1. Department of Building Physics, Yildiz Technical University, minekayalar@gmail.com Mine KAYALAR, Farhang TAHMASEBI, Ardeshir MAHDAVI EXPLORING THE INTERRELATIONSHIPS BETWEEN OCCUPANTS’ PRESENCE PATTERNS AND PLUG LOADS IN AN OFFICE BUILDING

2. Introduction  In Europe, office annual energy use varies from 100 to 1000 kWh.m -2.a -1, depending on location, construction type, indoor environmental control systems, use and type of equipment, etc.  Plug loads account for more than 20% of primary energy used in office buildings, and this ratio is stipulated to increase by 40% in the next 20 years.  The current state of knowledge (including both available information in standards and typical simulation input assumptions) with regard to the prevailing plug loads in office buildings may be characterised as not fully satisfactory.

3. Introduction Office layout u Location of the office

4. Overview SpaceOccupantsTotal installed power [W]Area [m²] Open plan office areaP1,P2,P3,P4,P588043 Single occupancy office 1P618019 Single occupancy office 2P79034 Single occupancy office 3P813017 OccupantInstalled equipment power [W] Person-related plug loads [W] Allocated area [m²]Area-related plug loads [W. m -2 ] P113019.18.62.2 P214027.78.53.3 P319047.96.9 P424071.66.910.4 P518029.312.12.4 P618036.119.01.9 P79014.034.10.4 P813015.717.00.9 Overview of the selected office areas with respective occupants, areas, and installed power Overview of person-related and area-related plug loads (annual averages, working days)

5. Research Questions 1.What is the degree of diversity amongst the occupants with regard to presence levels and plug loads? 2.Is there an overall relationship between an occupants' presence probability at his/her work station and his/her energy use for electrical equipment? 3.Is there a relationship between the installed equipment power and the annual energy used for electrical equipment? 4.What is the overall magnitude of annual person-related and area-related plug loads and to which extend are these values in agreement with recommendations in pertinent standards? 5.Can we establish predictive models to estimate an occupant's equipment- related electrical energy demand based on his/her presence probability at his/her workstation (and vice versa)?

6. Results 1.What is the degree of diversity amongst the occupants with regard to presence levels and plug loads?  The eight occupants' presence probability for a reference day representing one year's working days  The eight occupants' plug loads for a reference day representing one year's working days

7. Results  The correlation between occupant's presence probability (P7) and the plug load fraction  The correlation between presence probability and plug load fraction for eight occupants 2.Is there an overall relationship between an occupants' presence probability at his/her work station and his/her energy use for electrical equipment?

8. Results  The relationship between plug load fraction and presence probability for each of eight occupants 3.Is there a relationship between the installed equipment power and the annual energy used for electrical equipment?  The correlation between annual equipment energy use and installed power

9. Results  Excluding the maximum and minimum values, plug loads in the monitored office space was found to vary roughly between 16 and 48 W.person -1 (average = 22 W.person -1 ).  In area-related terms, this corresponds to a range of loads between 1 and 7 W.m -2 (mean value = 3 W.m -2 ).  ASHARAE 90.1-2013 Performance Rating Method suggests a receptacle power density of 8.1 W.m -2 for office buildings where the actual receptacle loads are not known. 4.What is the overall magnitude of annual person-related and area-related plug loads and to which extend are these values in agreement with recommendations in pertinent standards?

10. Results  Based on data of all occupants, the plug load fraction could be estimated as a function of the occupants' presence probability according to equation below: PLF = 0.53 × OPP + 0.09 PLF : The ratio of actual plug loads to the installed plug loads OPP: Occupants' presence probability 5.Can we establish predictive models to estimate an occupant's equipment- related electrical energy demand based on his/her presence probability at his/her workstation (and vice versa)?

11. Results Comparison of plug load model’s predictions with the monitored annual electrical energy use associated with plug loads for the years of 2013, 2014 and 2015 ModelRun PeriodValue [kWh]Relative Error [%] Measured20142289.7- Simplified model20141960.4-14.4 Measured20131978.0- Simplified model20131958.1 Measured20151801.5- Simplified model20151863.13.4

12. Conclusion  Knowledge about occupants’ presence and behaviour in buildings can yield better (simulation-based) estimations of energy use and improve the building design and operation process.  The results suggest that the observed loads in the selected office do not necessarily correspond to common assumptions in standards and simulation input data.  Moreover, patterns of user presence and plug load requirements differ significantly amongst individual office users.  The results point also to an interesting and potentially highly useful relationship between occupants' presence, their respective installed equipment power, and the resulting electrical energy use.

13. Thank you for your attention !