Analysis of energy saving optimization of campus buildings based on energy simulation

Abstract

The energy consumption of campus buildings has specific characteristics, because of the concentrated distribution of people’s working time and locations that change in line with distinct seasonal features. The traditional energy system design and operation for campus buildings is only based on the constant room temperature, such as 25°C in summer and 18°C in winter in China, not taking into consideration the real heating or cooling load characteristics of campus buildings with different functions during the whole day and whole year, which usually results in a lot of energy waste. This paper proposes to set different set-point temperatures in different operation stages of public and residential campus buildings to reduce the heating and cooling design load for energy station and total campus energy consumption for annual operation. Taking a campus under construction in Tianjin, China as an example, two kinds of single building models were established as the typical public building and residential building models on the campus. Besides, the models were simulated at both set-point room temperature and constant room temperature respectively. The comparison of the simulation results showed that the single building energy saving method of the peak load clipping could be used for further analysis of the annual energy consumption of campus building groups. The results proved that the strategy of set-point temperature optimization could efficiently reduce the design load and energy consumption of campus building groups.

Keywords

campus buildings / set-point temperature / energy simulation / energy saving optimization

Introduction

Nowadays with the expansion of new campus construction of universities, campus energy consumption has become an important component in building energy consumption in China [1]. So the discussion of energy-saving optimization of campus buildings has a great significance because of the huge energy saving potential. Present researches on campus energy efficiency [2–4] mostly focus on energy retrofit of existing buildings or development of new energy, instead of on optimization of energy saving of campus building groups.

The energy consumption of campus building groups has specific characteristics compared with other ordinary building groups [5]. On the one hand, the distribution of the staff on campus is concentrated in time and space. In the daytime, most of teachers and students usually gather in public buildings like classrooms, libraries and offices, so the room temperature in these buildings should be increased in wither while the room temperature in residential buildings where there are not many teachers and students at that time should be decreased to some extent. On the contrary, when the students are staying in dormitories at night, the room temperature in residential buildings should be kept at the required standard while the room temperature in the classrooms, libraries and offices where there are not many teachers or students at that time should be adjusted to the duty temperature. On the other hand, the winter vacation in China usually lasts from late-January to late-February and the summer vacation from early-July to late-August, during which the conventional heating and cooling load occurred could lead to less energy consumption than the design condition. So both the total design load and the peak load of the annual energy consumption may be reduced because the design load of the energy station is determined by the adverse weather condition which usually appears in vacations. If taking these characteristics of energy consumption of campus buildings into account at the design stage, the total annual energy consumption of campus buildings can be decreased effectively, and thus the initial investments and operation costs of the energy station also can be saved to some extent [6,7].

This paper takes a new campus of Tianjin University which is under construction as an example to analyze the energy-saving optimization of campus buildings based on the specific energy consumption characteristics of campus building groups using energy consumption simulation software. The simulation software can perform an hour-to-hour dynamic energy consumption analysis, so the characteristics of the energy consumption of campus building groups can be represented preferably [8,9]. In view of the particularity of the energy consumption of campus building groups, an energy saving optimization mode has been proposed and analyzed with the help of energy simulation software “EnergyPlus” [10,11]. It is concluded that the energy saving optimization strategy based on the specific characteristics of the energy consumption of campus building groups can significantly reduce the total energy consumption and peak load.

Methodology

Combining the characteristics of energy consumption of campus buildings with dynamic energy consumption simulation, the optimization method for the total design load and annual energy consumption of the campus energy system was proposed.

The campus buildings were simplified as residential buildings and public buildings according to their functions. Therefore, the single residential building model and single public building model [12] were established based on the national design standard. Besides, the energy consumption simulation results of the single building models were compared with the calculation results of the traditional design method, and applied to the campus building group for further prediction analysis.

