Machine Learning Shows Promise for Predicting Building Energy Use

An overhead view of a neighborhood, with blue lines and dots between houses suggesting connectivity.

A new machine learning approach developed through an international collaboration between Polytechnic University of Milan and Drexel University could help architects and urban planners better predict neighborhood energy consumption during early design stages.

The study, published in the journal Buildings, tested an artificial intelligence model that forecasts building energy use with 88% accuracy using just four key data points, a major improvement over current methods that require extensive inputs. The research team used the CatBoost machine learning model, which outperformed several other artificial intelligence approaches in their testing.

"This framework gives designers quick insights about energy impacts when decisions matter most," said co-author Simi Hoque, PhD, PE, professor of civil, architectural and environmental engineering at Drexel University. "We can now make energy-smart choices from the start of neighborhood projects."

The research team, led by Milan’s Andrea Giuseppe di Stefano, developed and validated their approach using data from over 22,865 buildings. The model analyzes building size, primary use, number of floors, and climate zone to generate energy predictions. These four factors were identified as the most influential through detailed statistical analysis of the dataset.

When tested on mixed-use buildings in New York City, the model's predictions differed from traditional energy modeling calculations by no more than 8.69% lower to 11.04% higher. This level of accuracy is particularly impressive given the minimal input data required.

The simplified approach addresses a key challenge in sustainable urban development - the need to consider energy efficiency before designs are finalized. Current modeling tools require detailed information about building materials, mechanical systems, and operational schedules that isn't typically available in early planning stages. By contrast, this new method needs only basic information that architects and planners typically have at the start of a project.

The model's effectiveness stems from its training on a comprehensive dataset combining information from both residential and commercial buildings. The researchers merged data from the Commercial Buildings Energy Consumption Survey and the Residential Energy Consumption Survey, creating a robust foundation for predictions across different building types.

This research marks the first phase of a larger framework to optimize neighborhood energy use. Future phases will analyze building shapes and evaluate district-level energy systems. The team plans to incorporate additional features such as solar exposure analysis and district heating potential in subsequent stages.

The work is particularly timely as cities face pressure to reduce carbon emissions from buildings, which account for about 40% of energy use in the European Union. With urban populations expected to double by 2050, the need for efficient energy planning tools becomes increasingly critical.

"We're giving designers practical tools to create more sustainable neighborhoods," Hoque said. "Making good energy choices early leads to better performing buildings."


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