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Research field 1: Building energy simulation
Research field 1: Building energy simulation
Building energy simulation is a research field that involves the use of computer models to simulate the energy performance of buildings and building systems. These models can be used to assess the energy efficiency of existing buildings, design new buildings and building systems, and evaluate the impact of different design strategies and technologies on building energy consumption.
The field of building energy simulation is an important tool for advancing sustainable building design and reducing energy consumption and greenhouse gas emissions in the built environment.
Research field 2: Urban building energy simulation
Research field 2: Urban building energy simulation
Urban energy simulation is a research field that focuses on modeling and simulating the energy consumption and production of urban systems, including buildings, transportation, and infrastructure. These models can be used to evaluate the performance of urban energy systems, identify opportunities for energy savings and renewable energy integration, and inform urban energy policy and planning. The field of urban energy simulation is important for advancing sustainable urban development and addressing the challenges of climate change and urbanization.
Image: Reinhart, C. F., & Davila, C. C. (2016). Urban building energy modeling–A review of a nascent field. Building and Environment, 97, 196-202.
Research field 3: Bayesian calibration
Research field 3: Bayesian calibration
Bayesian calibration is an effective method for improving the accuracy and reliability of building energy simulation models. By adjusting the model parameters to fit observed data, Bayesian calibration can help to reduce uncertainties in model predictions and improve the accuracy of energy consumption and performance predictions. This approach is particularly important for advancing sustainable building design and reducing energy consumption in the built environment.
Research field 4: Building energy data analysis with deep neural network
Research field 4: Building energy data analysis with deep neural network
The research field of building energy data analysis focuses on utilizing advanced statistical methods and machine learning techniques to understand and predict building energy consumption and performance. Key methods employed in this field include sensitivity analysis, principal component analysis (PCA) uncertainty analysis, and deep neural networks.
Sensitivity Analysis: This technique identifies the input parameters that have the greatest influence on the model output, enabling researchers to prioritize the most significant factors affecting building energy performance.
PCA Uncertainty Analysis: This method quantifies the impact of input parameter uncertainties on the model output, which helps in reducing the overall uncertainty in energy consumption and performance predictions.
Deep Neural Networks: These powerful machine learning models can automatically learn complex patterns in large datasets and offer improved predictive performance compared to traditional statistical methods. They are particularly useful for analyzing high-dimensional building energy data and capturing intricate relationships between various factors influencing energy consumption.
Research field 5: Central heat pump water heater (CHPWH)
Research field 5: Central heat pump water heater (CHPWH)
Our research lab is currently exploring the adaptation of central heat pump water heaters, already widely utilized in the United States, for use in South Korea. We are focusing on tailoring these systems to align with Korea's unique climatic and infrastructural conditions. By conducting comprehensive studies on local energy usage patterns and environmental impacts, our goal is to enhance the system’s efficiency and compatibility, thus promoting a more sustainable and energy-efficient solution for heating water across Korean homes and businesses.
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