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This AI Model Will Identify Decarbonization Opportunities in Buildings

Construction, being one of the biggest CO2-emitting sectors, demands rapid decarbonization. To achieve our net-zero targets, we must use all available tools – Electric Vehicles for mobility, low- carbon materials, and advanced AI models. Artificial intelligence, a promising avenue for operational optimization, proves to be a valuable ally in our decarbonization efforts.

Today, we will explore an AI model developed by researchers at the University of Cambridge that identifies hard-to-decarbonize (HtD) houses. Since many structures are not as eco-friendly as they could be, this AI model steps in as a valuable solution. With it, professionals can identify the ‘less green’ structures and cook up strategies to make them more environmentally friendly.

But first, let’s look at the urgency behind decarbonization, especially in construction.

Urgency of Decarbonization

The Built environment contributes to roughly 42% of the total global CO2 emissions. Construction operations like excavation, transportation, and heavy machinery operation burn fuels which contribute to CO2 emissions. We are already witnessing frequent disastrous natural calamities like wildfires and hurricanes owing to climate change. Back in July, the United Nations declared that we were entering the Global Boiling Era, ringing alarm bells throughout the world.

In response to the mounting climate crisis, the AEC industry must take a leadership role in the global decarbonization effort. It’s no longer a question of if, but how quickly and effectively we can transition to sustainable, carbon-neutral practices. This includes embracing innovative technologies, sustainable materials, and energy-efficient construction methods for our new buildings, but also for green retrofitting of existing structures.

The AI model, created by researchers Maoran Sun and his PhD supervisor Dr. Ronita Bardhan, promises to be a significant asset. Dr. Bardhan, who also leads Cambridge’s Sustainable Design Group says, “This is the first instance of AI being trained to recognize challenging-to-decarbonize buildings.”

AI Model to Identify Decarbonization Prospects

The researchers trained their AI model using data from Cambridge, UK. They used information from Energy Performance Certificates (EPCs), street view images, aerial view images, land surface temperature, and building stock. The model identified 700 HtD houses and 635 non-HtD houses using open-source data. It can also pinpoint heat loss areas like roofs and windows and determine a building’s age.

Dr. Bardhan says the model is currently 90% accurate and expects the number to improve as more data is added. Sun and Dr. Bardhan are already developing an advanced framework with additional data layers, including energy use, poverty levels, and thermal images of building facades, to enhance accuracy and provide more details.

She emphasizes that in the past, decarbonization decisions were often based on limited data, underscoring the complexities of the issue. But she’s hopeful that AI can change this since it can handle a lot of data and offer deeper insights, leading to smarter and more effective decarbonization policies in the future.

“We can now deal with far larger datasets. Moving forward with climate change, we need adaptation strategies based on evidence of the kind provided by our model. Even very simple street view photographs can offer a wealth of information without putting anyone at risk,” she says.

The researchers stress that making data more visible and accessible can help unite efforts to reach net-zero goals. Transparency and data-driven insights are pivotal in rallying collective action to address climate change.

This innovative technology has the potential to revolutionize our approach to identifying “hard-to-decarbonize” buildings. We can make more informed and data-driven decisions to make them greener, paving the way for a more sustainable and environmentally responsible future. As the urgency of decarbonization becomes increasingly evident, the integration of AI is a promising and essential tool to address the complex challenges we face.

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