sustainable building designs<\/a>, and reducing the growing energy burden on citizens.<\/p>\nSiavash Ghorbany, a doctoral student in the Department of Civil and Environmental Engineering and Earth Science, explained: “The first step toward mitigating the energy burden for low-income families is to get a better understanding of the issue and to be able to measure and predict it.<\/p>\n
“So, we asked, ‘What if we could use everyday tools and technologies like Google Street View, combined with the power of machine learning, to gather this information?’ We hope it will be a positive step toward energy justice in the United States.”<\/p>\n
The growing energy burden for US households<\/h3>\n Low-income households across the US are grappling with an energy burden that is significantly higher than the national average, recent findings from the US Department of Energy reveal.<\/p>\n
A report found that more than 46 million households in the US are experiencing a substantial energy burden, defined as allocating over 6% of their gross income towards basic energy needs such as heating and cooling their homes.<\/p>\n
How passive design can reduce home energy prices<\/h3>\n Passive design strategies, such as natural ventilation and optimal use of sunlight, have emerged as potential solutions to mitigate home energy prices.<\/p>\n
However, the scarcity of data on passive design effectiveness poses a challenge in assessing its impact on a broader scale.<\/p>\n
To address this knowledge gap, a team of interdisciplinary experts devised a novel method employing AI.<\/p>\n
Scalable model for energy auditing<\/h3>\n The research concentrated on three pivotal elements in passive design\u2014window size, window type (operable or fixed), and the extent of shading. The study utilised a convolutional neural network to examine Google Street View images of residential structures in Chicago.<\/p>\n
Subsequently, various machine learning techniques were employed to determine the most effective prediction model. Their approach predicted home energy prices with an impressive accuracy rate exceeding 74%.<\/p>\nNotre Dame researchers analyzed Google Street View images of residential buildings in Chicago to predict household energy expenses. Credit: University of Notre Dame<\/figcaption><\/figure>\nUnlike conventional approaches that require meticulous building-by-building assessments, this model enables swift and scalable evaluations.<\/p>\n
The findings underscore the significance of passive design attributes in predicting average energy burden, emphasising their indispensable role in predictive modelling.<\/p>\n
Moving forward, the researchers aim to expand their analysis to include additional passive design elements such as insulation and green roofs.<\/p>\n
Moreover, they envision scaling up the project to address energy burden disparities at a national level, with the ultimate goal of fostering sustainable and equitable energy practices across the country.<\/p>\n","protected":false},"excerpt":{"rendered":"
Learn how University of Notre Dame researchers are using AI and Google Street View to predict home energy prices.<\/p>\n","protected":false},"author":15,"featured_media":44607,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24204],"tags":[570,628],"acf":[],"yoast_head":"\n
Predicting home energy prices with Artificial Intelligence<\/title>\n \n \n \n \n \n \n \n \n \n \n \n \n\t \n\t \n\t \n \n \n \n \n \n\t \n\t \n\t \n