The concern of increasing energy consumption with depleting energy resources is ever growing. Though the solution to this problem lies in part in renewable energies, it is becoming increasingly clear that sustainable building design also plays a critical role. Controlling solar gain, for example, can greatly reduce the cooling energy consumption and lowering the peak cooling load. Having the ability to model these effects can have a substantial impact on the sizing of equipment and further reduce operational costs of a building. As a result, renewed interest has been invested by researchers and industry to promote the development and use of building simulation tools to aid in the design process. Efforts at the University of Waterloo’s Advanced Glazing Systems Laboratory have resulted in a set of shading device models, with emphasis on generality and computational efficiency, tailored for use in building simulation. These models have been validated with measurements at the component level and with measurements performed at the National Solar Test Facility (NSTF) on a full scale window system, giving confidence to model validity. Continued research has resulted in the integration of these shading device models into ESP-r via the Complex Fenestration Construction (CFC) module, capable of modelling multi-layer glazing and shading layer systems and greatly improving the value of ESP-r as a design tool. The objective of the current research was to implement shading device models for roller blinds, pleated drapes and insect screens to the CFC module. These would be in addition to the venetian blind model which had previously been established. A Monte-Carlo ray tracing analysis of pleated drape geometry and incident angle dependent fabric characteristics gave further confidence to the view factor or net reduction method used by the implemented models. On model implementation, a preliminary comparison was performed between a high-slat angle venetian blind, a roller drape and drapery fabric, all given the same material properties, with similar results. Further comparison was then performed using EnergyPlus shading device models to establish further confidence in the functionality of the models. Though there was some discrepancy between the results, primarily due to convective models, good agreement was found, and the effect of the shading device models on building performance was demonstrated. The successful implementation of roller blind, pleated drape and insect screen shading models to the CFC module in ESP-r has been demonstrated in the current research. It should also be noted that the convective models for indoor shading attachments is a worthwhile topic for further research, at which point it would then be beneficial to conduct further empirical validation on the ESP-r simulation.

Wood is a highly versatile renewable material (with carbon sequestering properties), that is light in weight, has good strength properties in both tension and compression while providing good rigidity and toughness, and good insulating properties (relative to typical structural materials). Engineered wood products combine the benefits of wood with engineering knowledge to create optimized structural elements. Cross-laminated timber (CLT), as one such engineered wood product, is an emerging engineering material which provides great opportunities for the building industry. While building with wood has many benefits, there are also some concerns, particularly decay. Should wood be exposed to elevated amounts of moisture, rots and moulds may damage the product or even risk the health of the occupants. As CLT panels are a relatively new engineered wood product, the moisture characteristics have yet to be properly assessed. Consequently, the amount of decay risk for CLT in building applications is unknown, and recommended protective actions during design construction and operation have yet to be determined. The goal of this research was to determine the moisture durability of CLT panels in wall assemblies and address concerns related to built-in construction moisture. The approach used to address the problem was to first determine select moisture properties of CLT panels through experimental approaches, and then use the results to calibrate a hygrothermal model to quantify the risks of wall assemblies. The wall assemblies were simulated in six different cities across Canada, representing a range of climates: Vancouver, B.C., Edmonton, A.B., Winnipeg, M.B., Ottawa, O.N., Québec City, Q.C., and St. John, New-Brunswick. The risks associated with moisture exposure during construction are also considered in the simulations. The experimental phase of the research was limited to moisture uptake tests. These tests were utilized to determine the liquid water absorption coefficient for four different types of full scale panels (2’x2’) and 12 clear wood samples. The panels were either made of 5-ply of Western-SPF, Eastern-SPF, Hemlock-Fir, or 3-ply of a generic softwood provided by a European CLT manufacturer; the clear samples were all cut from the same nominal 2x6 SPF-grade lumber. The panels were installed in a drying rack and gravimetrically tracked to assess the drying rates of the panels. Finite resources precluded more thorough material testing, but a parametric study was conducted to determine the relative impact of the missing material data on the final simulation results. In the hygrothermal simulations, four main wall assembly types were considered- those with either exterior or interior insulation, and those using either vapour permeable or impermeable air-water barriers. Various types of insulation and vapour control were also modelled. The simulations were run for a variety of interior relative humidities. The metric for comparison between the simulations was the water content of a 4mm thin layer on the extreme lamina of a CLT panel system. The results of the simulation suggest that vapour impermeable membranes, when install on dry CLT panels (less than 14% M.C.) do not pose moisture risks in any of the climates considered. However, when high levels of construction moisture is considered, only vapour permeable membranes controlled moisture risks by allowing the CLT panel to dry both to the interior and to the exterior.

