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."