A paid more under on TOU rates during the winter post-TOU (on average $0.18 more per week), the cost was less than during the summer post-TOU period. The change in costs expressed here does not reflect any reduced costs that may have resulted from conservation. For example, if the households were shown to have a conservation effect, they might have lower electricity costs. Additionally, the changes in costs do not reflect any additional fees or charges that might be attributed to the smart meter installation and the Smart Metering Initiative (e.g., additional fees from Milton Hydro). In conclusion, TOU rates appear to be ineffective at motivating these lower-income and senior-headed households in Milton, Ontario to shift electricity from on-peak periods to off-peak periods, however, a reduction in electricity usage may be attributed to TOU rates. Further research is required to confirm these effects. It is important to note that some of the lower-income and senior-headed households in this study appeared to see an increase in their electricity bill, particularly during the summer TOU period. Lower-income and senior-headed households are thought to be less able to shift electricity consumption, therefore it is important to develop mechanisms to identify households that are at risk of bill increases.

Through the Smart Metering Initiative in the Canadian province of Ontario, all residential electricity customers will be converted from a tiered rate regime to a time-of-use (TOU) rate regime by the year 2010. Although TOU rates are designed to be cost-neutral for the average consumer, research suggests that TOU rates may affect consumers differently depending on their socioeconomic characteristics. In an effort to better understand the effects of TOU rates on lower-income and senior-headed households, a case-study in Milton was conducted between June and December of 2007. The overarching thesis question is: What are the behavioural responses to, and financial impacts of, TOU electricity rates on lower-income and senior-headed households? Nine expert interviews were conducted with Ontario professionals working in government, environmental non-profit groups, citizen advocacy organizations and affordable housing associations in order to provide context for the study. Time-differentiated electricity consumption data were then collected from 199 households from two senior housing complexes and two affordable housing complexes in Milton, Ontario between June and December 2007. A questionnaire was also sent to each household to determine some socio-economic and structural characteristics of the households. The electricity consumption data collected from the four sites suggest that the households would not benefit financially from TOU rates given electricity consumption behaviour during the period prior to the implementation of TOU rates in June 2007. Thus, they would have to change their behaviour in order to benefit financially from TOU rates. During this pre-TOU period, Site A, Site B and Site C would have paid more, on average, for their electricity under TOU rates than on tiered rates ($0.34, $0.61 and $0.15 per week, respectively). While Site D, on average, would have seen no change under TOU rates. A conservation effect was detected by comparing the electricity consumption from billing periods in 2006 to corresponding billing periods in 2007 after the implementation of TOU rates. Site A saw a conservation effect during the first corresponding billing period (35%); while Site B saw a conservation effect for three corresponding billing periods (21%, 24% and 9%). Site C saw a conservation effect for the first five corresponding billing periods (ranging from 8% to 21%), while Site D saw a conservation effect for all corresponding billing periods (ranging from 10% to 34%). The presence of a conservation effect at Site D was unexpected, particularly because households at Site D are not responsible for paying their own electricity bills. Although a conservation effect was observed after the implementation of TOU rates, the extent to which it could be attributed to the implementation of TOU rates is unclear, and should be investigated further. There was no considerable shift in the proportion of electricity consumed during each of the peak periods during the summer TOU period for Site A and Site D after the introduction of TOU rates. There was, however, a slight reduction in the portion of electricity consumed during the summer TOU period for Site B and Site C (0.2% and 0.1% per week, respectively). Due to the change in the on-, mid- and off-peak schedule from the summer TOU period to the winter TOU period, the households consume more electricity during the off-peak periods in the winter than they do during the off-peak periods in the summer (even though their patterns of consumption do not change). Similar to the pre-TOU period, during the summer post-TOU period, Site A and Site B, and Site C, on average, paid more for electricity (commodity) under TOU rates than they would have paid if they had continued on tiered rates ($0.38, $0.51 and $0.16 more per week, respectively), while Site D would have seen no change in their electricity costs. In contrast, during the winter post-TOU period several sites paid less for electricity on TOU rates than they would have if they had continued on tiered rates. Site B, Site C and Site D paid, on average, $0.78, $0.16 and $1.76 less per week, respectively. Although Site A paid more under on TOU rates during the winter post-TOU (on average $0.18 more per week), the cost was less than during the summer post-TOU period. The change in costs expressed here does not reflect any reduced costs that may have resulted from conservation. For example, if the households were shown to have a conservation effect, they might have lower electricity costs. Additionally, the changes in costs do not reflect any additional fees or charges that might be attributed to the smart meter installation and the Smart Metering Initiative (e.g., additional fees from Milton Hydro). In conclusion, TOU rates appear to be ineffective at motivating these lower-income and senior-headed households in Milton, Ontario to shift electricity from on-peak periods to off-peak periods, however, a reduction in electricity usage may be attributed to TOU rates. Further research is required to confirm these effects. It is important to note that some of the lower-income and senior-headed households in this study appeared to see an increase in their electricity bill, particularly during the summer TOU period. Lower-income and senior-headed households are thought to be less able to shift electricity consumption, therefore it is important to develop mechanisms to identify households that are at risk of bill increases.

