Study on the Energy Consumption Characteristics and the Self-Sufficiency Rate of Rooftop Photovoltaic of University Campus Buildings

Abstract

Campus buildings often face issues with high energy consumption, low efficiency, and significant carbon emissions, making the creation of a green, low-carbon campus urgent. Utilizing solar photovoltaics on rooftops can provide an effective power solution to address high energy consumption. This study focuses on a university campus, employing the DeST energy consumption simulation software to model the HVAC systems, electrical devices, and hot water loads of five typical buildings. It combines this with calculations of available rooftop areas to assess the potential for rooftop solar photovoltaics. The results confirm varying annual electricity consumption among the different buildings, which directly correlates with building size and operational schedules. Among the five building types, sports facilities and academic buildings have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest, under 20%. The entire university campus has an annual rooftop photovoltaic self-sufficiency rate of 35%, significantly addressing the issue of high energy consumption in university campuses. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings.

1. Introduction

Building energy consumption accounts for a significant portion of total societal energy usage, with school buildings being an essential component of this. Research data indicate that university buildings account for up to 30% of a city’s total energy consumption [1]. In China, data released by the Ministry of Education of the People’s Republic of China at the end of 2023 show that there are over 3000 higher education institutions, with more than 40 million university students [2]. University campuses, characterized by high building and population densities, are major energy consumers. However, energy management in universities has not yet developed into a standardized system for collecting and analyzing energy data, and the management remains relatively extensive, leading to significant waste [3,4]. This indicates that there is substantial potential for energy savings in campus buildings. Understanding the spatial and temporal distribution of campus building energy consumption is crucial for reducing energy waste and lowering building energy consumption.

Current research on campus building energy consumption is extensive [5,6,7], primarily relying on field surveys and numerical simulations to gather data. Andreas et al. [8] conducted an energy consumption analysis of building clusters in 68 university campuses across countries like Luxembourg, summarizing the relationship between campus building functions and building energy standards. Chung et al. conducted an energy efficiency survey on building groups within traditional Korean campuses, discovering that retrofitting existing buildings could achieve energy savings ranging from 6% to 30% [9]. Predicting campus building energy consumption can also aid in reducing energy usage.

With the continuous advancement of artificial intelligence algorithms, an increasing number of improved methods are being applied to the prediction of campus building energy consumption [10,11,12]. Mohsen et al. [13] proposed an optimized algorithm using intelligent weights, combining EMD with intelligent algorithms to study the load prediction of campus buildings. Guan et al. [14] analyzed real-time data on electricity and heating demand and the load usage characteristics of Norwegian university campuses using clustering methods to assess contributions and identify optimization potential for buildings. Clearly, understanding the characteristics of campus building energy consumption is a prerequisite for reducing energy usage.

There are numerous methods to reduce energy consumption in campus buildings, such as effective energy management, behavioral energy savings, and integrating renewable energy sources [15,16,17]. Shafiullah et al. [18] proposed a scenario-based mixed-integer linear programming model for an energy management system in a local energy community, using a medical college in the Netherlands as a case study. This improved energy management can activate energy converters such as combined heat and power systems and heat pumps, reducing electricity imports from the grid and thereby lowering overall costs and carbon emissions.

Zhang et al. [19] introduced a new predictive method based on occupant behavior and energy consumption. Yuan et al. [20] investigated the air conditioning electricity consumption and user behavior in 21 office spaces within a university campus, performing a statistical cluster analysis on the duration, frequency, and electricity consumption associated with air conditioning use. They found that varying occupancy densities can lead to significant differences in air conditioning electricity consumption. Panicker et al. [21] focused on residential buildings within a campus, designing and assessing the feasibility of integrating grid-connected rooftop and facade photovoltaic systems to meet the energy needs of academic campus residential buildings. They discovered that integrating photovoltaics in low-rise residential buildings can increase the system’s energy production by up to 62.5%.

Indeed, the integration of renewable energy can significantly reduce energy consumption in university buildings. Considering the relatively low-rise nature of campus buildings and the availability of unused rooftops, along with the semi-autonomous nature of campus energy systems, university buildings are well-suited to harness solar energy resources, offering both potential and advantages for its application. Current research on energy systems, represented predominantly by photovoltaic power generation, is also extensive within campus settings. For instance, Mahmud et al. [22] developed an optimal energy management strategy that integrates demand response and solar rooftop systems using an improved evolutionary particle swarm optimization at two Malaysian campuses equipped with NEM solar photovoltaic rooftop systems. This strategy not only reduces campus energy consumption but also aids energy users in managing loads and solar power generation, through the formulation of effective compensation schemes.

