Transfer Learning Approaches for Multi-Hour Ahead Prediction of Indoor Air Temperatures

抄録

This research examines the potentiality of transfer learning for buildings to predict indoor air temperatures at times ahead from 1 hour to 5 hours. The trained models of the source building when varying forecasting times from 1 to 5 hours have the root mean square error (RMSE) less than 1.