Forecasting Energy Cost Savings in Weather-Guided Heating Control
Heating in buildings accounts for a major part of the world’s energy consumption and CO2 emissions. Saving heating energy can help to slow down global warming.
Heating energy consumption can be reduced by accounting for local weather conditions. However often it is not clear to customers whether and when it will pay off to install a weather guided heating control system.
In collaboration with SEnerCon GmbH and funded by the Berlin Program for Sustainable Development we develop Machine Learning Methods to help customers to estimate the energy savings they could achieve with weather guided heating control.
Förderung:
EFRE:BENE