<p>Commercial buildings not only have significant impacts on occupants’ well-being, but also contribute to more than 19% of the total energy consumption in the United States. Along with improvements in building equipment efficiency and utilization of renewable energy, there has been significant focus on the development of advanced heating, ventilation, and air conditioning (HVAC) system controllers that incorporate predictions (e.g., occupancy patterns, weather forecasts) and current state information to execute optimization-based strategies. For example, model predictive control (MPC) provides a systematic implementation option using a system model and an optimization algorithm to adjust the control setpoints dynamically. This approach automatically satisfies component and operation constraints related to building dynamics, HVAC equipment, etc. However, the wide adaptation of advanced controls still faces several practical challenges: such approaches involve significant engineering effort and require site-specific solutions for complex problems that need to consider uncertain weather forecast and engaging the building occupants. This thesis explores smart building operation strategies to resolve such issues from the following three aspects. </p> <p>First, the thesis explores a stochastic model predictive control (SMPC) method for the optimal utilization of solar energy in buildings with integrated solar systems. This approach considers 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 optimization problem, we introduce a new approximate dynamic programming methodology that represents the optimal cost-to-go functions using Gaussian process, and achieves good solution quality. We use an emulator 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 considered, the SMPC saves up to 44% of the electricity consumption for heating in a winter month, compared to a well-tuned rule-based controller, and it is robust, imposing less uncertainty on thermal comfort violation.</p> <p>Second, this thesis explores 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 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 occupants’ overrides can contribute up to 55% of the HVAC energy consumption on average with MPC. 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. </p> <p>Finally, this thesis presents an approach based on meta-reinforcement learning (Meta-RL) that enables autonomous optimal building controls with minimum engineering effort. In reinforcement learning (RL), the controller acts as an agent that executes control actions in response to the real-time building system status and exogenous disturbances according to a policy. The agent has the ability to update the policy towards improving the energy efficiency and occupant satisfaction based on the previously achieved control performance. In order to ensure satisfactory performance upon deployment to a target building, the agent is trained using the Meta-RL algorithm beforehand with a model universe obtained from available building information, which is a probability measure over the possible building dynamical models. Starting from what is learned in the training process, the agent then fine-tunes the policy to adapt to the target building based on-site observations. The control performance and adaptability of the Meta-RL agent is evaluated using an emulator of a private office space over 3 summer months. For the system and climate under consideration, the Meta-RL agent can successfully maintain the indoor air temperature within the first week, and result in only 16% higher energy consumption in the 3^rd month than MPC, which serves as the theoretical upper performance bound. It also significantly outperforms the agents trained with conventional RL approach. </p>

