To create the monthly thermal energy benchmarks (MTEBs), the actual thermal consumption data and the operational hours of the heating systems of 52 buildings in four university campuses (Trinity College Dublin, University College Dublin, Dublin City University, Dublin Institute of Technology) were analyzed. The actual energy consumption data were obtained from the Cylon Active Energy Management online dataset [
39]. The heating degree day data were collected from Degree Days.net [
40]. To discover the mixed activities in the case study buildings, a survey was conducted at the floor scale. According to the assessment of energy consumption of 52 UC buildings, five key parameters that affect the thermal energy demand were found to be:
Two models were developed to generate the MTEBs: (1) mixed-use model and (2) converter model. The mixed-use model relies upon the impact of all activities in a building on thermal consumption. Accordingly, a composite benchmark that considers the role of mixed activities in terms of thermal energy demand was progressed. The converter model, developed based on the annual thermal consumption, presenting on DECs. The accuracy of both models was validated against the actual thermal consumption.
To assess the impact of various activities on thermal demand, the area of all the activities of the case study buildings was surveyed, and then the area of each activity calculated in AutoCAD precisely. Ten activities were identified in 52 analyzed college buildings, while among them, 7 activities were common in all cases. Based on the analysis, a typical college building in terms of mixed activities is defined for the first time: a typical college building is a type of educational building, comprising seven typical mixed activities, including computer rooms and laboratories (31%), offices (29%), seminar and research rooms (18%), library (14%), workshop (4%), stores (3%), and restaurant or coffee shop (1%).
The energy demand estimation based on TM46 UC benchmark against the actual consumption data of ”Aras An Phiarsaigh” building at the Trinity College Dublin (TCD) campus was analyzed as a sample and the results, as well as the estimation of the mixed-use model, are presented in
Figure 3. Both estimations were assessed against the actual data. Lines (a) and (M) show the mean annual estimations of TM46 UC benchmark (240 kWh/m
2/yr) and the mixed-use model, respectively, while line (b) presents the mean of annual actual data.
Considering the Aras An Phiarsaigh building as an example, the differences between thermal demand estimations of TM46 (mean annual) and the mixed-use model with the actual consumption were 68% and 45%, respectively (
Figure 3). The result shows the mixed-use model improved the thermal demand estimation, approximately 42% compared with TM46. Coefficient (n) was defined to improve the accuracy of the mixed-use model as the ratio of the composite benchmark to the TM46 UC benchmark (240 kWh/m
2/yr). Coefficient (n) reduced the errors of the mixed-use model to 6%. At this stage, the mixed-use model presents an annual-fixed estimation (line M); however, the aim is to convert this horizontal line into monthly figures. To generate the monthly thermal benchmarks, two models were improved using further drivers. Additional information about the generation of the models is presented in
Section 2.1 and
Section 2.2.
2.1. Mixed-Use Model
The mixed-use methodology is applicable to existing buildings and buildings at the construction stage. The method relies on CIBSE TM46 benchmarks, including 29 building categories, especially those categories found mostly in a typical college building such as “general office”, “restaurant”, “cultural activities”, “classrooms”, and “general retail”. Based on the analysis, most of the college buildings comprise seven typical activities, i.e., mixed-use functions. In fact, activity plays a key role in thermal demand; for example, a general office needs 120 kWh/m
2/yr of thermal energy while a restaurant needs 370 kWh/m
2/yr [
4,
14].
Using Equation (1) and the architectural maps, the quantity of thermal demand of a mixed-use college building can be calculated. By dividing the annual thermal demand by 12 (Equation (2)) the mean monthly thermal demand can also be calculated. To calculate the composite benchmark, Equation (1) is divided by the total useful floors area (
TUFA) of the buildings; therefore, Equation (3) indicates how to calculate a composite benchmark. The mixed-use method to estimate the annual thermal demand follows:
where (
fi) is the
CIBSE TM46 benchmark of activity (
i), (
Ai) is the relevant area of activity (
i), and
A (m
2) is the total useful floor area of the building.
