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No description available.

No description available.

Many countries in hot-humid regions cope with rapid urbanisation. Land cost becomes higher especially in high-density areas. There is a demand of residential and commercial buildings due to the economic growth in the cities. Row house became a popular building type since it combines both functions in one place. A large amount of row houses are found in Bangkok, Thailand. Despite their versatility, they are not properly designed for the climate and urban conditions. The occupants adopt air conditioning systems to control indoor comfort whereas row houses are not properly designed for the installation of the airconditioners. This increases the energy demand. This research aims to establish appropriate design guidelines and recommendations to ensure indoor comfort in urban row houses in hot-humid climates. Thermal comfort is the main concern apart from others such as visual, acoustic and indoor air quality. Thermal stress should be minimised by primarily passive means as long as possible and the building design should also provide the flexibility for operating active systems whenever they are required. To achieve this aim, it is important to assess indoor conditions in existing row houses to provide a basis of comparison. A case study in Bangkok has been selected for a field investigation during the hottest month of the year to examine how the case study responds to the critical condition. Flow visualisation tests have been conducted to compare the results of indoor air velocity to those of the field investigation. Computer simulations have been carried out to investigate and compare the performance of design variables affecting indoor comfort such as orientation, zone location, roof and wall materials, aperture schedules, and shading devices. Two blocks of 4-level row houses have been simulated and three units: an intermediate and two units at the both ends of the row have been examined. There are two sets of the simulations: naturally ventilated and air-conditioned cases. Then, the design guidelines and recommendations are developed based on the results from the two series of simulations. Results from the 3-day field investigation show that indoor conditions of the case study are overheated. The indoor temperatures are slightly lower than To during the day while they are higher than To during the night. These results correspond to those of simulation runs for a verification study. The wind tunnel test shows low indoor air velocities. It is recommended to install ceiling fans to increase the wind speed enough to generate cooling effect. The level of interior illuminance drops with distance from the window. It should be improved by installing artificial light sources in the area far from the opening. The sound levels exceed the noise control limit at all times. Results from the simulations for the naturally ventilated and air-conditioned row houses show some similarities. In the former, internal conditions of the occupied units are above the comfort limit almost all day. Orientation is a crucial factor affecting the indoor thermal condition. The front of a row house should face either north or south and the end unit with east-facing side wall performs better than that with west-facing side wall. The worst orientation is generally west but the east could be worse for the end unit with south-facing side wall at such an orientation. The effect of zone location is also related to the orientation. The intermediate unit is more sensitive to design variables since it generally shows the biggest T difference between the variants with the best and the worst design factors. Aperture schedule has a great effect on indoor conditions for naturally ventilated cases. Closing windows during the day could keep the zone cooler than opening the windows which would admit the hot air from the outside. Concrete flat roof with ceiling insulation gives the best results while metal sheet roof gives the worst. Concrete block with acoustic board performs best for both naturally ventilated and air-conditioned buildings while the worst wall type is aerated concrete in cases of the naturally ventilated buildings and common brick wall in cases of the air-conditioned ones. However, the results from the effect of roof and wall material study show that adding insulation could improve the indoor condition more effectively than changing the roof and wall materials. An additional height from the mezzanine floor only affects the thermal performance of the room on ground floor. In comparison to the row house without mezzanine floor, the presence of mezzanine slightly increases indoor temperature in naturally ventilated cases while increases cooling load of the room on the ground floor drastically in air-conditioned cases. Shading devices should be designed particularly for each orientation since their effects are tremendous once applied to the opening to protect it from solar radiation. The limited distance between the front of row houses and the street as allowed in the existing building regulations should be extended for the devices to provide enough shading for the building.

Many countries in hot-humid regions cope with rapid urbanisation. Land cost becomes higher especially in high-density areas. There is a demand of residential and commercial buildings due to the economic growth in the cities. Row house became a popular building type since it combines both functions in one place. A large amount of row houses are found in Bangkok, Thailand. Despite their versatility, they are not properly designed for the climate and urban conditions. The occupants adopt air conditioning systems to control indoor comfort whereas row houses are not properly designed for the installation of the airconditioners. This increases the energy demand. This research aims to establish appropriate design guidelines and recommendations to ensure indoor comfort in urban row houses in hot-humid climates. Thermal comfort is the main concern apart from others such as visual, acoustic and indoor air quality. Thermal stress should be minimised by primarily passive means as long as possible and the building design should also provide the flexibility for operating active systems whenever they are required. To achieve this aim, it is important to assess indoor conditions in existing row houses to provide a basis of comparison. A case study in Bangkok has been selected for a field investigation during the hottest month of the year to examine how the case study responds to the critical condition. Flow visualisation tests have been conducted to compare the results of indoor air velocity to those of the field investigation. Computer simulations have been carried out to investigate and compare the performance of design variables affecting indoor comfort such as orientation, zone location, roof and wall materials, aperture schedules, and shading devices. Two blocks of 4-level row houses have been simulated and three units: an intermediate and two units at the both ends of the row have been examined. There are two sets of the simulations: naturally ventilated and air-conditioned cases. Then, the design guidelines and recommendations are developed based on the results from the two series of simulations. Results from the 3-day field investigation show that indoor conditions of the case study are overheated. The indoor temperatures are slightly lower than To during the day while they are higher than To during the night. These results correspond to those of simulation runs for a verification study. The wind tunnel test shows low indoor air velocities. It is recommended to install ceiling fans to increase the wind speed enough to generate cooling effect. The level of interior illuminance drops with distance from the window. It should be improved by installing artificial light sources in the area far from the opening. The sound levels exceed the noise control limit at all times. Results from the simulations for the naturally ventilated and air-conditioned row houses show some similarities. In the former, internal conditions of the occupied units are above the comfort limit almost all day. Orientation is a crucial factor affecting the indoor thermal condition. The front of a row house should face either north or south and the end unit with east-facing side wall performs better than that with west-facing side wall. The worst orientation is generally west but the east could be worse for the end unit with south-facing side wall at such an orientation. The effect of zone location is also related to the orientation. The intermediate unit is more sensitive to design variables since it generally shows the biggest T difference between the variants with the best and the worst design factors. Aperture schedule has a great effect on indoor conditions for naturally ventilated cases. Closing windows during the day could keep the zone cooler than opening the windows which would admit the hot air from the outside. Concrete flat roof with ceiling insulation gives the best results while metal sheet roof gives the worst. Concrete block with acoustic board performs best for both naturally ventilated and air-conditioned buildings while the worst wall type is aerated concrete in cases of the naturally ventilated buildings and common brick wall in cases of the air-conditioned ones. However, the results from the effect of roof and wall material study show that adding insulation could improve the indoor condition more effectively than changing the roof and wall materials. An additional height from the mezzanine floor only affects the thermal performance of the room on ground floor. In comparison to the row house without mezzanine floor, the presence of mezzanine slightly increases indoor temperature in naturally ventilated cases while increases cooling load of the room on the ground floor drastically in air-conditioned cases. Shading devices should be designed particularly for each orientation since their effects are tremendous once applied to the opening to protect it from solar radiation. The limited distance between the front of row houses and the street as allowed in the existing building regulations should be extended for the devices to provide enough shading for the building.

<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.

<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>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>