<p>The residential sector is responsible for approximately 21% of the total energy use in the U.S. As a result, there have been various programs and studies aiming to reduce energy consumption and utility burden on individual households. Among various energy efficiency strategies, behavior-based approaches have received considerable attention because they significantly affect operational energy consumption without requiring building upgrades. For example, up to 30% of heating and cooling energy savings can be achieved by having an efficient temperature setpoint schedule. Such approaches can be particularly beneficial for multi-family residential buildings because 88% of their residents are renters paying their own utility bills without being allowed to upgrade their housing unit.</p> <p>In this context, eco-feedback has emerged as an approach to motivate residents to reduce energy use by providing information (feedback) on human behavior and environmental impact. This research has gained significant attention with the development of new smart home technology such as smart thermostats and home energy management systems. Research on the design of effective eco-feedback focuses on how to motivate residents to change their behavior by identifying and notifying implementable actions in a timely manner via energy analytics such as energy prediction models, energy disaggregation, etc.</p> <p>However, unit-level energy analytics pose significant challenges in multi-family residential buildings tasks due to the inter-unit heat transfer, unobserved variables (e.g., infiltration, human body heat gain, etc.), and limited data availability from the existing infrastructure (i.e., smart thermostats and smart meters). Furthermore, real-time model inference can facilitate up-to-date eco-feedback without a whole year of data to train models. To tackle the aforementioned challenges, three new modeling approaches for energy analytics have been proposed in this Thesis is developed based on the data collected from WiFi-enabled smart thermostats and power meters in a multi-family residential building in IN, U.S.</p> <p>First, this Thesis presents a unit-level data-driven modeling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings. The proposed modeling approach provides normalized groups of units that have similar building characteristics to provide the relative evaluation of energy-related behaviors. The physics-informed approach begins from a heat balance equation to derive a linear regression model, and a Bayesian mixture model is used to identify normalized groups in consideration of the inter-unit heat transfer and unobserved variables. The probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data is available. The model finds distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.</p> <p>Second, this Thesis presents a real-time modeling approach to predict the HC energy consumption of individual units in a multi-family residential building. The model has a state-space structure to capture the building thermal dynamics, includes the setpoint schedule as an input, and incorporates real-time state filtering and parameter learning to consider uncertainties from unobserved boundary conditions (e.g., temperatures of adjacent spaces) and unobserved disturbances (i.e., window opening, infiltration, etc.). Through this real-time form, the model does not need to be re-trained for different seasons. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.</p> Finally, this Thesis presents a scalable and practical HC energy disaggregation model that is designed to be developed using data from smart meters and smart thermostats available in current advanced metering infrastructure (AMI) in typical residential houses without additional sensors. The model incorporates sequential Bayesian update whenever a new operation type is observed to learn seasonal parameters without long-term data for training. Also, it allows modeling the skewed characteristics of HC and non-HC power data. The results show that the model successfully predicts disaggregated HC power from 15-min interval data, and it shows less than 12% of error in weekly HC energy consumption. Finally, the model is able to learn seasonal parameters via sequential Bayesian update and gives good prediction results in different seasons.

<p>    </p> <p>Technological advances and gaming have assisted users in becoming energy-efficient or raising awareness about energy efficiency. However, these games typically take place in schools and workplaces. Low-income households, which spend a larger percentage of income on utilities compared to average income households, exhibit greater sensitivity to energy disturbances. Despite this, there has been limited research on applying these technologies in low-income households. </p> <p><br></p> <p>The dissertation addresses the research gap concerning motivating low-income households to adopt new technologies focused on implementing energy-efficient HVAC behaviors. To achieve this objective, a gamification approach is employed, integrating a competitive social game into a cloud-based application named MySmartE. This application offers personalized eco-feedback and enables voice commands using Amazon Alexa. The game is deployed in two multi-residential low-income household communities located in Indiana. The collected data from field studies is analyzed to explore various aspects, including community interactions during the gaming seasons, technology adoption, and factors influencing participation in the social game. The findings reveal a positive correlation between increased gaming interac- tions and the adoption of MySmartE technology within these communities, underscoring the potential of gamification and technology to effectively engage low-income households in adopting energy-efficient practices. </p>

The purpose of this research is to determine the average energy intensity of multi-unit residential buildings (MURBs) in Toronto, and evaluate whether certain building characteristics influence energy intensity. This information is particularly important in the Toronto market. Relative to the city’s population, Toronto has an unusually high proportion of MURBs with more than half of residential dwellings in apartment buildings. Additionally, Toronto MURBs are significant consumers of energy and produce an estimated 1.3M tonnes of CO2e each year. The ultimate goal is to assess the most efficient building retrofit measures. Energy consumption data for Toronto MURBs were collected and weather normalized. Correlations between the energy data and the building characteristics were examined. Window characteristics and heating system type were found to have the most significant influence on energy intensity. Establishing energy consumption characteristics of MURBs is the first step towards improving the energy efficiency of Toronto’s MURBs stock.

