Abstract
The primary objective of this research is to maximize the building energy efficiency by identifying the zone-based conditions and employing an advanced energy simulation model. It is possible to achieve this by reducing periods of excessive heating and cooling by utilizing a calibrated energy model with 15-minute measured data. Further, by regulating the energy consumption of various functional zones corresponding to the work schedules, building's energy system timetables, and the sensible temperature comfort requirements, additional energy savings can be achieved. To show the effect of dynamic simulation on building energy consumption, periodic 15-min temperature data were collected in different zones to be used both in the calibration of the energy model and in the improvement of the current energy profile. Mean bias error and cumulative variation of the root mean squared error were chosen as a performance indicator. Utilizing measured data, over-heated and over-cooled periods were defined by manually identifying zone-based indoor comfort conditions to predict improvements in overall building energy performance. The potential energy savings that can be achieved by largely eliminating over-heating periods are calculated. In addition, energy needs of different zones were considered and zone-based scheduling and zone-based comfort conditions were applied with the implementation of demand-side management. As a result, 17% energy efficiency can be achieved with an automated heating system that controls the indoor temperatures and ensures that the temperature is always kept at the desired level. As a further improvement, 32% energy efficiency can be achieved by applying zone-based scheduling and comfort conditions.