The conventional building heating or cooling load calculation method is based only on the constant design room temperature, such as 18°C in heating season and 25°C in cooling season during all day. However, the building load is time-varying, so, constant design temperature cannot fit for the varying load, which results in energy waste. Therefore, the set-point room temperature mode was proposed to optimize the design load and energy consumption for the campus energy system compared with the traditional constant room temperature mode. Xu et al. [13] proposed that intermittent heating and indoor design temperature improvement could obviously reduce the building energy consumption. Engdahl and Johansson discovered that the optimal control of the temperature of supply air could result in a significantly lower HVAC energy use than with a constant supply air temperature [14]. Based on this theory, the design load and energy consumption of two single building models in two room temperature modes were simulated to verify the optimization result. The simulation results were applied for the prediction of energy saving of the whole campus after testing the accuracy. Because the peak load of residential buildings and public buildings on campus appears at different times, based on the hourly simulation results, the peak load as well the energy consumption of the whole campus could be reduced. The comparison of the simulation results and the traditional calculation results indicated that the optimization was valid.

It is worth noticing that this paper is mainly focus on building load simulation, therefore, the term “energy consumption” only refers to the thermal consumption.

Establishment of single building model

Establishment of residential building model

Taking a dormitory building on the old campus of Tianjin University as the prototype, a residential building model was created to serve as the criterion for determination of the average energy consumption of residential buildings on the campus. The prototype building is a 6-storey building facing south without basement, whose construction area is 1840.32 m² with a height of 16.85 m. There is a patio in the middle section of the building which is used for day lighting and ventilation of some rooms. The thermal performance parameters of the building model envelop were selected according to China National Design Standard for Residential Buildings [15].

The simplified standard building model is shown in Fig. 1, while the settings of the space enclosing structure materials and thermal performance parameters are listed in Table 1.

Establishment of public building model

Taking a teaching building on the old campus of Tianjin University as the prototype, a public building model was created to serve as the criterion for determination of the average energy consumption of public buildings on the campus. The prototype building is a 10-storey building facing south with basement, whose construction area is 31204 m² with a height of 43.4 m. The thermal performance parameters of building model envelop were selected according to China National Design Standard for Public Buildings [16].

The simplified standard building model is illustrated in Fig. 2. The settings of the space enclosing structure materials and thermal performance parameters are presented in Table 2.

Settings of other parameters

The energy consumption simulation software “EnergyPlus” was used to analyze the energy saving optimization strategy based on the two single building models. The meteorological data of Tianjin in typical meteorological years [17] were used as input parameters for the simulation. Other input parameters are tabulated in Table 3.

Analysis of load and energy consumption of single building model at constant room temperature

The tradition design method usually calculates the heating or cooling load based on the constant indoor design temperature. According to the relevant Chinese building design standards [18], the indoor design temperature is 18 degrees during heating season and 25 degrees during cooling season.

Simulation results at constant room temperature

Simulation results of load and energy consumption of the residential building model

The residential building model was simulated at constant room temperature and the simulation results of the annual energy consumption were obtained, as demonstrated in Fig. 3.

It is observed form Fig. 3 that the annual heating energy consumption is 486858 MJ. The heating load is mainly concentrated at October 20 to April 10 next year, while the annual peak load is 68411 W, appearing at 3:00 am on January 6. It is, therefore, deduced that the heating load index of residential buildings is 37.08 W/m².

The annual cooling energy consumption is 305594 MJ. The cooling load is mainly concentrated at June 25 to September 15, while the annual peak load is 102755 W, appearing at 4:00 pm on July 5. It is, therefore, deduced that the cooling load index of residential buildings is 60.17 W/m².

Simulation results of load and energy consumption of the public building model

The public building model was simulated at constant room temperature and the simulation results of daily energy consumption were obtained, as displayed in Fig. 4.

It is seen from Fig. 4 that the total annual heating energy consumption is 9434740 MJ. The heating load is mainly concentrated at October 20 to April 10 next year, while the annual peak load is 1314887 W, appearing at 3: 00 am on January 6. It is, therefore, deduced that the heating load index of public buildings is 42.14 W/m².

The total annual cooling energy consumption is 6 174 211 MJ. The cooling load is mainly concentrated at June 25 to September 15, while the annual peak load is 2352130 W, appearing at 4:00 pm on July 5. It is, therefore, deduced that the cooling load index in public buildings is 81.85 W/m².

