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The method developed in this research is implemented on one of the campus buildings at the University of Pennsylvania—the Towne building, which is designed in the manner of the English classicism of the seventeenth century. The building has 4 floors (with one basement floor) and is mainly composed of classroom and offices. The total floor area of the building is about 13000 m2_.

The simulation input for the building is collected in a text based file format including building geometric information, operation schedule, building systems, building envelopes, and etc., and then being fed to SimBldPy simulation tool. We adopted a classic “perimeter-core” modeling method to model this building. For each floor, including the basement floor, a core and a perimeter zone are modeled to make the SimBldPy model stay simple. The building envelopes are also modeled for each zone. The brief information on the building envelope is reported in Table 17.

Table 17 Towne building envelope in each orientation

Orientation Opaque (m2_{) Window (m}2_{) Below Grade Opaque (m}2₎

S 1787.6 406.8 259.0 SE 0.0 0.0 0.0 E 948.4 187.3 155.7 NE 0.0 0.0 0.0 N 1127.9 256.9 218.2 NW 0.0 0.0 0.0 W 1028.9 246.5 147.3 SW 0.0 0.0 0.0 Roof 3995.9 0.0 0.0

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The model is calibrated with its actual energy performance in 2015 by metered hourly and monthly energy use data, which are stored and maintained by Penn Facilities and Real Estate Services (FRES). The heating and cooling set point of the building is constant, which is 21.8 for cooling and 22.5 for heating, respectively. In Table 19, the operation schedule of Towne is calibrated based on its electricity use pattern because cooling and heating energy use are

separated from electricity use. The building wall section consists of outside air film, face brick, air cavity, CMU (concrete masonry unit), air cavity, veneer plaster, and inside air film. The calibrated thermophysical properties of the building envelope used in building simulation are shown in Table 18. All the campus building uses district cooling and heating source in the form of chilled water and steam. Thus, the building simulation model is calibrated with the metered chilled water and steam usage so as to get prepared for the following retrofit optimization and ensure the optimization have practical significance. The simulation results of the calibrated building model are shown in Fig. 26. The dotted line is the simulated result and the solid line is the actual use. Possible reasons for the deviation from the actual and simulated energy use of the building may be the real time use schedule, local microclimate condition, HVAC system control strategy, system fault, impact of occupant behavior, and etc.

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Table 18 Calibrated thermo physical properties of building envelopes

Envelope U-value (W/m2 °C) Absorption coefficient (SHGC for window)

Wall 1.1 0.8

Roof 0.92 0.78

Below-grade 2.95 0.81

Window 4.16 0.69

Table 19 Towne building operation schedule

time of day wd_occ we_occ wd_app we_app wd_light we_light

From 0 To 7 0 0 0.72 0.51 0.72 0.51 From 7 To 8 0 0 0.73 0.55 0.73 0.55 From 8 To 9 0.3 0.4 0.78 0.58 0.78 0.58 From 9 To 16 0.5 0.5 0.82 0.62 0.82 0.62 From 16 To 17 0.95 0.5 0.82 0.62 0.82 0.62 From 17 To 18 0.7 0.5 0.83 0.63 0.83 0.63 From 18 To 22 0.95 0.5 0.84 0.64 0.84 0.64 From 22 To 24 0.7 0.3 0.75 0.65 0.75 0.65

winter week hourly heating summer week hourly cooling monthly heating & cooling Fig. 26 Building model calibration

In order to show how the simulation results of SimBldPy model compare to the metered data, the scatter plot depicting outdoor air temperature versus cooling and heating energy use of meters, SimBldPy model and a data-driven RF model are drawn respectively in Fig. 27. The plot

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shows the “signature” of the building performance in terms of heating and cooling energy use. The RF model is trained on the real measured weather data in 2015 and the occupancy schedule used in SimBldPy model. The original purpose for training this RF is to provide a self-

benchmarking tool that examines how the building is performing in the years after 2015.

Cooling Energy Heating Energy

Meter Data SimBldPy Random Forest Model

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Fig. 27 “Signature” plot of cooling and heating energy from meters, SimBldPy model, and RF model for Towne Building

The difference between SimBldPy model and the metered data is mainly due to the fact that the simplified hourly model assumes that the building’s heating and cooling system is

performing ideally just as described in section 4.1, while in practice, in a building like Towne, the HVAC system may suffer from degradation and ageing, which can possibly result in overheating or overcooling due to system faults or failure. Thus, the actual operation state can deviate from what the system is supposed to perform. Other reasons for the difference between the model and metered data could be attributed to the difference in actual occupancy and use, as well as the actual operation and management of the building system. It is should be noted that the “elbow” in the plots, where a watershed of different slopes of temperature versus energy use occur, reflects the different operation mode of the building system concerning heating and cooling. It is generally at that point that the building switches its system operation between cooling and heating. These points are also called “change point” (Paulus, Claridge, and Culp 2015).

The downscaled future hourly weather data is also an important input for the optimization model and is obtained by using the morphing method described in (Shen and Lior 2016). Fig. 28 and Fig. 29 show the trends of monthly mean dry bulb temperature and downwelling shortwave radiation from the year of 2017 to 2069 under different RCPs scenarios, respectively. In the following study concerning building retrofit and its optimization, RCP6.0 scenario will be used as the future climate scenario. The full set of downscaled climatic variables includes dry bulb air temperature, relative humidity, solar irradiation, and wind speed.

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Fig. 28 Monthly mean air temperature, daily maximum and minimum temperature in different RCPs and TMY in Philadelphia

Fig. 29 Monthly mean downwelling shortwave radiation in different RCPs and TMY in Philadelphia