The Facility Manager of a laboratory environment continuously walks a fine line between safe and economical operation of that facility. The primary responsibility of the laboratory is to provide a safe environment for personnel while optimizing the space for experiment. Energy efficiency is not a necessary goal. Laboratories typically require HVAC systems utilizing 100% outside air to protect the occupants. Facilities demanding the basic design requirement of 100% outside air can result in annual energy costs 4 to 5 times greater than that of the typical office building requiring 20 CFM per person. With energy costs typically representing a substantial part of an organizations operating budget is it prudent for facility managers to seek opportunities to reduce these costs. The intent of this research is to show that participation of a knowledgeable Facility Manager, during the initial design phase of a laboratory facility, can result in a finished product capable of easily incorporating a variety of energy efficiency technologies. The scope of this research is limited to smaller chemical laboratories supported with less than 20,000 CFM of comfort air. When the Facility Manager actively participates in the design process for laboratory environments there is potential for increased HVAC energy efficiency. A substantial portion of this research has been conducted from the authors daily experience and responsibility for a small chemical laboratory. Additional data was collected using personal interviews among industry experts and fellow colleagues working in the Atlanta metropolitan area with significant laboratory experience. This research focused on the mechanical systems supporting laboratories as they represent the largest percentage in first costs, energy consumption, and offer the greatest opportunity for energy reduction. The results of this research are intended to provide guidance to Facility Managers to incorporate cost effective energy recovery systems in either new construction or at a future date. The results of this research project the impact of energy consumption in a small chemical laboratory from the hypothetical installation of a customized energy recovery system.

The thesis attempts to investigate the issues pertaining to design, fabrication and application of real-time adaptive systems for building envelopes, and to answer questions raised by the idea of motion in architecture. The thesis uses the Solar Decathlon Competition as a platform to base all the research and consequently to verify their applications. Photo-voltaic (PV) panels and shading devices are two different components of Georgia Institute of Technology s the Solar Decathlon House, located above the roof, that are based on the concept of Homeostasis or self-regulated optimization. For the PV panels, the objective is to optimize energy production, by controlling their movement to track the changing position of Sun, whereas, the objective for the shading devices is to reduce heating or cooling loads by controlling the position of shading devices, thus controlling direct and diffused heat gains through the roof. To achieve this adaptive feature, it required three layers of operations. First was the design of the mechanics of movement, which tried to achieve the required motion for the PV panels and shading devices by using minimum components and parameters. Second was the design of the individual parts that are consistent with the overall concept of the House. And finally, the third layer is the design of controls that automates the motion of the PV panels and Shading Devices, using a set of sensors that actuate the attached motors. As a final product, there is an attempt to integrate the precision and material efficiency of digital fabrication with the self-regulated optimization of the roof components.

Disruption Tolerant Networks (DTNs) are mobile wireless networks that are designed to work in highly-challenged environments where the density of nodes is insufficient to support direct end-to-end communication. Recent efforts in DTNs have shown that mobility provides a powerful means for delivering messages in such highly-challenging environments. Unfortunately, many mobility scenarios depend on untethered devices with limited energy supplies. Without careful management, depleted energy supplies will degrade network connectivity and counteract the robustness gained by mobility. A primary concern is the energy consumed by wireless communications because the wireless interface is one of the largest energy consumers in mobile devices whether they are actively communicating or just listening. However, mobile devices exhibit a tension between saving energy and providing connectivity through opportunistic encounters. In order to pass messages, the device must discover communication opportunities with other nodes. At the same time, energy can be conserved by ``sleeping,' i.e., turning off or disabling the wireless interfaces. However, if the wireless interface is asleep, the node cannot discover other nodes for communication. Thus, power management in DTNs must balance the discovery of other nodes while aggressively sleeping the radio during the remaining periods. In this thesis, we first develop a power management framework for a single radio architecture that allows a node to save energy while discovering communication opportunities. The framework is tailored to the available knowledge about network connectivity over time. Further, the framework supports explicit trade-offs between energy savings and connectivity, so network operators can choose, for example, to conserve energy at the cost of reduced message delivery performance. We next examine the possibility of using a hierarchical radio architecture in which nodes are equipped with two complementary radios: a long-range, high-power radio and a short-range, low-power radio. In this architecture, energy can be conserved by using the low-power radio to discover communication opportunities with other nodes and waking the high-power radio to undertake the data transmission. However, the short range of the low-power radio may result in missing communication opportunities. Thus, we develop a generalized power management framework in which both radios support the discovery. In addition, we incorporate the knowledge of traffic load and network dynamics and devise approximation algorithms to control the sleep/wake-up cycling of the radios to provide maximum energy conservation while discovering enough communication opportunities to handle the expected traffic load. Finally, we investigate the Message Ferrying (MF) routing paradigm as a means to save energy while trading off data delivery delay. In MF, special nodes called ferries move around the deployment area to deliver messages for nodes. While this routing paradigm has been developed mainly to deliver messages in partitioned networks, here we explore its use in a connected MANET. The reliance on the movement of the ferries to deliver messages increases the delivery delay if a network is not partitioned. However, delegating message delivery to the ferries provides the opportunity for nodes to save energy by aggressively putting their radios to sleep when ferries are far away. To exploit this feature, we present a power management framework, in which nodes switch their power management modes based on the knowledge of ferry location.

