Phase Change Materials have been the subject of increased research in modern times. Phase Change Materials, abbreviated as PCMs, are being used in a variety of applications in the energy conservation world. In this study, the effect of PCMs on a residential building’s energy consumption was evaluated at different locations across the United States and compared to the standard building at the same locations. An average American residential building was designed and modeled in SketchUp software. The building was evaluated for energy consumption at different locations across the United States using weather data for each chosen location. After the baseline results were collected, the building was re-evaluated, under the same conditions, with a Heptadecane embedded in the exterior walls as the chosen PCM for this study. The results of this study show that Phase Change Materials have a wide-ranging effect on the energy consumption of the designed building. Addition of the PCM to the building walls decreased total energy usage, over the course of a year, by 3.02 – 6.72%, depending on the location.

<p>Current global efforts in building information research include the development of low-cost, high reliability sensing systems capable of quantifying metrics such as human occupancy, indoor environmental quality, and building system dynamics. Such information is of high value for model development, building energy management, and improving occupant comfort. Further, indoor air quality (IAQ) has been a growing concern in recent years, only to be exacerbated by the COVID-19 pandemic. A common provisional measure for IAQ is carbon dioxide (CO2), which is regularly used to inform the ventilation control of buildings. However, few commercially available sensors exist that can reliably measure CO2 while being low cost, exhibiting low power consumption, and being easily deployable for use in applications such as occupancy monitoring. </p> <p><br></p> <p>This work presents research related to the initial development and evaluation of low-cost, stable, and easily deployable sensors for monitoring indoor CO2 levels in buildings. Two different types of sensors are presented that have the potential to perform as well as current commercially available CO2 sensing technologies, at significantly lower costs. The first is a chemiresistive sensor that is fabricated using a carbon nanotube thin film in conjunction with a blend of branched poly(ethylenimine) (PEI) and poly(ethylene glycol) (PEG), which serve as a CO2 absorbing layer. The second is a resonant mass sensor, functionalized with similar polymer-based materials including a blend of PEI and poly(ethylene oxide) (PEO). Prototype sensors were assessed in a bench-top environmental test chamber which varied temperatures, relative humidity levels, CO2 concentrations, as well as other gas constituents to simulate typical and extreme indoor conditions. The results indicate that the proposed system could ultimately serve as an attractive alternative to commercial CO2 sensors that are currently available.</p>

Indiana University-Purdue University Indianapolis (IUPUI) / Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

By partly replacing the use of primary energy sources with waste heat recovery, climate and environmental goals for the future will be closer at hand. This thesis investigates the waste heat potential of Alfa Laval’s vacuum brazing furnaces in Ronneby and alternative ways of integrating the furnace’s waste heat into the building’s HVAC system. The main challenge was the low-temperature qualities associated with the cooling water, which constituted an obstacle to recovering waste heat without any additional equipment, such as a heat pump. Tests and analyses performed in this thesis are, therefore, mainly aimed at raising the temperature quality of the cooling water. A test was conducted on the cooling system to calculate the energy losses with regards to the cooling water. In one 11-hour cycle, 1546 kWh of electricity was used to heat the furnace. Out of that, 1360 kWh was cooled off to the atmosphere. Additionally, a test on the furnace’s clean-up cycle was performed. The maximum cooling water temperature reached during this test was 44 C. This shows excellent potential in the possibility of recovering the waste heat without any additional equipment. Further, this thesis aims to broaden the knowledge around areas concerning increased cooling water temperatures, which, during the writing, seemed to have a gap in documented sources. The results of this thesis indicate that a temperature quality increase of the furnaces’ cooling water is possible. Cooling system changes have also been suggested, which is necessary for an efficient and safe heat recovery.

