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Applying Statistical Methods to Air Quality and Asthma Data in Chicago Homes
Title
Applying Statistical Methods to Air Quality and Asthma Data in Chicago Homes
Creator
Abromitis, Kari
Date
2020
Description
This thesis investigates one years’ worth of indoor and outdoor air pollution data collected from Chicago area homes in relation to results...
Show moreThis thesis investigates one years’ worth of indoor and outdoor air pollution data collected from Chicago area homes in relation to results from monthly asthma surveys taken by the residents of those homes. This examination involves the processing and calibration of this large dataset, basic statistical analysis, and analysis of asthma as related to variation of air pollution and air pollution resulting from nearby transportation sources. The data was collected as a part of the Breathe Easy project, which was funded by the HUD and involved IIT and Elevate Energy, a Chicago-based economic development organization that promotes building equality through climate action. The majority of the data processing and analysis were performed using Python and it is intended for continued use during the ongoing Breathe Easy project. The basic statistical analysis of this data led to initial investigations of how the variability of pollutants on a daily basis triggered asthma severity and symptoms. There were limited relationships observed between asthma symptoms and pollutant variability, and it was found to not be as important as overall pollutant levels. A second investigation was pursued to examine how the proximity to transportation, including Metra trains, freight trains, elevated rail trains, highways, bus lines, and busy roads, affected indoor and outdoor pollution levels at each home, as well as concurrent asthma outcomes. Similar to previous research, there was some relation for transportation closeness, particularly for highways and Metra trains, and pollution emitted that effects the health of nearby residents. In addition, homes that had greater air infiltration (via envelope airtightness measurements) had elevated levels of particulate matter – the pollutant most associated with transportation proximity. This thesis provides a basis for further investigations in this ongoing project and for similar asthma and air quality relationship studies.
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ADVANCING OPEN-SOURCE TOOLS FOR INDOOR ENVIRONMENTAL MONITORING AND BUILDING SYSTEMS CONTROLS USING WIRELESS SENSOR NETWORKS
Title
ADVANCING OPEN-SOURCE TOOLS FOR INDOOR ENVIRONMENTAL MONITORING AND BUILDING SYSTEMS CONTROLS USING WIRELESS SENSOR NETWORKS
Creator
Ali, Akram Syed
Date
2021
Description
Incorporating data monitoring and visualization tools in buildings can provide a glimpse into their energy use, thermal performance, daily...
Show moreIncorporating data monitoring and visualization tools in buildings can provide a glimpse into their energy use, thermal performance, daily operation, and maintenance requirements. However, buildings have traditionally been monitored using hardware and software that are expensive, proprietary, and often limited in terms of ease of use and flexibility. Many existing buildings remain unmonitored or poorly monitored, leaving many opportunities for energy savings and improving indoor environmental conditions unaddressed. To this end, the goal of this research is to develop and demonstrate an open-source hardware and software platform for monitoring and controlling the performance of buildings and their systems, called Elemental. It is designed to provide real-time data on indoor environmental quality, energy usage, heating, ventilating, and air-conditioning (HVAC) operation, and other factors to its users, and provide easy development of building controls. It combines: (i) custom low power printed circuit boards (PCBs) with RF transceivers for wireless sensors, control nodes, and USB gateway, (ii) a Raspberry Pi with custom firmware acting as a backhaul, and (iii) custom software applications that manage data storage, device configuration and interface for querying and visualizing the data in real-time. The platform is built around the idea of a private, secure, and open technology for the built environment. Among its many applications, the platform allows occupants to investigate anomalies in energy usage, environmental quality, and thermal performance. It also includes multiple frontends to view and analyze building activity data, which can be used directly in building controls. This proposal describes the development process of the hardware and software used in the Elemental platform along with three distinct applications including: (1) deployment in a research lab for long-term data collection and automated analysis, (2) use as a full-home energy and environmental monitoring solution, and (3) building heating system automation at the room-level with the development and deployment of a custom radiator control. Through these applications, this work demonstrates that the platform allows easy and virtually unlimited datalogging, monitoring, and analysis of real-time sensor data with low setup costs. Low-power sensor nodes placed in abundance in a building can also provide precise and immediate fault-detection, allowing for tuning equipment for more efficient operation and faster maintenance during the lifetime of the building.
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Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Title
Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator
Riley, Christopher W.
Date
2023
Description
The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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ESTIMATING PM2.5 INFILTRATION FACTORS FROM REAL-TIME OPTICAL PARTICLE COUNTERS DEPLOYED IN CHICAGO HOMES BEFORE AND AFTER MECHANICAL VENTILATION RETROFITS
Title
ESTIMATING PM2.5 INFILTRATION FACTORS FROM REAL-TIME OPTICAL PARTICLE COUNTERS DEPLOYED IN CHICAGO HOMES BEFORE AND AFTER MECHANICAL VENTILATION RETROFITS
Creator
Wang, Mingyu
Date
2021
Description
PM2.5 are fine inhalable particles that are 2.5 micrometers or smaller in size. Indoor PM2.5 consists of outdoor PM2.5 (ambient PM2.5) that is...
