{"id":44598,"date":"2024-02-29T14:27:34","date_gmt":"2024-02-29T14:27:34","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=44598"},"modified":"2024-02-29T14:27:34","modified_gmt":"2024-02-29T14:27:34","slug":"predicting-home-energy-prices-with-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/predicting-home-energy-prices-with-artificial-intelligence\/44598\/","title":{"rendered":"Predicting home energy prices with Artificial Intelligence"},"content":{"rendered":"

University of Notre Dame researchers are using AI and Google Street View to predict home energy prices.<\/h2>\n

Collaborating with experts from the universities of Maryland and Utah, the researchers are utilising AI techniques to analyse a household’s passive design characteristics<\/a> to calculate home energy prices.<\/p>\n

The team believes the research will help inform policymakers and urban planners in identifying vulnerable communities, creating more sustainable building designs<\/a>, and reducing the growing energy burden on citizens.<\/p>\n

Siavash 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>\n

\"\"
Notre Dame researchers analyzed Google Street View images of residential buildings in Chicago to predict household energy expenses. Credit: University of Notre Dame<\/figcaption><\/figure>\n

Unlike 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":"\nPredicting home energy prices with Artificial Intelligence<\/title>\n<meta name=\"description\" content=\"Learn how University of Notre Dame researchers are using AI and Google Street View to predict home energy prices.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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