Calculation results of load using traditional method

To test the accuracy of simulation results, the heating and cooling load of two single building models were calculated using the traditional theoretical calculation method which involves building envelope basic heat consumption, infiltration heat loss, invasion heat loss respectively. The traditional theoretical cooling load calculation method involves cooling load of the building envelope, heat released by indoor heat and humidity source, outdoor air cooling load respectively [19].

The calculation made by using the traditional method shows that the heating and the cooling load of the residential building is 46.32 W/m² and 63.16 W/m², respectively, and the heating and the cooling load of the public building is 40.93 W/m² and 82.54 W/m², respectively.

Conclusion of load analysis at constant room temperature

To check the accuracy of the simulation results, load calculation was conducted using the traditional method. The summary and comparison of the calculation results using the two methods are given in Table 4. It can be seen that there exist relative errors in the acceptable range, which proves that the simulation results can be considered to be reliable and effective.

Analysis of load and energy consumption of single building model at set-point room temperature

Strategies for load reduction

The operation of the HVAC system should be based on the use of the buildings. The special characteristics of the campus buildings energy consumption are embodied by the relative centralized distributions of people’s working time and locations on the campus and the reduction of campus energy consumption in vacations as mentioned above. So, based on the characteristics of the energy consumption of campus buildings, when there are not many teachers or students in certain buildings, the proper decrease of the design room temperature in winter and increase of it in summer can effectively reduce the energy consumption of the HVAC system in buildings. As the national standard mentioned, the practical building operation condition should be taken into account when setting the indoor temperature in summer and winter. Therefore, the concept of set-point temperature mode is proposed, which means setting another indoor design temperature during the time when there are not many teachers or students in residential buildings or public buildings. In this way, the total energy consumption of heating and cooling can be reduced to some extent [20].

The specific setting values of set-point temperature and corresponding time are shown as Table 5. Since not many students stay at school in the winter and summer vacation, the indoor design temperature of residential buildings is set at 15°C and that of public buildings 5°C in the winter vacation, while the indoor design temperature of residential buildings is set at 28°C and that of public buildings 30°C in the summer vacation. At other times during the heating season, the indoor design temperature of residential buildings is set at different values with the movement of students at the university. As is presented in Table 5, the indoor design temperature of public building changes from 5°C (00:00 - 03:00) to 16.5°C (07:00 - 08:00) in the heating season. The gradual change of the design temperature may reduce the excessive load.

Simulation results of load and energy consumption at set-point temperature

According to the set-point temperature above, the energy consumption of the two single building models were simulated by “EnergyPlus.”

Simulation results of load and energy consumption of residential building model

The distribution of the hourly heating energy consumption, with the highest heating energy consumption—January 6, is depicted in Fig. 5. It can be seen that in the set-point room temperature mode, there is no significant change in the peak load. The reason for this is that the peak load in the constant temperature mode occurs at 3:00 am, instead of in the period of set-point temperature mode for residential buildings. So in the set-point temperature mode, the peak load which is 69651 W still appears at 3:00 am and the heat load index is 37.85 W/m².

The date when the highest annual cooling energy consumption appears moves from July 5 to June 30 due to the set-point temperature in the summer vacation. The distribution of the hourly energy consumption of cooling on that day is illustrated in Fig. 6. It can be seen that the time for the peak load moves from 4:00 pm to 2:00 pm, and the peak load changes from 102755 W to 76584 W, thus the deduced cooling load index is 54.71 W/m².

In conclusion, the annual heating energy consumption and cooling energy consumption of the single residential building model is 426339 MJ and 166540 MJ respectively.

Simulation results of load and energy consumption of public building model

The distribution of the hourly heating energy consumption, with the highest heating energy consumption—January 6, is exhibited in Fig. 7. It can be seen that the peak load in constant temperature mode occurs at 3:00 am, just in the period of set-point temperature for public buildings. So in the set-point temperature mode the peak load appearing at 9:00 am changes to 1407132 W. And the deduced heat load index also changes to 45.09 W/m².