The studies mentioned previously address energy supply and management planning issues to a certain extent, both from the perspective of individual buildings and from the broader perspective of campus regional architecture. However, there is a noticeable gap in the comparative analyses of energy consumption and supply among different types of buildings, particularly in terms of the interconnections between energy consumption and supply within various buildings in a campus region. This study focuses on a specific university campus and conducts research from two perspectives: building simulation and solar energy utilization assessment. The goal is to analyze whether the rooftop solar utilization within campus buildings can meet the operational energy consumption needs of buildings in the campus area.

This article discusses the energy consumption characteristics of campus buildings and the self-sufficiency rates of rooftop photovoltaic systems. Section 2 primarily introduces the research methods, divided into two subsections: building energy consumption simulation and solar photovoltaic potential assessment. Section 2.1 uses DeST 2.0 software to simulate the energy consumption of typical buildings, while Section 2.2 and Section 2.3 employ QGIS and Python to calculate the available rooftop area and simulate the layout of photovoltaic panels. Section 3 presents the photovoltaic self-sufficiency rates of five typical building types—teaching buildings, sports halls, dormitories, dining halls, and libraries—and analyzes the overall campus photovoltaic self-sufficiency. Section 4 summarizes the research findings, highlighting the application potential of rooftop photovoltaic systems in reducing energy consumption in campus buildings, and proposes directions for future research. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings.

2. Methods

The research focuses on a university campus in Wuhan, characterized by its extensive land area and diverse building functions. Building zoning is a crucial aspect of regional energy planning, and rational zoning can effectively optimize building energy demands [23]. To enhance the study, an analysis of the functional use and energy characteristics of the buildings within the area was conducted, along with considerations for vacation periods and operational schedules, leading to the classification of buildings into distinct types.

This study employs remote sensing imagery data acquisition, sourcing the original imagery from Google satellite maps. Using QGIS 3.26.1 software, the imagery is gridded with image parameters set to a resolution of 2000 × 2000 pixels, corresponding to an actual distance of 100 × 100 m. Buildings within the area are categorized into five types: teaching office buildings, sports halls, dormitories, dining halls, and libraries, as illustrated in Figure 1.

2.1. Building Energy Consumption Simulation

This building energy consumption simulation is conducted using DeST software, which has been developed over more than ten years by Tsinghua University. The software features a wide range of applications and a user-friendly interface. The simulation accounts for various aspects of building energy consumption, including the operational energy consumption of HVAC systems, electricity usage by various devices, and the hot water load for residential areas. Initially, a survey is conducted on various typical buildings to assess their HVAC systems, types and quantities of equipment, residential hot water systems, and operational schedules. The survey results were then used as boundary conditions in the DeST 2.0 energy simulation software.

2.1.1. Typical Building Physical Model

This building energy consumption simulation uses DeST to create physical models of five typical buildings, as shown in Figure 2. The physical models of each building are drawn to scale, matching the actual dimensions of the buildings on a 1:1 scale. They are oriented according to their real-world directions. The window-to-wall ratio, shape coefficient, building shading coefficient, and enclosure structure materials are all set according to their actual application conditions. The building structure information input parameters are set in the physical model parameters of the DeST software.

2.1.2. Important Parameter Setting

(1)

Indoor and outdoor design parameters

The outdoor air design parameters are set according to the current Chinese standards [24], as shown in

Table 1. Calculation parameters of outdoor design in Wuhan area.

Region	Wuhan
Air conditioning outdoor temperature in winter (°C)	−2.6
Air conditioning outdoor relative humidity in winter (%)	77
Outdoor dry bulb temperature in summer (°C)	35.2
Summer ventilated outdoor relative humidity (%)	67

. For the ease of analysis in this simulation, the indoor design temperature for winter heating and summer air conditioning in all buildings is set at 20 °C and 26 °C, respectively. These settings ensure that the indoor environment achieves a comfort level of Class II.