To indicate how the mixed-use method was developed, further discussion is presented in the following sections. As a sample, the model was applied in the Aras An Phiarsaigh building. The energy benchmarks of various activities are presented in
Table 2. For example, the energy benchmark of a library is 200 kWh/m
2/yr while the benchmark of a laboratory is 160 kWh/m
2/yr. The weight of each benchmark is normalized based on its area in the building. The other necessary data to run the model are presented in
Table 2.
The annual thermal demand estimation using the mixed-use model equals:
The assessments demonstrated that by considering the role of mixed activities (Equation (4)) in a building, the accuracy of thermal demand estimation can be improved. Comparing the results of estimations with the actual records proved this progress.
To develop the annual model into a monthly model, a series of other drivers were taken into account. One of the important factors is the heating degree days (HDD). The HDD is sensitive to the outdoor conditions. The weather data of Dublin Airport, IE (6.30° W, 53.42° N) was applied in the calculations and the base temperature of 15.5 °C chosen to determine the HDDs. In
Table 3, the HDD data of 2014 are reported.
Through multiplying Equations (1) and (2) by the result obtained from the division of the monthly
HDD by annual
HDD (
), Equation (5) was created. Then, using Equation (5), the primary version of the monthly thermal models was generated. The primary model was applied in 10 buildings and its accuracy was calibrated using the actual thermal measurements; nevertheless, the Aras An Phiarsaigh building is discussed in detail.
where (
fi) is the
CIBSE TM46 benchmark of activity (
i), (
Ai) is the relevant area of activity (
i),
A (m
2) is the total useful floor area of a building, and the
HDD is the heating degree days at both annual and monthly scale.
The analysis showed there were significant differences between the estimations of the primary version (Equation (5)) of the model and the actual monthly consumption data. The differences, especially in the summer season, were notable. The reason for the lower accuracy of the primary version of the model refers to the local energy efficiency policies in universities. For example, it was found that despite heating degree days, which shows the thermal demand even during summer in Dublin (
Table 3), the Estates and Facilities Office at TCD turns off the heating systems during summer. This policy drastically reduced the actual thermal consumption during the summer at TCD. Therefore, another factor, i.e., typical operation hours of heating systems, was taken into account and multiplied by Equation (5) to create Equation (6). In public buildings such as colleges, the operation hours of heating systems are not affected by occupant behavior, but controlled by energy managers at universities.
where (
fi) is the
CIBSE TM46 benchmark of activity (
i), (
Ai) is the relevant area of activity (
i),
A (m
2) is the total useful floor area of a building, and
HDD is heating degree day at both annual and monthly scale.
The mean absolute percentage error (MAPE) on a monthly scale evaluated the accuracy of the final mixed-use model (Equation (6)). Besides, the accuracy of the model was calibrated by R-squared value, which indicates the error between the modeled values and the recorded values. The model applied to the other case study buildings. In all of the analyzed buildings, the maximum MAPE at the monthly level was under 21%, whereas it was 18% at the annual level. Compared with the best result (22%) of other annual estimation models [
14], the result is acceptable.
2.2. Converter Model
Display energy certificates (DECs) present annual thermal consumption. If DEC documents are available, the converter model is more user-friendly compared to the mixed-use method to convert the annual heat demand into the monthly profiles. Normally the TPFER (
Figure 1) is presented on DECs in kWh/m
2yr. To create a monthly thermal energy model using TPFER,
HDD and the operation hours of heating systems play a key role. Equation (7) shows the final version of the converter model:
where
A (m
2) is the total useful floor area of the building and
HDD is heating degree day at both annual and monthly scale.
The maximum unit interval of 20%, presented by the coefficient (m) in which m ∈ [0.80, 1] was considered in the model and refers to the difference between TPFER and TFC. This difference was also shown by other scholars [
22]. To increase the accuracy of simulations this difference was considered. Using the converter model, the annual thermal demand of a typical college building can be converted into the monthly figures. To understand how both mixed-use and converter models can be applied in practice, a flowchart is presented in
Appendix A.