The date of the highest annual cooling energy consumption moves from July 5 to June 30 due to the set-point temperature in the summer vacation. The distribution of the hourly cooling energy consumption on that day is shown in Fig. 8. It can be seen that there is no significant change in the peak cooling load. This is because the peak load in constant temperature mode occurs at 4:00 pm, instead of in the period of set-point temperature mode for public buildings. So in the set-point temperature mode, the peak load which is 1641261 W, still appearing at 16:00 pm, and the heat load index is 75.52 W/m².

In conclusion, the annual heating energy consumption and cooling energy consumption of single public building model is 5362360 MJ and 3440570 MJ respectively.

Contrast and analysis of simulation results of single building model in the two modes

Contrast and analysis of load simulation

Load index simulation results at constant room temperature (Mode-C) and set-point room temperature (Mode-S) are shown respectively in Table 6. The distribution of the annual heating and cooling energy consumption of residential and public buildings are shown in Figs. 9 and 10.

It can be seen that for both the residential building and the public building, the annual heating and cooling energy consumption are significantly reduced in set-point room temperature. Furthermore, the cooling load index of Mode-S decreases significantly compared with that of Mode-C, but the heating load index is even slightly higher than that in Mode-C. This is because in winter, the indoor temperature of public buildings is set at 5°C at night, while in the daytime the indoor temperature is set at 18°C, which presents such a large temperature gradient that it takes more energy to raise the temperature from 5°C to 18°C in unit time, making the heating load index of public buildings in Mode-S slightly larger than that in Mode-C. And that also accounts for the fact that the set-point temperature of the public buildings in Table 5 in the heating season increases hourly, just in order to avoid the excessive load caused by the large temperature gradient. The same is true of residential buildings, but the gap between the heat load indices in the two modes is smaller than that of public buildings. The reason for this is that the temperature gradient of residential buildings caused by the set-point temperature is lower than that of public buildings. Even though the load increases to some extent, the annual energy consumption decreases to a great degree.

Contrast and analysis of energy consumption simulation

Contrast of energy consumption of residential building

As is shown in Fig. 9, the annual heating energy consumption of the residential building is 426339 MJ in Mode-S and 486858 MJ in Mode-C respectively. The annual cooling energy consumption is 166540 MJ in Mode-S and 305594 MJ in Mode-C respectively. It is evident that the monthly heating energy consumption in Mode-S is lower than that in Mode-C, especially in January and February, and as well as monthly cooling energy consumption, especially in July and August; thus it could be concluded that the indoor design temperature adjustment can definitely reduce the annual energy consumption of the residential building.

Contrast of energy consumption of public building

As is shown in Fig. 10, the annual heating energy consumption of the public building is 5362360 MJ in Mode-S and 9434737 MJ in Mode-C respectively. The annual cooling energy consumption is 3440570 MJ in Mode-S and 6174211 MJ in Mode-C respectively. It is apparent that the monthly heating energy consumption in Mode-S is lower than that in Mode-C, especially in January and February, and as well as monthly cooling energy consumption, especially in July and August; thus it could be concluded that the indoor design temperature adjustment can definitely reduce annual energy consumption of the public building.

Summary and analysis of annual energy consumption

The simulation results are summarized in Table 7. It can be seen that the annual heating and cooling energy consumption of the residential building is reduced by 12.4% and 45.5% respectively, and the annual heating energy consumption and cooling energy consumption of public buildings is reduced by 43.1% and 44.3% respectively. The reason for the less reduction of total annual heating energy consumption in residential buildings than public buildings is that some of the dormitories still need heating in the winter vacation, so the set-point temperature shouldn’t be set at a very low degree, and therefore, the energy-saving effect is not as obvious as that of public buildings.

Analysis of load and energy consumption of campus building groups

Prediction of load and energy consumption of campus building groups based on energy saving optimization method

Optimization method

According to the simulation results in Figs. 5 - 8, the peak loads of two kinds of buildings appear at different times. The simulation results of the single building model at set-point temperature can be applied to campus building groups that include both public and residential buildings to obtain the total load and energy consumption [21]. By adding the load hour by hour, the load and energy consumption of campus building groups can be obtained.

