مدل پیش بینی بار خنک کننده با در نظر گرفتن زمان وقوع پیشگیرانه در ساختمان ها

As interest in automation technologies for maintaining occupants' comfort and reducing building energy consumption increases, Energy Management Systems (EMS) and Building Energy Management Services (BEMS) are gradually receiving more attention. However, the conventional methods associated with these systems cannot predict the future thermal load based on real time thermal load and climatic factors. This study shows that the external environment and building thermal load may not be the same due to the time-lag phenomenon. Consequently, prediction models that take the time-lag phenomenon into consideration are developed. In order to create the prediction models, meteorological data (mixed humidity) over 4 years (2011–2014) in Seoul were consolidated using the collected data by KMA (Korea Meteorological Administration) and mathematical equations used in energy analysis simulations. A cooling load prediction model per building size considering the time-lag phenomenon was proposed based on multiple regression analysis. It was found that there are some cases where energy could not be predicted as there is a different time-lag due to architectural characteristics and building thermal load conditions.