Energy consumption detection system based on smart campus

Abstract

The invention discloses an energy consumption detection system based on a smart campus, and relates to the technical field of energy consumption monitoring and adjustment, and the system comprises a data collection module which is used for obtaining curriculum schedule data from an educational administration system, obtaining a work and rest time table from a dormitory management system, and collecting the water and electricity energy consumption data of each region in real time; the dynamic reference generation module is used for associating reference energy consumption according to a course type and dynamically correcting a reference value based on an inter-class rest period and a light-off period in a work and rest time table; the anomaly detection module is used for calculating the deviation between the actual energy consumption and the corrected reference value, and generating a grading alarm signal when the deviation exceeds a threshold value; and the control execution module responds to the alarm signal to execute equipment linkage control. Therefore, the monitoring real-time performance can be improved, the prediction precision can be improved, and the operation and maintenance cost can be reduced.

Description

Technical Field

[0001] This invention belongs to the field of energy consumption monitoring and regulation technology, and specifically relates to an energy consumption detection system based on a smart campus. Background Technology

[0002] With the advancement of smart campus construction, campus energy management has gradually become an important issue in energy conservation and emission reduction. However, existing campus energy monitoring systems still have the following technical problems, leading to low energy management efficiency and serious resource waste. These include the fact that existing energy monitoring systems mostly rely on fixed thresholds or historical energy consumption data for anomaly detection, failing to respond to dynamic changes such as course adjustments and temporary activities, and ignoring the regular effects of students' collective behavior.

[0003] Therefore, an energy consumption monitoring system based on smart campuses is needed. Summary of the Invention

[0004] The purpose of this invention is to provide an energy consumption monitoring system based on a smart campus, which can improve monitoring real-time performance, increase prediction accuracy, and reduce operation and maintenance costs. The specific technical solution is as follows:

[0005] On the one hand, the present invention provides an energy consumption monitoring system based on a smart campus, comprising:

[0006] The data acquisition module is used to obtain course schedule data from the academic affairs system, obtain daily schedules from the dormitory management system, and collect water and electricity consumption data in various areas in real time.

[0007] The dynamic benchmark generation module associates benchmark energy consumption with course type and dynamically adjusts benchmark values ​​based on break times and lights-out times in the timetable.

[0008] The anomaly detection module calculates the deviation between the actual energy consumption and the corrected baseline value, and generates a graded alarm signal when the deviation exceeds the threshold.

[0009] The control execution module responds to the alarm signal to perform device linkage control.

[0010] Compared with existing technologies, the present invention has the following advantages:

[0011] The dynamic benchmark generation module associates benchmark energy consumption with course type and dynamically adjusts benchmark values ​​based on break times and lights-out times in the timetable. By linking timetable time, region, course type and student schedules, a dynamic energy consumption benchmark is established. By comparing the preset benchmark energy consumption with the real-time collected energy consumption, abnormal energy consumption in the region can be accurately identified, and timely action can be taken to reduce losses.

[0012] A further technical solution is that the dynamic benchmark generation module includes:

[0013] The water consumption correction unit during breaks is used to adjust water usage according to the formula. Adjust the baseline value, where k is a correction factor of 0.3-0.5, and N student Estimate the current number of students in the region;

[0014] The dormitory power consumption attenuation unit is used to reduce the base value hourly after the lights-out period. The reduction rules are: 20% for the first hour, 10% for the second hour, and 5% from the third hour onwards.

[0015] A further technical solution is that the hierarchical alarm strategy of the anomaly detection module includes:

[0016] A Level 1 alarm is triggered when the actual water consumption exceeds the corrected baseline value by 50% for 5 minutes.

[0017] A level 2 alarm is triggered when the actual power consumption exceeds 80% of the corrected baseline value for 10 consecutive minutes.

[0018] A further technical solution is that the control execution module links the device in the following way:

[0019] Trigger a Level 1 alarm, push a warning message to the administrator's terminal page, and reduce the water supply to the abnormal area;

[0020] A level 2 alarm is triggered, and the alarm is triggered and unnecessary power is automatically cut off.

[0021] On the other hand, the present invention provides an energy consumption detection method for smart campuses, employing the aforementioned energy consumption detection system for smart campuses, specifically including:

[0022] S1. Obtain course schedule data and student daily schedules;

[0023] S2. Dynamically adjust the energy consumption benchmark values ​​for each area based on course type and work / rest schedule;

[0024] S3. Collect actual energy consumption data in real time and compare it with the benchmark value to trigger a graded alarm;

[0025] S4. Implement region-specific control policies.

