Cornell Using Machine Learning to Aid in Energy Savings

by | Feb 7, 2019

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Researchers at Cornell University are using machine learning to predict weather and avoid wasting energy in occupied structures.

According to engineering.com, the model can reduce a building’s energy usage by up to 10%. Fengqi You, a professor of Energy Systems Engineering at Cornell and researcher on the project, is relying on the notion that if a building could be smart enough to predict weather conditions, it could make smarter adjustments to its heating and cooling systems. This would, in turn, allow building owners and operators to save on energy costs.

The model uses years’ worth of data on forecasts and actual weather conditions. The researchers combined that predictor with a mathematical model that considers building characteristics like the size and shape of rooms, the construction materials, the location of sensors and the position of windows.

According to futurity.org, the framework has potential applications in building control systems and irrigation control in agriculture. If effective, it could also lead to more efficient indoor environmental control in vertical farms and plant factories that are increasingly popular in large cities.

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