Conference Agenda

Residential and commercial buildings in the U.S. are major energy consumers, and their energy demand is expected to rise significantly in the coming decades. This research focuses on developing building envelope solutions that serve as decentralized energy sources. By integrating thermoelectric generators (TEGs) and phase change materials (PCMs), the aim is to generate electricity for building operations and reduce energy consumption. TEGs generate voltage through the Seebeck effect, utilizing temperature gradients, but low thermal to electrical conversion efficiency remains a challenge. A key to improving TEG efficiency is adding a heat sink to dissipate heat and maintain the optimal temperature gradient. PCMs act as effective heat sinks by undergoing phase transitions. They store thermal energy when transitioning from solid to liquid and release it when solidifying, stabilizing the temperature gradient needed for TEG efficiency. This research proposes a dynamic building envelope that generates electricity by incorporating TEG modules and PCMs as energy harvesters and heat sinks. The generated electricity can be used for certain building operations, such as powering automated shading devices or charging sensors for indoor environmental monitoring. In this research, we used two 40 mm by 40 mm TEG modules with BioPCM (melting point 29°C) as a heat sink. Under a 15°C temperature difference, the TEGs produced approximately 400 millivolts, enough to power a motor that rotated a 400 mm by 70 mm vertical aluminum louver for a few seconds. These results show the potential of integrating TEGs and PCMs into building envelopes as decentralized energy solutions. The study suggests that further development of TEG-PCM integration could lead to energy savings in residential and commercial buildings, offering a sustainable method for electricity generation and reducing energy demand.

Energy storage plays a critical role in ensuring the stability of renewable energy systems, particularly due to the intermittency of sources like solar power. While batteries are the most commonly used form of energy storage, they face significant challenges, including degradation over time and environmental harm from improper disposal. As a sustainable alternative, hydrogen storage presents a promising solution. Using electrolyzers, electrical energy can be stored as hydrogen and later converted back to electricity via fuel cells when needed. As part of a doctoral project to integrate reversible fuel cells into building skin for energy storage, this study investigates the sizing of hydrogen storage in different climate zones by integrating Matlab/Simulink with EnergyPlus to model building energy loads and solar energy potential. Phoenix, Arizona, and Chicago, Illinois, were selected for their contrasting climate characteristics—Phoenix is hot and dry, while Chicago is cold and humid. The model accounts for temperature fluctuations, as the energy system is integrated into the building façade, which directly affects the efficiency of components. In the proposed design, photovoltaic-reversible proton exchange membrane fuel cell (PV-RPEMFC) panels are assumed to be installed on the south, east, and west opaque walls of the reference apartment building model. The daily average hydrogen production for March, June, September and December, along with the maximum hydrogen production for a day, were determined using the Matlab/Simulink model. The study calculates the required storage capacity for different locations by exploring various storage methods, including pressurized tanks, cryogenic liquid tanks, and metal hydride tanks. The size and weight of the tank vary depending on the storage method used. Since less solar energy is available in Chicago, the simulation results indicate that the storage volume in Chicago is 22% less than in Phoenix.

Providing thermal comfort for building occupants and accurately predicting that is essential for enhancing both occupant well-being and building energy efficiency. Traditional models often assume a uniform indoor thermal environment, overlooking individual comfort preferences. In response, personalized and localized heating systems have emerged as effective strategies for improving comfort and reducing energy consumption. Personalized heating systems, such as radiant heating in office buildings, allow users to adjust heating to suit their preferences, targeting specific body parts for improved thermal comfort. This study investigates the impact of radiant heating on local body parts’ skin temperature and whole-body thermal comfort. Experiments were conducted in a climate chamber, where subjective responses were gathered through questionnaires and objective data (skin and core body temperatures) were recorded using sensors. The result was a large dataset (~12,000 data points), making traditional statistical methods insufficient for pattern recognition and prediction. To address this, machine learning models were employed. Linear, Ridge, and Lasso regression were used to evaluate correlations between thermal parameters and comfort, with the best performance seen in the linear regression model (training score of 0.76, test score of 0.71). Additionally, Random Forest algorithms were implemented to predict thermal comfort. The findings show a strong relationship between targeted heating of specific body parts and overall thermal satisfaction, demonstrating that personalized heating can enhance comfort while reducing energy consumption. This study highlights the potential of integrating machine learning models with personalized comfort systems and building automation to increase energy efficiency and occupants’ well-being inside buildings.