Estimating morning ramp-up duration for the cooling season in a smart building using machine learning: Determining most promising features

The nighttime setback approaches commonly consider fixed morning schedules regardless of the rooms’ thermal behavior, resulting in rooms being conditioned before occupants’ arrival. The present work is focused on developing machine learning-based pipelines for estimating the ramp-up duration in different indoor spaces, which is the time the HVAC system needs to bring the space temperature to the desired setpoint. A medical center located in northern Italy, with an HVAC system monitored during the cooling season, is employed as the case study, where the ramp-up procedure was experimentally deployed and investigated under various climatic conditions. Accordingly, machine learning-based pipelines with six different algorithms for estimating the ramp-up of each space are developed, permitting just-in-time start-up of the fan coil units for each day. Next, the most influential features are specified for each room's best-performing model, aiming for performance improvement and computational cost reduction. The obtained results indicated an average Mean Absolute Error of less than 5 min for all cases. Finally, the predicted ramp-ups are compared with the existing fixed schedule, where significant saving windows of up to 80 min are achieved.