In recent years, many strides have been achieved in the area of Wireless Sensor Networks (WSNs), which is leading to constant innovations in the types of applications that WSNs can support. Much advancement has also been achieved in the area of smart homes, enabling its occupants to manually and easily control their utility expenses. In this thesis, both areas of research will be colluded for a simple, yet critical application: efficient and economical comfort control in smart residential spaces. The goal is to design a central, modular energy consumption control system for residential spaces, which manages energy consumption in all aspects of a typical residence. This thesis is concerned with two facets of energy consumption in residences. The first facet is concerned with controlling when the heating, ventilating, and air conditioning unit (HVAC) operates for each room separately. This is in contrast to a typical HVAC system where comfort is provided across the floor as a whole. The second facet is concerned with controlling the lighting in each room so as to not exceed a certain input value. The communication network that supports the realization of these coveted goals is based on Zigbee interconnected sensor nodes which pour data unto a smart thermostat which does all the required calculations and activates the modules required for comfort control and energy management, if needed. A Java-based discrete event simulator is then written up to simulate a floor of a typical Canadian single-family dwelling. The simulation assumes error-less communication and proceeds to record certain room variables and the ongoing cost of operation periodically. These results from the simulator are compared to the results of the well known simulator, created by DesignBuilder, which describes typical home conditions. The conclusion from this analysis is that the Comfort Control and Energy Management Algorithms (CEMA) are feasible, and that their implementation incurs significant monetary savings.

This thesis is designed to introduce beginning and experienced heating, ventilation and air conditioning (HVAC) engineers to common indoor air quality (IAQ) problems and solutions. The bulk of the work is a literature review of common pollutants, pollutant sources, HVAC equipment and systems, and remediation techniques. Pollutants covered include fungi, bacteria, dust mites, viruses, biofilms, microbiological volatile organic compounds (MVOC?s), volatile organic compounds (VOC?s), carbon dioxide, ozone, and radon. The HVAC systems covered are ventilation, direct expansion (DX), desiccant dehumidification, and system filters. The remediation techniques discussed are proper hygiene and maintenance, increased ventilation, humidity control, and proper selection of building materials.

Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation. / Doctor of Philosophy / With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.