[0026] A further technical solution is that the dynamic correction of the reference value in step S2 includes:

[0027] Water consumption in the teaching area during breaks should be increased by 30%-50% above the baseline.

[0028] The electricity consumption in the dormitory area after lights out will decrease to less than 20% of the normal value on an hourly basis.

[0029] A further technical solution is that the triggering conditions for the graded alarm in step S4 include:

[0030] If the actual electricity consumption in the teaching area is less than 70% of the benchmark value during a course, it will be marked as an equipment malfunction.

[0031] If water consumption in the dormitory area continues to exceed the baseline value by 50% after lights out, it is considered a water pipe leak.

[0032] A further technical solution is that the student's daily schedule is obtained through any of the following methods:

[0033] Nighttime return data recorded by the dormitory access control system;

[0034] Offline time periods for student activity statistics on the campus app.

[0035] A further technical solution is that the formula for calculating the energy consumption deviation coefficient is:

[0036]

[0037] Among them, P min The anti-shake threshold is set to 0.5kW. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0039] Figure 1 This is a schematic diagram of the structure of an energy consumption detection system based on a smart campus according to one embodiment of the present invention.

[0040] Figure 2 This is a flowchart illustrating an energy consumption detection method based on a smart campus according to one embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Next, the working principle of this embodiment will be described in detail to enable those skilled in the art to better understand the present invention:

[0043] On the one hand, this invention provides an energy consumption detection system based on smart campuses, using primary, middle, and high school campus models, including:

[0044] The data acquisition module 101 is used to obtain course schedule data from the academic affairs system, obtain daily schedule data from the dormitory management system, and collect water and electricity consumption data in various areas in real time.

[0045] The acquired timetable data can include information such as course type, course time, number of courses, break time, course location, and number of students. The course data is mapped to the corresponding areas on campus, and water and electricity consumption data in various areas are collected in real time. The campus areas can include teaching areas, dormitory areas, etc.

[0046] The dynamic benchmark generation module 102 associates benchmark energy consumption with course type and dynamically corrects benchmark values ​​based on break times and lights-out times in the schedule.

[0047] The courses include experimental courses, electronic information courses, and theoretical courses. Experimental courses require hands-on experiments and are typically conducted in designated experimental teaching buildings. Electronic information courses are conducted in computer lab buildings, while theoretical courses are conducted in regular classrooms. Each type of course is associated with a different baseline energy consumption value. Generally, the duration of each course is the same, but the content differs, resulting in varying energy consumption. Therefore, a baseline energy consumption can be set for each course type, and the baseline value can be dynamically adjusted to account for student restroom breaks and lights-out periods.

[0048] The anomaly detection module 103 calculates the deviation between the actual energy consumption and the corrected baseline value, and generates a graded alarm signal when the deviation exceeds the threshold.

[0049] The system detects student activities currently taking place in each area and generates tiered alarm signals when thresholds are exceeded. For example, when a class is in session in the teaching area, the system compares the preset baseline energy consumption of the corresponding course in that area with the real-time collected energy consumption. The same applies to breaks between classes and lights-out periods in the dormitory area.

[0050] It's worth noting that more precise monitoring can be performed based on information from the course data table. For example, monitoring the water and electricity consumption of a specific floor in a teaching building can be done by using the course data table to obtain information such as the types and number of courses held on that floor today. By comparing the preset baseline energy consumption with the real-time collected energy consumption, abnormal energy consumption in that area can be accurately identified, allowing for timely intervention and minimizing losses. Similarly, individual monitoring can be performed on each classroom and dormitory. Furthermore, warning signals can be sent in tiers based on the ratio of baseline energy consumption to real-time collected energy consumption.

[0051] The control execution module 104 responds to the alarm signal to perform device linkage control.

[0052] Based on timetable data, abnormal classrooms or dormitories within a region can be precisely located, allowing for remote control of equipment in those classrooms or dormitories. For example, if abnormal electricity consumption occurs in a classroom, the power consumption of appliances such as air conditioners and fans can be appropriately reduced. This enables unified management of facilities. Alternatively, if abnormal water consumption occurs in a dormitory during lights-out hours, the water supply to that dormitory can be reduced or blocked by controlling the water inlet valve. This prevents situations where students forget to turn off appliances and faucets on a large scale, or where increased energy consumption is caused by pipe leaks or old and damaged appliances, thus reducing potential losses.