In this thesis, a Proton Exchange Membrane Fuel Cell is modeled with COMSOL Multiphysics software. A cross-section that is perpendicular to the flow direction is modeled in a 2-D, steady-state, one-phase and isothermal configuration. Anode, cathode and membrane are used as subdomains and serpentine flow channels define the flow field . The flow velocity is defined at the catalyst layers as boundary conditions with respect to the current density that is obtained by using an agglomerate approach at the catalyst layer with the help of fundamental electrochemical equations. Darcy&rsquo / s Law is used for modeling the porous media flow. To investigate the effects of species depletion along the flow channels, a different type of cross-section that is parallel to the flow direction is modeled by adding flow channels as a subdomain to the anode and cathode. Differently, Brinkman Equations are used to define flow in the porous electrodes and the free flow in the channels is modeled with Navier-Stokes equations. By running parallel-to-flow model, mass fractions of species at three different locations (the inlet, the center and the exit of the channel) are predicted for different cell po- tentials. These mass fractions are used as inputs to the perpendicular-to-flow model to obtain performance curves. Finally, by maintaining restricted amount of species by having a very low pressure difference along the channel to represent a single mid-cell of a fuel cell stack, a species depletion problem is detected. If the cell potential is decreased beyond a critical value, this phenomenon causes dead places at which the reaction does not take place. Therefore, at these dead places the current density goes to zero unexpectedly.

Zero energy houses and (near) zero energy buildings are among the most ambitious targets of society moving towards an energy efficient built environment. The "zero" energy consumption is most often judged on a yearly basis and should thus be interpreted as yearly net zero energy. The fully self sustainable, i.e. off-grid, home poses a major challenge due to the dynamic nature of building load profiles, ambient weather condition and occupant needs. In current practice, the off-grid status is accomplishable only by relying on backup generators or utilizing a large energy storage system. The research develops a risk based holistic system design method to guarantee a match between onsite sustainable energy generation and energy demand of systems and occupants. Energy self-sufficiency is the essential constraint that drives the design process. It starts with information collection of occupants' need in terms of life style, risk perception, and budget planning. These inputs are stated as probabilistic risk constraints that are applied during design evolution. Risk expressions are developed based on the relationships between power unavailability criteria and "damages" as perceived by occupants. A power reliability assessment algorithm is developed to aggregate the system underperformance causes and estimate all possible power availability outcomes of an off-grid house design. Based on these foundations, the design problem of an off-grid house is formulated as a stochastic programming problem with probabilistic constraints. The results show that inherent risks in weather patterns dominate the risk level of off-grid houses if current power unavailability criteria are used. It is concluded that a realistic and economic design of an off-grid house can only be achieved after an appropriate design weather file is developed for risk conscious design methods. The second stage of the research deals with the potential risk mitigation when an intelligent energy management system is installed. A stochastic model based predictive controller is implemented to manage energy allocation to sub individual functions in the off-grid house during operation. The controller determines in real time the priority of energy consuming activities and functions. The re-evaluation of the risk indices show that the proposed controller helps occupants to reduce damages related to power unavailability, and increase thermal comfort performance of the house. The research provides a risk oriented view on the energy self-sufficiency of off-grid solar houses. Uncertainty analysis is used to verify the match between onsite sustainable energy supply and demand under dynamic ambient conditions in a manner that reveals the risks induced by the fact that new technologies may not perform as well as expected. Furthermore, taking occupants' needs based on their risk perception as constraints in design evolution provides better guarantees for right sized system design.

A Post-Occupancy Energy Analysis of the Gwinnett Environmental and Heritage Center conducted with the view of recommending optimizations that result in energy savings.