Sophisticated heating and cooling systems in Buildings adjust themselves based on the predicted weather. But when the forecast is imperfect – as it often is – buildings can end up wasting energy.
A new approach developed by Cornell Researchers predicts the accuracy of the weather forecast using a machine learning model trained with years’ worth of data on forecasts and actual weather conditions. The Researchers combined that predictor with a mathematical model that considers building characteristics including the size and shape of rooms, the construction materials, the location of sensors and the position of windows.
The result is a smart control system that can reduce energy usage by up to 10 percent, according to a case study the research team conducted on Toboggan Lodge, a nearly 90-year-old building on Cornell’s campus.
Combining the machine learning algorithms and the mathematical programming methods creates a control system that’s more accurate and “smarter” than either of them would be on its own. The framework has potential applications in building control systems and irrigation control in agriculture, and could be used for more efficient indoor environmental control in vertical farms and plant factories that are increasingly popular in large cities.
News Source: Cornell