EFFICIENT AND ROBUST TRAINING METHODOLOGY FOR INVERSE BUILDING MODELING
Jie Cai, James E. Braun
This paper expands on a previous approach for inverse building modeling that utilizes a simplified state-space approach. The goal of the current effort is to provide an efficient and robust parameter training methodology, to which several elements are added. Some seasonal effects, such as variation of window transmittance at different times of the year, are taken into consideration and captured during the training process. In addition, a mixed-mode training approach is developed that allows the use of a combination of data obtained when cooling or heating is occurring with the zone temperature under control at setpoint and when the zone temperature is floating during periods of no load. Different search algorithms were tested for learning a “nearly” global optimal model. A multi-start search method was found to be robust and provide good computational efficiency and accurate results. At the end of this paper, this training methodology is implemented for a single zone case study and some results are provided. Figure
- There are currently no refbacks.