AI Revolutionizes Energy Efficiency by Detecting Least Energy-Efficient Homes


  • University of Cambridge scientists pioneer AI model to identify energy-inefficient homes.
  • AI offers a 90% success rate in detecting hard-to-decarbonise (HtD) homes.
  • This technology accelerates efforts to achieve net-zero emissions and combat climate change.

Our homes are energy hogs, consuming vast amounts of power for heating, lighting, and running our daily appliances. However, identifying which homes are wasting energy and how to rectify this has been a challenging task—until now. Scientists at the University of Cambridge have harnessed the power of artificial intelligence (AI) to spot energy-inefficient homes accurately.

The role of AI in energy efficiency

Artificial intelligence, or AI, is the science of enabling computers to perform tasks typically requiring human intelligence. In this groundbreaking development, AI is used to pinpoint houses with poor energy efficiency, marking a significant step towards sustainable living.

This innovative model is the first of its kind and can not only detect energy wastage but also precisely identify where and how much energy may be escaping from a residence. These homes with energy inefficiency are often referred to as “hard-to-decarbonise” (HtD).

Understanding decarbonisation

Decarbonisation involves transitioning from fossil fuel-based energy sources to clean, renewable energy alternatives. Fossil fuels, including coal, oil, and natural gas, are finite resources that release carbon dioxide when burned, contributing to global warming. HtD homes present challenges in transitioning to cleaner energy sources, hampering efforts to combat climate change.

Houses can become HtD for various reasons, including their age, construction materials, and location. These factors can make it difficult to switch to cleaner, greener energy sources for heating and powering these homes.

The pioneering AI model

Data scientist Maoran Sun and Dr. Ronita Bardhan have created a groundbreaking AI model capable of identifying HtD homes with remarkable accuracy, achieving up to a 90% success rate. As they gather more data, they anticipate the model’s accuracy will further improve.

This development holds immense significance because HtD homes are responsible for over a quarter of all emissions generated from housing.

Hard-to-decarbonise homes contribute significantly to carbon emissions, making them a major hurdle in achieving net-zero emissions targets. Net-zero targets are crucial for mitigating climate change, and identifying HtD homes is essential for governments and local authorities to reach these ambitious goals.

The AI model’s functionality

The AI model created by Dr. Bardhan and Maoran Sun is designed to help governments and authorities identify HtD homes more efficiently. By directing them to high-priority homes that require immediate attention, the model saves valuable time and resources.

The model is capable of assessing various factors contributing to energy inefficiency, including heat loss through roofs or windows. Additionally, it can determine whether a building is old or new, further assisting in prioritizing energy-efficient upgrades.

The road ahead to Accelerate green targets

As the AI model continues to evolve and refine its accuracy with additional data, it is expected to play a pivotal role in helping governments and local authorities meet their green targets more expeditiously. The ability to pinpoint energy-inefficient homes accurately and recommend specific energy-saving measures will significantly contribute to the global effort to combat climate change.

The University of Cambridge’s groundbreaking AI model has the potential to revolutionize the way we approach energy efficiency in our homes. By accurately identifying energy-inefficient homes, especially those deemed hard-to-decarbonise, this innovative technology brings us one step closer to achieving ambitious net-zero emissions targets. As AI continues to advance and evolve, it may prove to be a game-changer in the fight against climate change, offering a more sustainable future for generations to come.

Disclaimer. The information provided is not trading advice. Cryptopolitan.com holds no liability for any investments made based on the information provided on this page. We strongly recommend independent research and/or consultation with a qualified professional before making any investment decisions.

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Editah Patrick

Editah is a versatile fintech analyst with a deep understanding of blockchain domains. As much as technology fascinates her, she finds the intersection of both technology and finance mind-blowing. Her particular interest in digital wallets and blockchain aids her audience.

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