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.

<div>Currently, people in North America spend roughly 90% of their time indoors. Therefore, it is important to create comfortable, healthy, and productive indoor environments for the occupants. Unfortunately, our resulting indoor environments are still very poor, especially in multi-occupant rooms. In addition, energy consumption in residential and commercial buildings by HVAC systems and lighting accounts for about 41% of primary energy use in the US. However, the current methods for simulating building energy consumption are often not accurate, and various types of occupant behavior may explain this inaccuracy.</div><div>This study first developed artificial neural network models for predicting thermal comfort and occupant behavior in indoor environments. The models were trained by data on indoor environmental parameters, thermal sensations, and occupant behavior collected in ten offices and ten houses/apartments. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6 °C to 25 °C in winter and from 20.6 °C to 25.6 °C in summer. We also found that the comfortable air temperature in the residences was 1.7 °C lower than that in the offices in winter, and 1.7 °C higher in summer. The reason for this difference may be that the occupants of the houses/apartments were responsible for paying their energy bills. The comfort zone obtained by the ANN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that using behaviors was wider.</div><div>Then this study used the EnergyPlus program to simulate the energy consumption of HVAC systems in office buildings. Measured energy data were used to validate the simulated results. When using the collected behavior from the offices, the difference between the simulated results and the measured data was less than 13%. When a behavioral ANN model was implemented in the energy simulation, the simulation performed similarly. However, energy simulation using constant thermostat set point without considering occupant behavior was not accurate. Further simulations demonstrated that adjusting the thermostat set point and the clothing could lead to a 25% variation in energy use in interior offices and 15% in exterior offices. Finally, energy consumption could be reduced by 30% with thermostat setback control and 70% with occupancy control.</div><div>Because of many contextual factors, most previous studies have built data-driven behavior models with limited scalability and generalization capability. This investigation built a policy-based reinforcement learning (RL) model for the behavior of adjusting the thermostat and clothing level. We used Q-learning to train the model and validated with collected data. After training, the model predicted the behavior with R2 from 0.75 to 0.80 in an office building. This study also transferred the behavior knowledge of the RL model to other office buildings with different HVAC control systems. The transfer learning model predicted with R2 from 0.73 to 0.80. Going from office buildings to residential buildings, the transfer learning model also had an R2 over 0.60. Therefore, the RL model combined with transfer learning was able to predict the building occupant behavior accurately with good scalability, and without the need for data collection.<br></div><div><div>Unsuitable thermostat settings lead to energy waste and an undesirable indoor environment, especially in multi-occupant rooms. This study aimed to develop an HVAC control strategy in multi-occupant offices using physiological parameters measured by wristbands. We used an ANN model to predict thermal sensation from air temperature, relative humidity, clothing level, wrist skin temperature, skin relative humidity and heart rate. Next, we developed a control strategy to improve the thermal comfort of all the occupants in the room. The control system was smart and could adjust the thermostat set point automatically in real time. We improved the occupants’ thermal comfort level that over half of the occupants reported feeling neutral, and fewer than 5% still felt uncomfortable. After coupling with occupancy-based control by means of lighting sensors or wristband Bluetooth, the heating and cooling loads were reduced by 90% and 30%, respectively. Therefore, the smart HVAC control system can effectively control the indoor environment for thermal comfort and energy saving.</div><div>As for proposed studies in the future, at first, we will use more advanced sensors to collect more kinds of occupant behavior-related data. We will expand the research on more occupant behavior related to indoor air quality, noise and illuminance level. We can use these data to recognize behavior instead of questionnaire survey now. We will also develop a personalized zonal control system for the multi-occupant office. We can find the number and location of inlet diffusers by using inverse design.</div></div>