The purpose of this research is to determine the average energy intensity of multi-unit residential buildings (MURBs) in Toronto, and evaluate whether certain building characteristics influence energy intensity. This information is particularly important in the Toronto market. Relative to the city’s population, Toronto has an unusually high proportion of MURBs with more than half of residential dwellings in apartment buildings. Additionally, Toronto MURBs are significant consumers of energy and produce an estimated 1.3M tonnes of CO2e each year. The ultimate goal is to assess the most efficient building retrofit measures. Energy consumption data for Toronto MURBs were collected and weather normalized. Correlations between the energy data and the building characteristics were examined. Window characteristics and heating system type were found to have the most significant influence on energy intensity. Establishing energy consumption characteristics of MURBs is the first step towards improving the energy efficiency of Toronto’s MURBs stock.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis argues that an architectural surveying methodology via newspaper sampling offers new insight historic preservationists can use to more efficiently predict demolitions. Using data collected from the “Home Builder’s Department” section of the Indianapolis Star, this study compiles architectural information on 425 structures, mostly single-family and duplex residences, built between 1909 and 1926. Engaging with the historiographical themes of public history and architectural history as well as methodological components of historic preservation and digital humanities, the data-centric model relies on a collection of sampled newspaper articles, which were analyzed for specific information, compiled into a data repository with supplemental research, and then incorporated into the ArcGIS program for interpretation. The project provides a synopsis on early twentieth century building trends in Indianapolis and offers implications regarding the role that factors such as building type, geographic location, federal and municipal historic district protections, architectural style, and exterior building material or cladding play in predicting demolitions. Beyond these predictive results, this study also suggests a city-wide surveying methodology for organizing and analyzing large quantities of historic architecture for preservation planning initiatives.

Currently 10% of all single family homes produced in the U.S. are manufactured homes with 75% of these households making less than $50,000 in annual income (Manufactured Housing Survey). Manufactured homes typically use twice as much primary energy per square foot than site built homes yet there is no agenda within the industry or its governing bodies to address this excess energy consumption. The research presented in this thesis compares the thermal envelope performance of the typical wood stud framing used in the manufactured home industry to the thermal envelope of structural insulated panels (SIPs). This comparison examines the energy savings a SIP manufactured home could create for a home owner while speculating on the financial and technical feasibility of using SIPs in the manufactured housing industry. Ultimately, the comparison reveals the short comings of the Manufactured Homes Construction and Safety Standards (HUD Code) regarding thermal envelope requirements and energy use intensity. These short comings are revealed when the energy use of HUD compliant manufactured homes is scrutinized and compared to the energy use of a similar home built with SIPs for the thermal envelope. The continuous insulation and airtight qualities of the SIP home allow it to use 32%-46% less energy than the HUD compliant homes in the same locations. Manufactured homes require much more energy to heat and cool because the HUD code does not require a certain performance criteria be met for the airtightness of manufactured homes and the overall U-values it requires for the thermal envelopes of such homes is too high for the varying climate zones found in the U.S. If SIP panels were to be used for the thermal envelope of the manufactured housing industry, low income manufactured home owners could be saving $300-$700 annually in energy costs. These savings are not insignificant to low income households and could create a 5-8 year payback period of additional ownership costs under $2500. Unfortunately, the SIP industry cannot offer its product at a low enough price to compete with the economies of scale achieved by the manufactured housing industry when buying raw construction materials. The value of this research then, is the exposure of the manufactured home’s inferior envelope performance when compared to more modern construction technologies and the speculation of how the manufactured housing industry could begin to incorporate a more robust building envelope without putting its customers at a monetary disadvantage.

Satellite District Heating and Cooling (DHC) systems offer an alternative structure to conventional, centralized DHC networks. Both use a piping network carrying steam or water to connect disparate building heating and cooling loads together, providing a platform for improving energy efficiency, reducing emissions, and incorporating alternative means of energy generation. However, satellite DHC networks incorporate thermal production units that are distributed amongst the buildings nodes, which offers greater operational flexibility and reduced capital cost savings for applications using existing building stock. This study was focused on the development of the methodology behind a comprehensive energy model that can assess the practical and financial viability of satellite DHC network scenarios. A detailed scenario application of the model demonstrated significant energy savings and investment potential. Additionally, environmental assessment methods and alternative generation technology were explored in supplementary studies of Deep Lake Water Cooling (DLWC) and building-scale Combined Heat and Power (CHP).