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Energy Efficiency

Researchers Utilize AI, Google Street View to Predict Household Energy Costs

A team of experts from the University of Notre Dame, in collaboration with faculty at the University of Maryland and University of Utah, have found a way to use artificial intelligence to analyze a household’s passive design characteristics and predict its energy expenses with more than 74 percent accuracy, the University of Notre Dame reported.

By combining their findings with demographic data including poverty levels, the researchers have created a comprehensive model for predicting energy burden across 1,402 census tracts and nearly 300,000 households in the Chicago metropolitan area. 

The research, which was recently published in the journal Building and Environment, focused on three of the most influential factors in passive design: (1) the size of windows in the dwelling, (2) the types of windows (operable or fixed) and (3) the percent of the building that has proper shading.

The team analyzed Google Street View images of residential buildings in Chicago and then performed different machine learning methods to find the best prediction model.

The results show that passive design characteristics are associated with average energy burden and are essential for prediction models, the University of Notre Dame noted in a news release.

The resulting model “is easily scalable and far more efficient than previous methods of energy auditing, which required researchers to go building by building through an area,” the news release said.

The researchers are also working toward including additional passive design characteristics in the analysis, such as insulation, cool roofs and green roofs. And eventually, they hope to scale the project up to evaluate and address energy burden disparities at the national level.

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