[0053] Optionally, in one embodiment of the present invention, the dynamic benchmark generation module includes:

[0054] The water consumption correction unit during breaks is used to adjust water usage according to the formula. Adjust the baseline value, where

[0055] k is a correction factor of 0.3-0.5, N student Estimate the current number of students in the region;

[0056] The dormitory power consumption attenuation unit is used to reduce the base value hourly after the lights-out period. The reduction rules are: 20% for the first hour, 10% for the second hour, and 5% from the third hour onwards.

[0057] It's understandable that the baseline power consumption for the teaching area is a range of power consumption when there are no classes. Therefore, the sum of these baseline values ​​for each course type represents the baseline power consumption for that course, which can be preset based on the historical energy consumption of each campus. However, water usage during breaks is entirely based on student activities, which can easily lead to errors. Therefore, adjusting for break-time water usage using a correction unit can reduce this risk.

[0058] The reduction in dormitory electricity consumption is based on a comprehensive consideration of students' daily routines, electricity usage patterns, and safety and energy-saving needs. Specifically, it follows a reduction curve that closely matches actual student schedules: Hour 1 (reduction of 20%): After lights out, students still engage in short-term activities such as washing up or charging devices, resulting in a rapid but not precipitous decrease in electricity consumption. Hour 2 (reduction of 10%): Most students are asleep, but some equipment, such as air conditioners and fans, remains running. From Hour 3 onwards (reduction of 5%): Students enter a deep sleep phase, maintaining only the minimum necessary electricity usage, such as emergency lighting and fire monitoring. This stepped reduction ensures a smooth transition, accommodates a few students with delayed sleep schedules, and reduces false positive alarms.

[0059] Optionally, the hierarchical alarm strategy of the anomaly detection module includes:

[0060] When actual water consumption consistently exceeds the corrected baseline value by 50% for 5 minutes, a Level 1 alarm is triggered and pushed to the administrator's terminal. Water consumption in the teaching area during breaks naturally fluctuates, such as for handwashing and cleaning, but the dynamic baseline already includes adjustments for student numbers; therefore, exceeding the corrected baseline value by up to 30% is considered within the normal fluctuation range. However, water consumption consistently exceeding the corrected baseline value by 50% for 5 minutes is deemed abnormal, potentially indicating issues such as pipe rupture or valve malfunction. In such cases, the administrator must be notified immediately for maintenance and repair to prevent short circuits or structural damage caused by leaks.

[0061] A level two alarm is triggered when actual power consumption continuously exceeds 80% of the corrected baseline value for 10 minutes. Overload can potentially cause fires, such as due to overheating of wires, but it's crucial to distinguish between instantaneous and sustained overloads. The 10-minute duration filters out short-term fluctuations, such as air conditioner startup. The 80% threshold already exceeds the standard safety margin, typically 120% of the rated value.

[0062] A 10-minute buffer is allowed before a level 2 alarm is triggered to avoid interrupting teaching due to accidental power outages caused by brief peaks.

[0063] Optionally, the control execution module can link the device in the following ways:

[0064] A Level 1 alarm is triggered, pushing a warning message to the administrator's terminal page and reducing or cutting off the water supply to the abnormal area.

[0065] A level two alarm is triggered, alerting the system and automatically cutting off unnecessary power. If actual power consumption exceeds 80% of the correction benchmark for 10 minutes, it is considered unauthorized power use, such as unauthorized electrical connections or equipment malfunction. Automatic disconnection maximizes public safety and prevents fires caused by overheating.

[0066] On the other hand, the present invention also provides an energy consumption detection method based on a smart campus, comprising:

[0067] S1. Obtain course schedule data and student daily schedules.

[0068] It is used to obtain course schedule data from the academic affairs system, to obtain the student schedule from the dormitory access control system recorded at night in the dormitory management system, or to obtain the student schedule from the offline time period of the student activity statistics of the campus APP.

[0069] S2. Dynamically adjust the energy consumption benchmark values ​​for each region based on course type and work / rest schedule.

[0070] The courses include experimental courses, electronic information courses, and theoretical courses. Experimental courses require hands-on experiments and are typically conducted in designated experimental teaching buildings. Electronic information courses are conducted in computer lab buildings, while theoretical courses are conducted in regular classrooms. Each type of course is associated with a different baseline energy consumption value. Generally, the duration of each course is the same, but the content differs, resulting in varying energy consumption. Therefore, a baseline energy consumption can be set for each course type, and the baseline value can be dynamically adjusted to account for student restroom breaks and lights-out periods.