Show morePM2.5 are fine inhalable particles that are 2.5 micrometers or smaller in size. Indoor PM2.5 consists of outdoor PM2.5 (ambient PM2.5) that is infiltrated into the indoor environment and indoor generated PM2.5 (non-ambient PM2.5). As people spend nearly 90% of their lifetimes indoors, with most of that time in their homes, PM2.5 exposure in homes results in severe health effects such as asthma. One strategy increasingly being used to dilute air pollutants generated indoors and improve indoor air quality (IAQ) in homes is the introduction of mechanical ventilation systems. However, mechanical ventilation systems also have the potential to introduce more ambient PM2.5 than relying on infiltration alone, although limited data exist to demonstrate the magnitude of impacts in occupied homes. The objective of this paper is to estimate the infiltration factor (Finf) of PM2.5 before and after installing mechanical ventilation systems in a subset of occupied homes. The data source utilized comes from the Breathe Easy Project, a more than 2-year-long study conducted in 40 existing homes in Chicago, IL aiming to explore the effects of three different types of mechanical ventilation system retrofits on IAQ and asthma. An automated algorithm was developed to remove indoor PM2.5 peaks in time-series data collected from optical particle counters deployed inside and outside of each home. The Finf was estimated using the resulting indoor/outdoor ratio with indoor peaks removed. Before mechanical ventilation retrofits, the weekly median Finf was 0.29 (summer median = 0.41, fall median = 0.26, winter median = 0.29, spring median = 0.30); after mechanical ventilation retrofits, the median Finf was 0.34 (winter median= 0.28, spring median = 0.45, summer median = 0.54, fall median = 0.20). Differences in Finf between pre- and post-intervention periods were not statistically significant (p = 0.23 from Wilcoxon signed rank tests). The median PM2.5 infiltration factor increased ~22% (from 0.27 to 0.33) with the installation of balanced ventilation systems with energy recovery ventilators (ERV), although differences were not statistically significant (Wilcoxon signed rank p = 0.35). The median PM2.5 infiltration factor decreased ~4% (from 0.28 to 0.27) after installing intermittent CFIS systems, which intermittently supply ventilation air through the existing central air handling units and associated filters (which were upgraded to a minimum of MERV 10 in all CFIS homes), although differences were not statistically significant (Wilcoxon signed rank p = 0.24). The median PM2.5 infiltration factor increased ~26% (from 0.35 to 0.44) with the installation of continuous exhaust-only systems, and differences were significant (Wilcoxon signed rank p = 0.04). These results suggest that the filtration mechanisms used on the CFIS and balanced systems were adequate for maintaining similar distributions of Finf values pre- and post-interventions whereas the increased delivery of outdoor air via the building envelope by exhaust-only systems significantly increased Finf following retrofits.
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Developing Novel Optimization Algorithms Applied To Building Energy Performance and Indoor Air Quality
Title
Developing Novel Optimization Algorithms Applied To Building Energy Performance and Indoor Air Quality
Creator
Faramarzi, Afshin
Date
2021
Description
Residential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy...
Show moreResidential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy use accounts for 38%, 9%, and 7% of building energy consumption, which results in 54% of the total energy consumption of the building. Energy efficiency improvements in buildings require consideration of optimal design, operation, and control of building components (e.g., mechanical and envelope systems). We can address this task by taking advantage of computational optimization methods throughout the design, operation, and control processes.Non-gradient metaheuristic optimization methods known as metaheuristics are some of the most popular and widely used optimization methods in Building Performance Optimization (BPO) problems. Conventional metaheuristics usually have simple mathematical models with low rate of convergence. On the other hand, high-performance metaheuristic optimizers are efficient and usually have a fast rate of convergence, but their mathematical models are hard to understand and implement. As such, researchers are usually not inclined to employ them in solving their problems. To this end, we aimed at developing optimization algorithms which borrow simplicity from conventional methods and efficiency from high-performance optimizers to solve problems fast and efficiently while being welcomed by users from throughout the world. Therefore, the overarching objective of this work is defined to first develop novel optimization algorithms which are simple in mathematical models and still efficient in solving optimization benchmark problems and then apply the methods to building energy performance and indoor air quality (IAQ) problems. In the first objective of this work, which is the development phase, two continuous optimization methods and one binary optimizer are developed and are separately described in three different tasks. The first method called Equilibrium Optimizer (EO) is a simple method inspired by the mass balance equation in a control volume. The second optimization method called Marine Predators Algorithm (MPA) is a more complicated method compared to EO and is inspired by widespread foraging strategies between marine predators in the ocean ecosystem. Finally, the third method is the binary version of an already developed equilibrium optimizer called Binary Equilibrium Optimizer (BEO). The second objective of the dissertation is the application phase which focuses on the application of the developed methods and other widely used methods in research and industry for solving the almost new BPO and IAQ problems. The results showed that the developed methods were able to either reach more energy-efficient solutions compared to the other methods or to show a considerably faster rate of convergence compared to other methods in the problems in which the optimal solutions are similarly obtained by different methods.