Smart building's benefits range from improving comfort of occupant, increased productivity, reduction in energy consumption and operating costs, lower CO2 emission, to improved life cycle of utilities, efficient operation of building systems, etc. [65]. Hence, modern building owners are turning towards smart buildings. However, the current smart buildings mostly are not capable of achieving the objectives they are designed for and they can improve a lot better [22]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Using this technology, e.g. a door can be a thing and can sense how many people have passed it's sensor to enter a space and let the lighting system know to prepare appropriate amount of light, or the HVAC (Heating Ventilation Air Conditioning) system to provide desirable temperature. IoT will provide a lot of useful information that before that accessibility to it was impossible, e.g., condition of water pipes in winter, which helps avoiding damages like frozen or broken pipes. However, despite all the benefits, IoT suffers from being vulnerable to cyber attacks. Examples have been provided later in Chapter 1. In this project among building systems, HVAC system is chosen to be automated with a new control method called MPC (Model Predictive Control). This method is fast, very energy efficient and has a lower than 0.001 rate of error for regulating the space temperature to any temperature that the occupants desire according to the results of this project. Furthermore, a PID (Proportional–Integral–Derivative) controller has been designed for the HVAC system that in the exact same cases MPC shows a much better performance. To design controllers for HVAC system and set the temperature to the desired value a method to automate balancing the heat flow should be found, therefore a thermal model of building should be available that using this model, the amount of heat, flowing in and out of a space in the building disregarding the external weather would be known to estimate. To automate the HVAC system using the programming languages like MATLAB, there is a need to convert the thermal model of the building to a mathematical model. This mathematical model is unique for each building depending on how many floors it has, how wide it is, and what materials have been used to construct the building. This process is needs a lot of effort and time even for buildings with 2 floors and 2 rooms on each floor and at the end the engineer might have done it with error. In this project you will see a software that will do the conversion of thermal model of buildings in any size to their mathematical model automatically, which helps improving the HVAC controllers to set temperature to the value occupants desire and avoid errors and time loss which is put both into calculations and troubleshooting. In addition, a test environment has been designed and constructed as a cyber physical system that allows us to test the IoT- enabled control systems before implementing them on real buildings, observe the performance, and decide if the system is satisfying or not. Also, all cyber threats can be explored and the solutions to those attacks can be evaluated. Even for the systems that are already out there, there is an opportunity to be assessed on this testbed and if there is any vulnerability in case of cyber security, solutions would be evaluated and help the existing systems improve. / Master of Science / Buildings function as shelters more than any thing else, and this has allowed humans to use it as a space to store important things like private and important information. Therefore, this space should be safe and secure from any vulnerabilities for occupants and their information. Smart buildings, have made a great difference in increasing the comfort level of occupants, but they haven’t been greatly successful achieving their objectives [50]. Therefore, a new technology called, Internet of Things, or IoT, is combined with the smart buildings to improve their performance [23]. IoT is the inter-networking of things embedded with electronics, software, sensors, actuators, and network connectivity to collect and exchange data, and things in this definition is anything and everything around us and even ourselves. Internet of Things (IoT) has helped improving the smart buildings and getting a considerable amount of energy efficiency [27]. But adding Internet of Things has added a network of things connected to internet, which gives the cyber hackers an opportunity to hack the buildings, and get access to the information stored inside the building or put even occupants lives in danger. Therefore, in this thesis the following items have been contributed: • Designing and programming a novel control system for HVAC system of the buildings (Model Predictive Control): This is a new method to control HVAC system of buildings and in comparison with the methods available in the market, it is the most energy efficient, it is faster, and it has a lower error rate in following the desired temperature of the occupants. • Design and construction of IoT- enabled smart building testbed: Since cyber attacks make buildings vulnerable, the author believes it is better to build a test environment to simulate the buildings and the control methods that are used inside the buildings, and try to evaluate performance of the control methods before implementing them on real buildings. Also, by installing IoT sensors inside the test environment, the engineers can perform some cyber attack tests, and also evaluate the solutions for each attack on the testbed. • Design and program a software to convert thermal model of buildings to mathematical model : In designing a new control method for HVAC system of buildings, the first required information is the thermal model of the buildings. Eventually, there is a need to program. Thus, the thermal model should be converted to a mathematical model. However, there is a heavy manual calculation behind it that is really overwhelming, tiring, with a high possibility of error, and time-consuming even for a very small sized building. Therefore, automating this process in terms of a software that takes the information of thermal model of buildings as an input and giving the output of the mathematical model of building is a considerable achievement.

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions. / M.S. / The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle on average. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

Nowadays, the increasing of the energy consumption is producing serious changes in the natural environment as the global warming. Around the 40% of all greenhouse gas emissions in developed countries come from the building equipments, where approximately 60% are produced by the air conditioning systems. In this context, ground coupled heat pumps are an attractive solution as air conditioning systems in commercial buildings due to their higher efficiency compared with the conventional air to water heat pump. In fact, the American Environmental Protection Agency recognizes ground coupled heat pump systems among the most efficient and comfortable systems available today. Nevertheless, the energy efficiency of the ground coupled heat pumps could be improve by means a properly management of the di erent equipments which form them. The objective of the research of this PhD thesis will be the development of management strategies in the air conditioning system based on the ground coupled heat pumps to improve its energy efficiency at the same time that we keep the thermal comfort in the conditioned areas. The energy management strategies will be oriented in the three ways: combining of several generation systems (ground coupled heat pump and air to water heat pump), decoupling thermal generation from thermal distribution (by means a thermal storage device) and strategies based on the management of the devices of the system (by means of continuous regulation of them). From the results of this research we can obtain two main conclusions. The rst one is that a properly management of a system composed by a thermal storage, an air to water heat pump and a ground coupled heat pump produce an improvement of the energy efficiency around a 40% respect to a conventional system and around a 18% respect to a geothermal system. The second main conclusion of this thesis is that a properly management strategy in continuous regulation of the devices which are part of a ground coupled .. / Pardo García, N. (2009). Energy efficiency improvement of hybrid ground coupled HVAC systems from thermal energy generation and storage management [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/6065