(2)

Work and rest schedule

The university has set up a work and rest schedule with obvious lengths and periodicities, which directly affects the parameter settings of different typical buildings. According to the actual running status of the building, the personnel, lighting, equipment and air conditioning system of the building, respectively, are set up. The corresponding adjustment is made in DeST software. For all buildings of the same type, the heat gains from lighting, equipment, and per capita heat emissions are consistent. Taking the dormitories as an example, the room type definitions and schedules are shown in Figure 3a. The room type equipment definitions include parameters for occupancy, lighting, and equipment, as well as the schedule settings. The dormitory occupancy schedule is set as shown in Figure 3b, and the dormitory light schedule is set as shown in Figure 3c. Heat generation per capita is set to 61 W. Humidity production per capita is set to 0.109 kg/h. The maximum power of the light is set to 9 W. The maximum power of the equipment is set to 15 W. For instance, the number of occupants in a dormitory is set to four, and the per capita fresh air volume is set at 30 m³/h [24]. This value is the minimum amount of fresh air required by the code for the main function rooms of a public building that no one needs.

2.1.3. Integrated Power Consumption Composition

This simulation for calculating comprehensive electricity consumption encompasses three main parts: energy consumption for cooling and heating, energy consumption for domestic hot water, and electricity consumption for the lighting, elevators, and multimedia equipment. The energy consumption for cooling and heating is simulated using the DeST software with the previously mentioned settings, and this portion of the electricity consumption is calculated based on the HVAC system settings within the DeST software. To simplify the simulation calculation, the following assumptions are made:

(a)

The state space method is used in the simulation, and the indoor temperature is assumed to be a single node.

(b)

It is assumed that the physical properties of the wall do not change with time, and the heat transfer of the wall is simplified as one-dimensional heat conduction.

(c)

The lighting, equipment and heat dissipation of the human body in the same type of building remain unchanged.

(d)

All buildings use air-cooled heat pumps for both heating and cooling sources, with fan coil units and fresh air systems at the end-use stage.

(e)

The heat source for domestic hot water is also provided by air-cooled heat pumps, and its energy consumption is calculated through simulations in DeST software.

Through the analysis, it is found that only the dormitory has a certain amount of domestic hot water energy consumption. The power consumption of the lighting, elevators and multimedia is calculated by an on-site investigation of equipment power and number, and combined with the equipment running time determined by the rest schedule. The schedule of equipment operation can be seen in Section 2.1.2. The outdoor meteorological conditions in Wuhan were imported into the DeST simulation software as input data.

2.2. Solar Photovoltaic Potential Assessment

2.2.1. Calculation of Available Roof Area

Remote sensing imagery data are acquired and pixel recognition is employed for grid division. Within the grid, labels for “rooftops” and “obstacles” are created using Labelme. Python is used to identify the rooftop and obstacle pixel labels for each type of building. The process of grid division and label creation for the five typical buildings is illustrated in Figure 4. According to the field investigation, there are no solar photovoltaic devices installed on the roofs of all campus buildings at present.

Based on the image recognition processing results, the available roof area is calculated as follows [25]:

$$A_1=A_{roof}-A_{obstacle}$$

(1)

where A_roof is the total roof area on campus, m²; A_obstacle is the total roof obstruction area on campus, m².

The total roof area A_roof is calculated as follows:

$$A_{roof}={\displaystyle \frac{P_{pre}}{P_{total}}}\times Area$$

(2)

where P_total is the total number of pixels in the picture; P_pre is the total number of pixels identified on the roof; Area is the real area of the picture, m².

2.2.2. Simulation of Available Area of Photovoltaic Panel

A method based on image processing for arranging and overlaying small rectangles is employed, which can extract specific rectangles under certain conditions from an input image. First, the areas of rooftops and obstacles are identified, and after removing the rooftop obstacles, the fitted image is used as input through an image processing method. Secondly, considering that photovoltaic panels are typically arranged in regular rectangular shapes in practical applications, this method enables the extraction of specific rectangles from the image. Finally, by overlaying images, the deployment results of the photovoltaic panels are visualized, and the actual area available for photovoltaic panel installation and its utilization ratio are calculated.

Taking the library as an example, the process of simulating the usable area for rooftop PV panels is shown in Figure 5. Here, the rectangles are set to be 80 pixels in length and 40 pixels in width, with a spacing of 20 pixels between them, corresponding to actual dimensions of 4 m in length, 2 m in width, and a 1 m interval between the photovoltaic panels. In Figure 5b, the recognized obstacles are in green and the available roof area is in red. When calculating the available area of the roof photovoltaic, the area occupied by obstacles, heat pump units, etc., has been removed, as shown in Figure 5c. With the common rectangular arrangement of PV panels, specific rectangular shapes are extracted, arranged and combined, and the images are superimposed. The PV panels are visualized on the roof, as shown in Figure 5d. In the end, the actual usable area of the roof is calculated once more after the installation of PV panels. No consideration was given to reserving site area for other equipment attached to the PV. This is mainly because the equipment can be mounted under the PV panels, in the equipment room and on the wall.

