<p>Research on net-zero energy buildings (NZEBs) has been largely centered around improving building energy performance, while little attention has been given to indoor air quality. A critically important class of indoor air pollutants are nanoaerosols – airborne particulate matter smaller than 100 nm in size. Nanoaerosols penetrate deep into the human respiratory system and are associated with deleterious toxicological and human health outcomes. An important step towards improving indoor air quality in NZEBs is understanding how occupants, their activities, and building systems affect the emissions and fate of nanoaerosols. New developments in smart energy monitoring systems and smart thermostats offer a unique opportunity to track occupant activity patterns and the operational status of residential HVAC systems. In this study, we conducted a one-month field campaign in an occupied residential NZEB, the Purdue ReNEWW House, to explore how energy use profiles and smart thermostat data can be used to characterize indoor nanoaerosol dynamics. A Scanning Mobility Particle Sizer and Optical Particle Sizer were used to measure indoor aerosol concentrations and size distributions from 10 to 10,000 nm. AC current sensors were used to monitor electricity consumption of kitchen appliances (cooktop, oven, toaster, microwave, kitchen hood), the air handling unit (AHU), and the energy recovery ventilator (ERV). Two Ecobee smart thermostats informed the fractional amount of supply airflow directed to the basement and main floor. The nanoaerosol concentrations and energy use profiles were integrated with an aerosol physics-based material balance model to quantify nanoaerosol source and loss processes. Cooking activities were found to dominate the emissions of indoor nanoaerosols, often elevating indoor nanoaerosol concentrations beyond 10⁴ cm^-3. The emission rates for different cooking appliances varied from 10¹¹ h^-1 to 10¹⁴ h^-1. Loss rates were found to be significantly different between AHU/ERV off and on conditions, with median loss rates of 1.43 h^-1 to 3.68 h^-1, respectively. Probability density functions of the source and loss rates for different scenarios will be used in Monte Carlo simulations to predict indoor nanoaerosol concentrations in NZEBs using only energy consumption and smart thermostat data.</p>

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.

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.

В первом разделе работы рассмотрены факторы, определяющие климатические условия в помещении и методика расчета теплопотерь. Также приведено описание современных способов регулирования температуры в помещении и рассмотрены два реализованных алгоритма для управления температурным режимом в помещении, основанные на математическом анализе и на базе нечеткой логике. Во втором разделе рассматривается тепловая модель здания для традиционной системы регулирования температуры, реализованная в Matlab-Simulink. По рассмотренной модели проведен сравнительный анализ использования различного вида регуляторов и его выбор для реализации интеллектуальной системы управления температурным режимом. В третьем разделе описана реализация алгоритма для предикции температуры на базе нейронных сетей. А также представлено описание реализации аппаратной и программной части интеллектуальной системы. / In the first section of the dissertation, the factors that determine the climatic conditions in the room and the method for calculating heat loss are considered. It also provides a description of modern methods of room temperature control and considers two implemented algorithms for controlling the temperature regime in a room, based on mathematical analysis and on the basis of fuzzy logic. The second section examines a building thermal model for a traditional temperature control system implemented in Matlab-Simulink. Based on the considered model, a comparative analysis of the use of various types of controllers and its choice for the implementation of an intelligent temperature control system is carried out. The third section describes the implementation of an algorithm for predicting temperature based on neural networks. It also provides a description of the implementation of the hardware and software of the intelligent system.

As smart homes become increasingly common and concerns over Internet of Things (IoT) security grow, this study delves into the vulnerabilities of smart thermostats. These devices offer convenience but also comes with increased risk of cyber attacks. This study evaluates the susceptibility of the Shelly Thermostatic Radiator Valve (TRV) and the Meross Smart Thermostat to potential threats across various attack vectors – encompassing firmware, network, radio, and cloud – through penetration testing guided by the PatrIoT methodology. Findings reveal four unknown vulnerabilities in the Meross Smart Thermostat and two in the Shelly TRV. These vulnerabilities consist of insecure firmware updates, lack of network encryption, exploitable radio communication, and cloud-related gaps. Recommendations aiming at mitigating the found vulnerabilities include implementing secure Wi-Fi access points for both models during setup, and ensuring strong encryption for the Meross Smart Thermostat’s radio communication. The study contributes to an increased awareness of potential security risks associated with these devices, though the extent of vulnerabilities across all smart thermostat models cannot be definitively concluded. / I takt med att smarta hem blir allt vanligare och med växande medvetenhet om säkerhet för Internet of Things (IoT), undersöker denna studie potentiella sårbarheter hos smarta termostater. Dessa enheter förenklar användares vardag, men ger också upphov till nya cyberhot. Denna studie granskar Shelly TRV och Meross Smart Thermostat för potentiella hot inom attackvektorerna firmware, nätverk, radio och moln, genom penetreringstestning som vägleds av PatrIoT-metodiken. Resultatet är fyra upptäckta sårbarheter i Meross-modellen och två i Shelly Thermostatic Radiator Valve (TRV) inklusive osäkra firmware-uppdateringar, brist på nätverkskryptering, utnyttjbar radiokommunikation och molnrelaterade problem. Rekommendationer med syfte att mitigera de upptäckta sårbarheterna inkluderar att implementera säkra Wi-Fi-åtkomstpunkter för båda modellerna under installationen och att säkerställa stark kryptering för Meross Smart Thermostat:s radiokommunikationen. Studien bidrar till en ökad medvetenhet om potentiella säkerhetsrisker som är förknippade med dessa enheter, även om det inte kan fastställas hur vanligt det är med sårbarheter i smarta termostater

This thesis explores home energy management (HEM), an emerging field for interface design and sustainability. Section 1 introduces HEM’s broader context. In Section 2, I review the literature surrounding HEM. Section 3 outlines the usability study on the ecobee Smart Thermostat, to evaluate the technology’s ease-of-use, and better understand users’ experience with current HEM technology. Section 4 describes a “Critical Making” workshop, where participants investigated HEM through material interaction and discussion. Section 5 describes and evaluates the potential design spaces gleaned from previous sections. In Section 6, I return to the literature to investigate key concepts underlying the design intervention for the chosen design space. Section 7 describes my design intervention and experimental evaluation. In Section 8, I present the study results, which suggest enhanced display labelling had a significant and directional effect on user-selected temperatures. In Section 9, I discuss these results, study limitations, and make conclusions and recommendations.

This thesis explores home energy management (HEM), an emerging field for interface design and sustainability. Section 1 introduces HEM’s broader context. In Section 2, I review the literature surrounding HEM. Section 3 outlines the usability study on the ecobee Smart Thermostat, to evaluate the technology’s ease-of-use, and better understand users’ experience with current HEM technology. Section 4 describes a “Critical Making” workshop, where participants investigated HEM through material interaction and discussion. Section 5 describes and evaluates the potential design spaces gleaned from previous sections. In Section 6, I return to the literature to investigate key concepts underlying the design intervention for the chosen design space. Section 7 describes my design intervention and experimental evaluation. In Section 8, I present the study results, which suggest enhanced display labelling had a significant and directional effect on user-selected temperatures. In Section 9, I discuss these results, study limitations, and make conclusions and recommendations.