Cloud-based Data Analytics for Assessing Retrofit Needs of Residential Buildings in Rural Iowa
In the United States, residential buildings utilize an extensive amount of energy, particularly in rural areas where buildings can often be older and less energy-efficient. Retrofitting these buildings can help reduce emissions and improve energy efficiency, reducing overall costs for building occupants. However, limited information on these buildings has made identifying which ones require retrofit difficult. To address this issue, this project was developed using data mining techniques to analyze energy consumption and energy use intensity according to various factors, such as building size, year of construction, weather data, and demographic data. Energy utility companies in rural Iowa provide the data for analysis, which is then displayed on a web-based dashboard for easy access. The dashboard features visualizations that portray the energy use intensity of buildings in a community. The use of data mining techniques and the visualizations provided by the dashboard makes it easier to understand the energy performance of residential buildings in rural areas, facilitating more targeted retrofit efforts to address their energy consumption levels. This project was built entirely on AWS cloud technologies with a serverless architecture, allowing more efficient and scalable data processing. This project can help identify high energy consumption and emissions from residential buildings in rural Iowa and potentially other areas in the US with similar building stock.
Committee: Mengdi Huai (co-major professor) and Ulrike Passe (co-major professor)
Join on Zoom: https://iastate.zoom.us/j/95215185013