2.2.3. Solar Radiation Data Analysis

Solar radiation data are crucial for calculating photovoltaic power generation, yet monitoring the year-round solar radiation intensity across a campus area can be challenging. To simplify the analysis, solar radiation intensity can be directly extracted using the meteorological files in the TRNSYS 18.0 software. Considering the latitude of Wuhan (29°58′–31°22′) and the recommended installation angle for photovoltaic panels, the installation angle for the photovoltaic panels is set at 30° [26,27]. This installation tilt angle tends to improve annual PV production. The variation in the total monthly solar radiation on the inclined surface of the installation is shown in Figure 6. It is evident that the total monthly solar radiation in this area is considerable, with an average monthly value of 103.6 kWh/m². Particularly during the hot summer months, the peak value occurs in July, reaching 155.8 kWh/m².

2.2.4. Solar Photovoltaic Power Generation Calculation

Through the above analysis, the available area and solar radiation of the photovoltaic panels on the roof of campus buildings are determined, and the photovoltaic power generation can be calculated.

The theoretical power generation S of solar photovoltaic on the roof of campus buildings can be calculated by the following formula:

$$S=A_1\times I_{\theta }\times \eta$$

(3)

where A₁ is the available area of photovoltaic panels on the roof of the campus building, m²; I_θ is the total amount of solar radiation per month on the inclination angle of the photovoltaic panel installation, kWh/m²; η is the theoretical power generation efficiency of photovoltaic panels.

2.3. Solar Photovoltaic Self-Sufficiency Rate Analysis

Initial surveys within the campus revealed that the operation schedules of various buildings are influenced by the school’s vacation periods, resulting in differences in equipment usage across buildings. This study employs a monthly calculation of photovoltaic generation potential to facilitate a comparison with the electricity consumption of campus buildings, allowing for an analysis of the solar PV self-sufficiency rate for each building. The solar photovoltaic self-sufficiency rate can be calculated using the following formula:

$$f={\displaystyle \frac{S}{Q_{ELE}}}\times 100\mathrm{\%}$$

(4)

where f is the solar photovoltaic self-sufficiency rate, %; Q_ELE is the electricity consumption, kWh.

After conducting field surveys, energy consumption simulations, electricity consumption calculations, rooftop area simulations, calculations of photovoltaic generation potential, and calculations of rooftop photovoltaic self-sufficiency rates, the process flowchart for the analysis of photovoltaic self-sufficiency rates in this study is shown in Figure 7.

3. Results and Discussion

3.1. The Photovoltaic Self-Sufficiency Rate of Teaching Office Buildings

Teaching office buildings are among the most important types on campus, occupying large areas and having high utilization rates. As shown in Figure 8, the simulations reveal monthly variations in electricity consumption and rooftop photovoltaic power generation for this typical building type. Electricity consumption significantly spikes during the heating and cooling seasons, primarily due to the major energy demands of HVAC systems. Despite the reduction in campus population during the summer and winter vacations, some faculty and students remain, maintaining high energy use. The highest electricity consumption for typical teaching office buildings occurs in July, reaching 422,900 kWh. Similarly, rooftop photovoltaic power generation peaks in July, aligning with the highest monthly solar radiation.

Overall, the rooftop photovoltaic systems on teaching buildings exhibit a relatively high self-sufficiency rate, achieving an annual average of 61.75%. During the transitional seasons of April, May, September, and October, the rate is around 100%. Monthly variations show three peaks, occurring in February and during the transitional seasons. The peak in February is due to the low utilization of academic and office buildings during the school’s winter vacation. The other two peaks occur in the transitional seasons when the HVAC systems are not in operation, significantly reducing electricity consumption.

3.2. The Photovoltaic Self-Sufficiency Rate of Sports Halls

The monthly variations in electricity consumption, rooftop photovoltaic power generation, and photovoltaic self-sufficiency rate for the sports hall are shown in Figure 9. The usage of the sports facility is greatly influenced by seasons and school vacations, resulting in significant fluctuations in electricity consumption. The peak electricity consumption occurs in June, reaching 423,000 kWh. During the summer and winter vacations, the sports facility is closed, resulting in minimal electricity consumption. During these periods, the rooftop photovoltaic self-sufficiency rate exceeds 100%, allowing the surplus energy to be used by other buildings on campus. The annual rooftop photovoltaic self-sufficiency rate for this building is 63.60%, indicating excellent photovoltaic self-sufficiency performance.