<p>Current air conditioning systems generally operate with a relatively fixed moisture removal capacity, and indoor humidity conditions are usually not actively controlled in most buildings. If we focus only on sensible heat removal, an air conditioning system could operate with a fairly high evaporating temperature, and consequently a high coefficient of performance (COP). However, to provide an acceptable level of dehumidification, air conditioners typically operate with a much lower evaporating temperature (and lower COP) to ensure that the air is cooled below its dew point to achieve dehumidification. The latent (moisture related) loads in a space typically only represent around 20-30% of the total load in many environments; however, the air conditioning system operates 100% of the time at a low COP to address this small fraction of the load. To address issues associated with inadequate dehumidification and high energy consumption of conventional air conditioning systems, the use of a separate sensible and latent cooling (SSLC) system can dramatically increase system COP and provide active humidity control. Most current SSLC approaches that are reported in the literature require the installation of multiple components or systems in addition to a conventional air conditioner to separately address the sensible and latent loads. This approach increases the overall system installation and maintenance costs and complicates the controller design. </p> <p>A sequential SSLC system is proposed and described in this work takes full advantage of readily available variable speed technology and utilizes independent speed control of both the compressor and evaporator fan, so that a single direct expansion (DX) air-conditioning (A/C) system can be operated in such a way to separately address the sensible and latent loads in a highly efficient manner. In this work, a numerical model of DX A/C system is developed and validated through experiential testing to predict the performance under varied equipment speeds and then used to investigate the energy saving potential with the implementation of the proposed sequential SSLC system. To realize the sequential SSLC system approach, various corresponding control strategies are proposed and explained in this work that minimizes energy consumption while provides active control over both space temperature and relative humidity. At the end of this document, the benefits of applying the SSLC system in a prototype residential building under different typical climate characteristics are demonstrated.</p>

<p dir="ltr">Personalized thermal controls are beneficial for occupant comfort and productivity in office buildings. Recent research efforts on learning personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This Thesis presents the development and field implementation of personal preference-based thermal control in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles, a low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. The customized, preference-based HVAC control implemented in the offices indicated the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28-35% energy savings with personalized MPC (vs personalized static setpoint control).</p><p dir="ltr">Regarding the practical limitations in collecting sufficient data from occupants to train their thermal comfort model, we present a Bayesian meta-learning approach for developing reliable, data-driven personalized thermal comfort models using limited data from individuals. A high-dimensional neural network was developed, considering general thermal comfort impact factors (environmental variables, clothing level and metabolic rate) as well as personal thermal characteristics (expressed as a vector of continuous latent variables) as model inputs. The model parameters in the neural network were trained with subsets of ASHRAE RP-884 database. The trained neural network is transferrable, so that the thermal preferences of new individuals can be predicted by inferring their personal thermal characteristics using limited data. The results show that the developed Bayesian meta-learning approach to infer personal thermal comfort performs better than existing methods, especially when using limited data.</p><p dir="ltr">Moreover, this Thesis also discusses the potential of balancing thermal comfort and energy cost by setting dynamic temperature constraints in personalized MPC. A co-simulation framework of EnergyPlus and MPC is constructed using EnergyPlus Python API. Dynamic temperature constraints are selected based on personal thermal profile, weather conditions and utility rate variations. The performance of the personalized MPC with dynamic constraints demonstrates a balance between thermal comfort and energy cost in cooling season.</p>

<p>  </p> <p>Heating and cooling (HC) energy use account for about 40% of the total annual energy consumption and cost of an average household in the U.S and it is significantly affected by residents’ energy-related behavior. This is particularly important for low-income residents in the U.S. who spend a larger portion of their income (i.e., about 16%) on home energy costs compared to average-income households (i.e., 4%). To address opportunities for reducing residential HC energy usage without requiring physical building upgrades, this thesis presents a new paradigm for smart and connected energy-aware communities that leverage smart eco-feedback devices and social games to engage residents in understanding and reducing their home energy use.