Prediction of total load in optimization method

The new campus is located in Tianjin in North China. The total building area of the public buildings is 495430 m² for heating and 214700 m² for cooling. And the total building area of residential buildings for heating is 255025 m². Since there is no central cooling system in the residential buildings, the cooling load of residential buildings can be ignored for energy station design. Based on these area data and simulation results of hourly heating and cooling load index in unit area of the two single models by the optimization strategy, the peak load of residential and public buildings were obtained. It is known that the heating load and cooling load of all residential buildings are 9653707 W and 0, and the heating load and cooling load of all public buildings are 22341226 W and 16213330 W. Then, the peak load of the whole campus building groups can also be calculated by adding the load of residential buildings and that of public buildings respectively, as shown in Table 5. It should be noticed that the heating load of the building groups is not equal to the sum of two kinds of buildings, which was explained in detail in 6.3.

Prediction of annual energy consumption in optimization method

Similar to the load calculation, the annual energy consumption of campus building groups in optimization method were calculated based on campus area and the simulation results of hourly heating and cooling energy consumption in unit area of the two single models. The results show that the annual heating energy consumption is 138043165 MJ and the annual cooling energy consumption is 23672917 MJ, whose distribution is shown in Fig. 11.

Prediction of total load based on traditional calculation method

According to the heating and cooling area and the load index calculated using traditional method, multiplied by the heating and cooling area, the load of the campus building groups were obtained. That the heating load and the cooling load of building groups is 32090708 W and 17721338 W.

Contrast and analysis of the results of load prediction

The results of load prediction above are summarized in Table 8. It can be seen that both the cooling load and the heating load of campus building groups when using the optimization method are obviously less than that when using the traditional method. This is because the daily peak load of different types of campus buildings appears at different times. The specific hourly heating load on January 6 (the day the annual peak load appears) is shown in Fig. 12. It is observed from Fig. 12 that the peak load of residential buildings (9653707 W) appears at 3:00 am and that of public buildings (22341226 W) appears at 9:00 am.

By adding the heating load of all the buildings, the peak load of campus groups is found to appear at 8:00 am (31368771 W), neither 3:00 am nor 9: 00am, which is less than the sum of two buildings in their peak time (22341226 W+ 9653707 W= 31994933 W). In addition, since there is no central cooling system in the residential buildings on the campus, the peak clipping of total cooling load cannot be obtained.

Contrast and analysis of the results of annual energy consumption prediction

By adding the annual energy consumption of residential and public buildings, the annual energy consumption of the whole campus building groups was obtained. The comparison of the heating energy consumption of two modes (which is 138043165 MJ and 206390476 MJ respectively) shows that the optimization method can save 33.12% of energy. The comparison of the cooling energy consumption of two modes (which is 2327291 MJ and 42484830 MJ respectively) shows that the optimization method can save 45.22% of energy. The distribution of the total energy consumption of building groups is shown in Fig. 13.

As it can be seen that the annual heating and cooling energy consumption using the optimization method is obviously lower than the values without optimization, which proves that the optimization strategy is effective to a large extent.

Conclusions

The idea of energy-saving optimization for campus building groups has been proposed based on the energy consumption characteristics of campus buildings, and the feasibility of the idea has been verified through specific case analysis, which creates a new research view on the energy-saving strategy of campus building groups.

Based on the energy consumption characteristics of campus buildings, the set-point temperature mode was proposed. Compared with the traditional theoretical method to calculate the load, the dynamic simulation method is the foundation to realize the optimization strategy under the help of energy simulation software “EnergyPlus.” The comparison indicates that applying the optimization strategy can reduce the annual energy consumption of campus building groups and save the operation cost of the HVAC systems. In respect of load peak clipping, the peak load of two types of building groups occurs at different times, by which the peak load clipping can be achieved, and the total campus designed load can be reduced to save the initial investment.

The optimization strategies are based on the characteristics of the energy consumption of campus buildings, which is common on university campuses regardless of the type of the university, the number of the students, and the region where the university is located. Thus the optimization strategy can be widely used in campus energy planning. With this optimization strategy, the initial investment and operation cost of heating and cooling systems can be reduced effectively.

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