[0071] S3. Collect actual energy consumption data in real time and compare it with the benchmark value to trigger a graded alarm.

[0072] The system detects student activities currently taking place in each area and generates tiered alarm signals when thresholds are exceeded. For example, when a class is in session in the teaching area, the system compares the preset baseline energy consumption of the corresponding course in that area with the real-time collected energy consumption. The same applies to breaks between classes and lights-out periods in the dormitory area.

[0073] It's worth noting that more precise monitoring can be performed based on information from the course data table. For example, monitoring the water and electricity consumption of a specific floor in a teaching building can be done by using the course data table to obtain information such as the types and number of courses held on that floor today. By comparing the preset baseline energy consumption with the real-time collected energy consumption, abnormal energy consumption in that area can be accurately identified, allowing for timely intervention and minimizing losses. Similarly, individual monitoring can be performed on each classroom and dormitory. Furthermore, warning signals can be sent in tiers based on the ratio of baseline energy consumption to real-time collected energy consumption.

[0074] S4. Implement region-specific control policies.

[0075] Based on timetable data, abnormal classrooms or dormitories within a region can be precisely located, allowing for remote control of equipment in those classrooms or dormitories. For example, if abnormal electricity consumption occurs in a classroom, the power consumption of appliances such as air conditioners and fans can be appropriately reduced. This enables unified management of facilities. Alternatively, if abnormal water consumption occurs in a dormitory during lights-out hours, the water supply to that dormitory can be reduced or blocked by controlling the water inlet valve. This prevents situations where students forget to turn off appliances and faucets on a large scale, or where increased energy consumption is caused by pipe leaks or old and damaged appliances, thus reducing potential losses.

[0076] In one embodiment of the present invention, the dynamic correction of the reference value in step S2 includes:

[0077] S21: The water consumption benchmark for the teaching area during breaks between classes should be increased by 30%-50%.

[0078] During breaks between classes, students use facilities such as restrooms and water dispensers in large numbers, causing a sudden surge in water consumption. Actual measurement data shows:

[0079] Water usage during breaks between theoretical classes can reach 1.8 times the normal level, and water usage during breaks between experimental classes can reach 2.2 times the normal level. If handwashing is required after handling chemical reagents, an increase of 30%-50% can cover more than 90% of the reasonable water usage fluctuations during breaks.

[0080] Furthermore, the adjustment factor can be increased by 50% in summer or after physical education classes due to higher handwashing frequency, and by 30% in winter, with the adjustment factor automatically adjusted based on environmental parameters.

[0081] S22: The electricity consumption benchmark value in the dormitory area after the lights-out period shall be reduced to less than 20% of the normal value on an hourly basis.

[0082] Reducing the percentage to below 20% can cover the normal nighttime electricity consumption of 99% of dormitories, while also exposing illegal electricity use, such as unauthorized refrigerator connections.

[0083] In one embodiment of the present invention, the triggering conditions for the graded alarm in step S4 include:

[0084] S41: If the actual power consumption in the teaching area is less than 70% of the baseline value during the course, it is marked as an equipment malfunction.

[0085] Based on the operating characteristics of teaching equipment, under normal circumstances: during class, the classroom should maintain the operation of basic equipment (such as projectors, lighting, and computers), with power consumption typically maintained at 80%-120% of the baseline value. A value below 70% indicates that critical equipment is not in use; for example, power consumption may drop by 35%-50% if the projector malfunctions. A 60% setting can also be used to account for potential malfunctions, such as a 25% power drop if half of the lighting is damaged.

[0086] In addition, students may turn off some devices while studying, but power consumption should not drop significantly during the time periods specified in the timetable. A 70% threshold can be used to filter out brief voltage fluctuations and when teachers temporarily turn off some devices.

[0087] S42: If water consumption in the dormitory area continues to exceed the baseline value by 50% after lights out, it is considered a water pipe leak.

[0088] Under normal circumstances, water consumption in the dormitory area after lights out is about 5% of the baseline value, mostly used for toilet flushing. A consumption exceeding 50% of the baseline value indicates a pipe rupture. When a pipe ruptures, the instantaneous flow rate can reach 3-5 times the normal value. The 50% threshold ensures: timely warning signals are sent, pipe bursts are detected promptly for timely repairs, and slow leaks, such as continuous leaks from a malfunctioning toilet tank, are identified.

