Florida Tech study suggests machine learning can reduce environmental impact

Machine Learning

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More than 40% of all US energy use and greenhouse gas emissions are related to the building sector. A study by researchers at the Florida Institute of Technology is investigating whether machine learning can help reduce this environmental impact. (Florida Tech image)

BREVARD COUNTY • Melbourne, Florida – More than 40% of all energy use and greenhouse gas emissions in the US are related to the building sector. A study by researchers at the Florida Institute of Technology is investigating whether machine learning can help reduce this environmental impact.

This work was featured in the paper “A New Approach to Optimizing Building Energy Models Using Machine Learning Algorithms,” published in the January issue of the journal Energies.

Authored by Hamidreza Najafi, an associate professor of mechanical engineering at the Florida Institute of Technology, and Benjamin Kubwimana, a master’s degree in mechanical engineering, the study explores new approaches to both building energy modeling (BEM) and building energy model optimization. I’m using.

Current practice for building energy simulation software tools requires manual entry of a vast list of detailed inputs. This includes building characteristics such as walls, building skin and window materials, as well as set temperatures for different heat zones.

“When it comes to optimizing BEM, it is very difficult because of the large number of variables involved in BEM and the potential to develop thousands or millions of BEMs with different combinations of these variables,” Najafi said. said.

“Achieving a truly optimal design for a building requires evaluating all of these possible design/operational parameters, which is very computationally expensive and often impossible.”

Najafi and Kubwimana’s work includes developing software scripts in the Python language that enable automated data entry into a physics-based building energy simulation tool called EnergyPlus.

Using a set of variables as input via this Python script covers large variations in multiple parameters and creates a large data set that can be used to develop a surrogate energy simulation model. Machine learning algorithms, especially data-driven models using artificial neural networks, are trained using these datasets.

Two optimization approaches, genetic algorithm and Bayesian optimization, are applied to surrogate models to achieve optimal building design. This approach can be easily adjusted for different design or operational parameters.

“This process can be automated, so data from sensors in the building can be fed into the computer model to continuously adapt the digital twin to the current operational state of the building,” says Najafi. .

“This helps building owners forecast the amount of energy they will consume based on changes that may occur in operating parameters. can be predicted and the reduction of CO2 emissions associated with energy savings can be properly planned.”

This research was part of a broader effort to improve BEM and expand its applications. An enhanced BEM can be used as a digital twin of a building, providing value to owners and developers not only before construction, but also during the building’s lifetime.

This research was published in the ASME Journal of Engineering for Sustainable Buildings and Cities in December 2022.

“In this study, one of my PhDs, student Mariana Migliori explored the impact of COVID-19 on the energy performance of buildings and how BEM can be used to maintain accuracy when operational conditions change. We researched ways to adapt,” Najafi said.

“We were able to run a case study based on the data we collected from the Follard Alumni Center at the Florida Institute of Technology and the physics-based model we had previously developed for our building, developing a data-driven model that could adapt to the new environment. Operating conditions with extended HVAC system operating hours and changes to occupancy schedules given the COVID-19 situation.”

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