Machine learning lets old building use less heat
If the forecast calls for rain, you’ll probably pack an umbrella. If it calls for cold, you may bring your mittens. That same kind of preparation happens in buildings, where sophisticated heating and cooling systems adjust themselves based on the predicted weather.
But when the forecast is imperfect—as it often is—buildings can end up wasting energy, just as we may find ourselves wet, cold, or burdened with extra layers we don’t need.
The new approach 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 using Toboggan Lodge, a nearly 90-year-old building on the Cornell University campus.
“If the building itself could be ‘smart’ enough to know the weather conditions, or at least somehow understand a little bit more about the weather forecasting information, it could make better adjustments to automatically control its heating and cooling systems to save energy and make occupants more comfortable,” says Fengqi You, a professor in energy systems engineering and coauthor of a paper on the system in the Journal of Process Control. ..Read More..