</p> <p><br></p> <p>First, this Thesis presents a new modeling approach for personalized eco-feedback design integrated with a collaborative social game to assist residents to enhance their thermostat use while promoting community-level energy savings. The modeling framework is integrated into a cloud-based application, MySmartE, with visual (wall-mounted tablet) and voice (Alexa) user interfaces to facilitate behavioral changes in a user-centric approach. The platform is deployed in a multi-unit residential community in Fort Wayne, IN and the experimental data are used to investigate: (i) how occupants’ thermostat behaviors changed after using the MySmartE app; (ii) how users interacted with the app during the game; and (iii) how was users’ experience with the developed platform. Despite the heterogeneous characteristics of households, the results from the field study show the positive effect of the intervention in the thermostat-adjustment behaviors, which results in an increase in the indoor temperature during the cooling season compared to the baseline period. Findings from the user interaction analysis and post-experiment interviews also reveal the significant potential to nudge households’ energy conservation behaviors with the developed platform along with the challenges that should be tackled to derive long-term behavior changes. </p> <p><br></p> <p>Second, this Thesis introduces a sociotechnical modeling approach based on utility theory to reveal causal effects in human decision-making and infer attributes affecting households’ thermostat responses during an eco-feedback intervention. This modeling approach (i) is based on a utility model that quantifies residents’ preferences over indoor temperatures given decision attributes related to their thermal environment and eco-feedback and (ii) incorporates latent parameters that are inferred to determine the unique behavioral characteristics of each household. For parameter learning, a hierarchical Bayesian model is developed with a non-centered parameterization and calibrated to the field data. Based on the calibration results, the proposed model quantifies the impact of the eco-feedback on households’ thermostat-adjustment behaviors and serves as a foundation for analyzing resident behavior in connected residential communities with eco-feedback energy-saving programs.</p> <p><br></p> <p>Finally, this Thesis presents a modeling approach for investigating the decision trends of residents in goal-oriented collaborative social games while considering their decision preferences and goal achievement capabilities. The proposed approach involves a mechanism design method that derives optimal decisions by conducting counterfactual simulations given various scenarios of goal and reward sets. This modeling approach (i) re-defines utility functions to include decision attributes that reflect user preferences on the game status; (ii) calibrates the model to learn the decision preferences of the residents; (iii) simulates the decision-making process of residents by solving the Nash Equilibrium for a given set of game scenarios. The results revealed the decision trends of the residents given the various goals and rewards along with the potential goal achievement trends and the resulting variations in the marginal community utility.</p>

<p dir="ltr">In U.S. residential buildings, space heating makes up about 43% of total energy use, with natural gas fulfilling 45% of this demand. As climate change concerns escalate, moving away from fossil fuel heating systems to more sustainable options are essential, especially in cold climates where heating needs are significant. Air-source heat pumps are a promising alternative, but their capacity and efficiency decrease as outdoor temperatures drop, impacting comfort due to lower supply temperatures (e.g., 32°C/90°F). This can lead to potential discomfort, as such temperatures feel cooler than skin temperature. Additionally, defrosting cycles pull heat from indoor spaces to clear outdoor coils. Conversely, gas furnaces provide steady heat at higher temperatures (over 49°C/120°F) without defrosting issues. Research shows that discomfort prompts occupants to raise thermostat setpoints and increase energy use.This study aims to investigate the influence of operational characteristics of various comfort delivery systems in cold weather on occupants' thermostat adjustment behaviors, identify the limitations of current heat pump systems, and develop a novel wall-embedded micro heat pump (WEMHP) radiant heating system that enhances comfort and reduces energy consumption, supporting the electrification of residential buildings. To achieve this goal, the research focuses on the following specific objectives: 1) develop a controlled laboratory testbed to emulate different thermal comfort delivery systems, including convective air and radiant systems; 2) investigate occupant setpoint preferences and thermostat adjustment behaviors under different operational modes using a residential community testbed; 3) study occupant thermostat adjustment behaviors for different types of heat pump systems through laboratory experiments; 4) develop and evaluate a novel wall-embedded micro heat pump for radiant heating in buildings; 5) design and test a prototype of the wall-embedded micro heat pump as a proof-of-concept demonstration.