3.3. The Photovoltaic Self-Sufficiency Rate of Dormitories

Figure 10 illustrates the monthly variations in electricity consumption, rooftop photovoltaic power generation, and self-sufficiency rate for a typical dormitory. Dormitories are areas of relatively high population density within the university campus. The typical dormitory area is 5400 square meters, and the annual electricity consumption per unit area is 205 kWh. The electricity consumption throughout the year is at a high level. This is because dormitories are the main energy consuming buildings on college campuses. At the same time, the simulation thinks that the room is always occupied, and does not consider the influence of occupancy on energy consumption. The electricity consumption in summer vacation is obviously higher than that in winter vacation. Through the investigation, it is found that the number of students staying in school for academic reasons in summer vacation is much higher than in winter vacation.

The limited rooftop area of typical dormitories results in an annual total power generation of 373,000 kWh. The peak self-sufficiency rate for the rooftop photovoltaic systems in dormitories does not occur during the vacations but during the transitional seasons, reaching as high as 55%. The annual self-sufficiency rate for rooftop photovoltaic is only 33.62%. As dormitories are major consumers of electricity within the university campus, improving the photovoltaic self-sufficiency rate for dormitories could significantly reduce overall campus electricity consumption.

3.4. The Photovoltaic Self-Sufficiency Rate of Dining Halls

Figure 11 displays the monthly variations in electricity consumption, rooftop photovoltaic power generation, and the self-sufficiency rate of a typical dining hall. The dining hall operates year-round to ensure meal services for faculty and students, leading to consistently high energy consumption throughout the year. Electricity consumption peaks in the summer, notably in July at 508,800 kWh, due largely to extensive air conditioning use, which contrasts with the positive role of heating during the winter.

Substantial rooftop photovoltaic generation complements this, with maximum monthly output reaching 188,200 kWh. Despite high year-round electricity consumption, the dining hall still achieves an annual photovoltaic self-sufficiency rate of 39.41%. This is facilitated by the dining hall’s building being only three stories high and having a large rooftop area, with one rooftop serving all three floors.

3.5. The Photovoltaic Self-Sufficiency Rate of Libraries

Figure 12 presents the monthly changes in electricity consumption, rooftop photovoltaic power generation, and self-sufficiency rate for a library. The library, open year-round and frequently visited by students, exhibits considerable electricity consumption at 179 kWh per square meter annually. Beyond the electricity used by devices, the energy consumption is also influenced by the high number of people present, particularly in the summer when air conditioning use is significantly higher than winter heating. This disparity is also linked to the local climate conditions, where the cooling load is higher than the heating load.

The library is a six-story building, and part of its rooftop is occupied by air conditioning units, which limits the space available for photovoltaic panel installation. The maximum monthly power generation is only 191,400 kWh, which is significantly lower than the monthly electricity consumption. The annual self-sufficiency rate of the rooftop photovoltaic system is only 19.42%, which is lower than other typical buildings on the campus.

Based on the annual summary of the electricity consumption, rooftop photovoltaic power generation, and photovoltaic self-sufficiency rates for the five typical building types, the characteristics of these buildings are detailed in

Table 2. Annual summary of electricity consumption, rooftop photovoltaic power generation, and photovoltaic self-sufficiency rates for the five typical buildings.

Building Type	Building Area (m2)	Available Roof Area (m2)	Annual Power Consumption (×103 kWh)	Annual Power Generation (×103 kWh)	Annual Self-Sufficiency Rate (%)
Teaching office building	30,000	9673	3116.63	1924.40	61.75
Sports hall	25,000	5900	1845.17	1173.55	63.60
Dormitory	5400	1985	1109.33	373.00	33.62
Dining hall	18,000	7548	3810.24	1501.57	39.41
Library	44,000	7675	7863.26	1526.83	19.42

. Significant variations in annual electricity consumption among different building types correlate directly with their respective areas. The ranking of electricity consumption per unit area for the five types of buildings is as follows: dormitory, library, dining hall, teaching office building, and sports hall. Research and analyses have revealed that electricity consumption is closely related to the usage rate of buildings. The self-sufficiency rates of the five buildings clearly demonstrate how rooftop area and personnel schedules, including vacation periods, significantly influence photovoltaic efficiency. Sports hall and teaching office building have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest rate, under 20%.

3.6. The Photovoltaic Self-Sufficiency Rate of the Whole Campus

To better assess the rooftop photovoltaic self-sufficiency rate across the entire university campus, a comprehensive survey and simulation calculation were conducted for all buildings within the school. The results, summarized in

Table 3. Details of various types of buildings on the university campus.