[0089] In one embodiment of the present invention, the formula for calculating the energy consumption deviation coefficient is as follows:

[0090]

[0091] Among them, P min The anti-shake threshold is set to 0.5kW.

[0092] Among them, the energy consumption deviation coefficient can quantify the severity of energy consumption anomalies and provide a basis for determining graded alarms. Using the energy consumption deviation coefficient for judgment can adapt to dynamic benchmark changes in different regions and time periods, allowing the detection standard to be dynamically adjusted with the benchmark value, reducing the risk of false alarms or missed detections.

[0093] The foregoing description of specific exemplary embodiments of the present invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it is obvious that many changes and variations can be made based on the above teachings. Although embodiments of the invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. The purpose of selecting and describing exemplary embodiments is to explain the specific principles of the invention and its practical application, so that those skilled in the art, after reading this specification, can make modifications, substitutions, variations, and various choices and changes to the embodiments as needed without departing from the principles and spirit of the invention, provided that such modifications, substitutions, variations, and choices and changes are within the scope of the claims of the invention and are protected by patent law.

Claims

1. An energy consumption monitoring system based on a smart campus, characterized in that, include: The data acquisition module is used to obtain course schedule data from the academic affairs system, obtain daily schedules from the dormitory management system, and collect water and electricity consumption data in various areas in real time. The dynamic benchmark generation module associates benchmark energy consumption with course type and dynamically adjusts benchmark values ​​based on break times and lights-out times in the timetable. The anomaly detection module calculates the deviation between the actual energy consumption and the corrected baseline value, and generates a graded alarm signal when the deviation exceeds the threshold. The control execution module responds to the alarm signal to perform device linkage control.

2. An energy consumption detection method based on smart campus, characterized in that, include: S1. Obtain course schedule data and student daily schedule, which includes break times and dormitory lights-out times; S2. Dynamically adjust the energy consumption benchmark values ​​for each area based on course type and work / rest schedule; S3. Collect actual energy consumption data in real time and compare it with the corrected benchmark value to generate an energy consumption deviation coefficient; S4. When the energy consumption deviation coefficient exceeds the threshold, a graded alarm is triggered and control actions are executed.

3. The system according to claim 1, characterized in that, The dynamic benchmark generation module includes: The water consumption correction unit during breaks is used to adjust water usage according to the formula. Adjust the baseline value, where k is a correction factor of 0.3-0.5, and N student Estimate the current number of students in the region; The dormitory power consumption attenuation unit is used to reduce the base value hourly after the lights-out period. The reduction rules are: 20% for the first hour, 10% for the second hour, and 5% from the third hour onwards.

4. The system according to claim 1, characterized in that, The hierarchical alarm strategy of the anomaly detection module includes: When the actual water consumption exceeds the corrected baseline value by 50% for 5 minutes, a Level 1 alarm is triggered. A level 2 alarm is triggered when the actual power consumption exceeds 80% of the corrected baseline value for 10 consecutive minutes.

5. The system according to claim 4, characterized in that, The control execution module links the device in the following ways: Trigger a Level 1 alarm, push a warning message to the administrator's terminal page, and reduce the water supply to the abnormal area; A level 2 alarm is triggered, and the alarm is triggered and unnecessary power is automatically cut off.

6. The method according to claim 2, characterized in that, The dynamic correction of the reference value in step S2 includes: Water consumption in the teaching area during breaks should be increased by 30%-50% above the baseline. The electricity consumption in the dormitory area after lights out will decrease to less than 20% of the normal value on an hourly basis.

7. The method according to claim 2, characterized in that, The triggering conditions for the graded alarm in step S4 include: If the actual electricity consumption in the teaching area is less than 70% of the benchmark value during a course, it will be marked as an equipment malfunction. If water consumption in the dormitory area continues to exceed the baseline value by 50% after lights out, it is considered a water pipe leak.

8. The method according to claim 2, characterized in that, The student schedule was obtained through any of the following methods: Nighttime return data recorded by the dormitory access control system; Offline time periods for student activity statistics on the campus app.

9. The system or method according to claim 1 or 2, characterized in that, The course schedule data includes: Course time, region, course type, and number of students; The course types include at least theoretical courses, experimental courses, and electronic information courses, with each type of course associated with a different benchmark energy consumption value.

10. The method according to claim 2, characterized in that, The formula for calculating the energy consumption deviation coefficient is as follows: Among them, P min The anti-shake threshold is set to 0.5kW.

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