</p><p dir="ltr">This study first introduces the Human Building Interaction Laboratory (HBIL), a new facility with a modular design that includes reconfigurable thermally active panels for walls, floors, and ceilings. Each panel’s surface temperature can be independently controlled via a hot and cold water hydronic system, allowing the simulation of various climate zones, building conditions, and different heating/cooling systems. This setup facilitates research on localized comfort delivery, occupant comfort control, active building materials, and more.</p><p dir="ltr">Subsequently, a residential community test-bed was established within a newly built residential community in Indianapolis. A study was conducted in 30 homes to collect data on occupants' thermostat adjustments under two different operation modes: 1) a baseline mode featuring a heat pump paired with an auxiliary heater controlled by default thermostat heuristic rules, and 2) a comparison mode where the auxiliary heater was activated to provide the majority of heating. The findings showed that 8 out of 13 units preferred lower setpoints in the comparison mode, where higher supply air temperatures were utilized. Four distinct setpoint-increasing behaviors were identified, contributing to the observed setpoint differences between the two modes. Notably, two of these behaviors were closely linked to the operational characteristics of heat pumps in cold weather, specifically cases of insufficient and sufficient HP capacity.</p><p dir="ltr">To further explore the differences in setpoint preferences and the motivations behind setpoint adjustments, two scenarios were designed, and 32 experiments with human test-subjects were conducted in a controlled laboratory (Human Building Interaction Laboratory). The first case, with a single-stage heat pump and auxiliary heater, replicated the operational characteristics observed in the field study. The second case, using a variable-speed heat pump with enhanced comfort features, aimed to investigate participants' comfort preferences and provide insights for future heat pump design improvements. According to the thermal comfort survey results, 19 out of 32 participants increased their setpoints in the single-stage heat pump case, even though the heat pump had sufficient capacity to warm the indoor space. Cold air movement and indoor temperature fluctuations due to the heat pump cycling on/off were the main reasons participants reported increasing their setpoints in this case. In contrast, participants felt more comfortable with the variable-speed heat pump in the laboratory study, attributing their comfort to stable indoor temperatures and the absence of cold air movement.</p><p dir="ltr">Finally, a novel wall-embedded micro heat pump (WEMHP) was developed as a new distributed comfort delivery approach with several distinct advantages compared to alternatives: (1) A WEMHP eliminates the need for a secondary water loop and does not require separate indoor and outdoor units. Instead, a WEMHP unit operating in heating mode directly absorbs heat through an embedded heat exchanger (evaporator) at the outside wall surface and then conditions the indoor space using an embedded heat exchanger (condenser) at the indoor surface. (2) This packaged solution eliminates the need for extensive HVAC installation and on-site refrigerant charging. (3) The interior surface temperature of the exterior wall section empowered by the micro heat pump is independently controlled, allowing for distributed space conditioning and delivery of radiant heating to meet diverse occupant needs in different zones. The system performance was studied thoroughly based on energy simulation and experimental comfort study. Moreover, a prototype WEMHP was designed, assembled, and tested in a laboratory environment as a proof-of-concept demonstration. The test results demonstrated that the heating capacity under condition H1 reached around 190 W at a compressor speed of 4000 RPM with a COP of 1.67. Additionally, the system exhibited a fast thermal response, with a time constant τ₆₃ (the time it takes for the surface temperature to reach 63% of the difference between its final and initial values) of less than 0.5 hours and a τ₉₅of approximately 1.5 hours.</p>

<p>We make decisions every day, some with the potential for a huge impact on our lives. This process of decision-making is crucial not only for individuals but for industries, including construction. Unlike the manufacturing industry, where one can make certain decisions regarding an actual product by looking at it in real time, the nature of construction is different. Here, decisions are to be made on a product which will be built somewhere in the near future. The complex and interim nature of construction projects, along with factors like time essence, increasing scale of projects and multitude of stakeholders, makes it even more difficult to reach consensus. Incorporating VR can aid in getting an insight on the final product at the very beginning of the project life cycle. With a visual representation, the stakeholders involved can collaborate on a single platform to assess the project, share common knowledge and make choices that would produce better results in all major aspects like cost, quality, time and safety. This study aims at assessing decision-making in the earlier stages of construction and then evaluating the performance of immersive and non-immersive VR platforms.</p> <br> <p> </p>