Building Type	Building Number	Total Building Area (m2)	Total Usable Roof Area (m2)
Teaching office building	11	190,000	57,930
Sports hall	1	25,000	5900
Dormitory	27	145,800	47,639
Dining hall	2	46,000	11,604
Library	1	44,000	7675

, show the total building and rooftop areas. It is evident that teaching office buildings and dormitories have the largest share of building volume. Furthermore, these two types of buildings also have the highest ratio of usable rooftop area to building area, exceeding 30%. This indicates significant potential for rooftop photovoltaic installations on these types of buildings.

Simulations assessing electricity consumption and rooftop photovoltaic installation potential across all campus buildings show the overall campus photovoltaic self-sufficiency rate in Figure 13. Monthly data show that the rooftop photovoltaic generation is consistently lower than the electricity consumption. The campus can fully realize self-generation and self-consumption with rooftop photovoltaic, peaking in April at a self-sufficiency rate of 55% and reaching its lowest in December at less than 19%. The annual rooftop photovoltaic self-sufficiency rate for the entire campus is 35%, which significantly addresses the issue of high energy consumption in university campuses.

The buildings in the university campus are generally low-rise, mostly around six stories, which is conducive to maximizing solar energy capture on rooftops [28,29]. The concentrated layout of these buildings facilitates maintenance and management, providing a solid foundation for the application of rooftop photovoltaic systems. Furthermore, the university can enhance its photovoltaic self-sufficiency by developing green spaces and carports as additional installation sites, beyond just building rooftops.

This study found that the distribution of people and the simultaneous usage rate significantly impact campus electricity consumption. By strategically scheduling classes and planning the use of classrooms and dormitories to synchronize student activities, the use of electrical devices can be reduced, thereby lowering electricity consumption. The HVAC system employed in this study utilizes air-cooled heat pumps, which have high energy consumption. In practical applications, it may be beneficial to consider renewable energy systems with lower energy consumption, such as ground-source heat pumps.

3.7. Shortcomings and Prospects

This study balances the overall electricity consumption and the potential for rooftop photovoltaic power generation to evaluate the reliability of installing photovoltaic systems, providing guidance for achieving a low-carbon campus through rooftop photovoltaic applications. In the initial stage of the study, only a photovoltaic potential assessment was carried out, and there are many shortcomings, which can be carried out in the future study.

(1)

A lot of assumptions are made during the simulation of electricity consumption with DeST, such as ignoring the effect of actual occupancy on energy consumption. In practice, it is very important to reduce the energy consumption of campus buildings. Future studies could evaluate energy efficiency improvements before they become self-producing.

(2)

Rooftop PV installation form, slope optimization, etc., can have a significant impact on PV power generation. This cannot be ignored in analyzing the potential of rooftop PV power generation either. The slope optimization of rooftop photovoltaic for university campus buildings will be carried out in the next study.

(3)

The study does not detail the variations in campus electricity consumption under different long and short-term operational schedules, which makes it difficult to reflect the impact of supply and demand dynamics on power grid absorption. Future research will focus on the dynamic regulation and balance of the source–load–storage power system within the campus area.

4. Conclusions

This study focuses on a specific university campus, analyzing the comprehensive electricity consumption and the potential for rooftop solar photovoltaic installations across five typical building types and the entire campus. The research concludes with an assessment of the prospects for applying rooftop photovoltaic systems within university settings. The following conclusions are drawn:

(1)

HVAC systems, lighting fixtures, and hot water loads for five typical building types are simulated using DeSt software. The available roof area can be simulated and calculated using QGIS and Python. The rooftop solar PV self-sustainability can be evaluated by using both.

(2)

The annual electricity consumption of the five typical buildings shows significant differences. Except for sports facilities, which are closed during the summer vacation, the peak electricity consumption for all other typical buildings occurs in the summer. One of the dormitories, the most energy-intensive building on the university campus, has an electricity consumption of 205 kWh per unit area.

(3)

Based on the available rooftop space, the rooftop photovoltaic systems on sports facilities and academic buildings have a relatively high self-sufficiency rate, exceeding 60%. The library’s rooftop photovoltaic system has the lowest self-sufficiency rate, under 20%. The annual self-sufficiency rate of rooftop photovoltaic across the entire campus is 35%, which can be fully realized for self-production and self-use, effectively addressing the issue of high energy consumption in university campuses.