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Data-Driven Modeling for Advancing Near-Optimal Control of Water-Cooled Chillers
Title
Data-Driven Modeling for Advancing Near-Optimal Control of Water-Cooled Chillers
Creator
Salimian Rizi, Behzad
Date
2023
Description
Hydronic heating and cooling systems are among the most common types of heating and cooling systems installed in older existing buildings,...
Show moreHydronic heating and cooling systems are among the most common types of heating and cooling systems installed in older existing buildings, especially commercial buildings. The results of this study based on the Commercial Building Energy Consumption Survey (CBECS) indicates chillers account for providing cooling in more than half of the commercial office building floorspaces in the U.S. Therefore, to address the need of improving energy efficiency of chillers systems operation, research studies developed different models to investigate different chiller sequencing approaches. Engineering-based models and empirical models are among the popular approaches for developing prediction models. Engineering-based models utilize the physical principles to calculate the thermal dynamics and energy behaviors of the systems and require detailed system information, while the empirical models deploy machine learning algorithms to develop relationships between input and output data. The empirical models compared to the engineering-based approach are more practical in a system’s energy prediction because of accessibility to required data, superiority in model implementation and prediction accuracy. Moreover, selecting near accurate chiller prediction models for the chiller sequencing needs to consider the importance of each input variable and its contribution to the overall performance of a chiller system, as well as the ease of application and computational time. Among the empirical modeling methods, ensemble learning techniques overcome the instability of the learning algorithm as well as improve prediction accuracy and identify input variable importance. Ensemble models combine multiple individual models, often called base or weak models, to produce a more accurate and robust predictive model. Random Forest (RF) and Extra Gradient Boosting (XGBoost) models are considered as ensemble models which offer built-in mechanisms for assessing feature importance. These techniques work by measuring how much each feature contributes to the overall predictive performance of the ensemble.In the first objective of this work the frequency of hydronic cooling systems in the U.S. building stock for applying potential energy efficiency measures (EEMs) on chiller plants are explored. Results show that the central chillers inside the buildings are responsible for providing cooling for more than 50% of the commercial buildings with areas greater than 9,000 m2(~100,000 ft2). In addition, hydronic cooling systems contribute to the highest Energy Use Intensity (EUI) among other systems, with EUI of 410.0 kWh/m2 (130.0 kBtu/ft2). Therefore, the results of this objective support developing accurate prediction models to assess the chiller performance parameters as an implication for chiller sequencing control strategies in older existing buildings. The second objective of the dissertation is to evaluate the performance of chiller sequencing strategy for the existing water-cooled chiller plant in a high-rise commercial building and develop highly accurate RF chiller models to investigate and determine the input variables of greatest importance to chiller power consumption predictions. The results show that the average value of mean absolute percentage error (MAPE) and root mean squared error (RMSE) for all three RF chiller models are 5.3% and 30 kW, respectively, for the validation dataset, which confirms a good agreement between measured and predicted values. On the other hand, understanding prediction uncertainty is an important task to confidently reporting smaller savings estimates for different chiller sequencing control strategies. This study aims to quantify prediction uncertainty as a percentile for selecting an appropriate confidence level for chillers models which could lead to better prediction of the peak electricity load and participate in demand response programs more efficiently. The results show that by increasing the confidence level from 80% to 90%, the upper and lower bounds of the demand charge differ from the actual value by a factor of 3.3 and 1.7 times greater, respectively. Therefore, it proves the significance of selecting appropriate confidence levels for implementation of chiller sequencing strategy and demand response programs in commercial buildings. As the third objective of this study, the accuracy of these prediction models with respect to the preprocessing, selection of data, noise analysis, effect of chiller control system performance on the recorded data were investigated. Therefore, this study attempts to investigate the impacts of different data resolution, level of noise and data smoothing methods on the chiller power consumption and chiller COP prediction based on time-series Extra Gradient Boosting (XGBoost) models. The results of applying the smoothing methods indicate that the performance of chiller COP and the chiller power consumption models have improved by 2.8% and 4.8%, respectively. Overall, this study would guide the development of data-driven chiller power consumption and chiller COP prediction models in practice.
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