Intelligent optimization method and system for power utilization of office building

Abstract

The invention provides an intelligent optimization method and system for power utilization of an office building. The method comprises the steps that the personnel density of each floor in the office building and the operation state of power utilization equipment are monitored in real time; generating a load thermodynamic diagram based on the personnel density of each floor and the operation state of the electric equipment, and classifying the electric equipment into interruptible equipment and continuous operation equipment according to power regulation characteristics; in combination with the load thermodynamic diagram, the interruptible equipment and the continuous operation equipment, establishing a demand priority matrix of personnel density and equipment association degree; when the total load of the public building is larger than a preset load threshold value, operation parameters of the interruptible equipment are adjusted according to the demand priority matrix; if the adjusted total load is still larger than the preset load threshold value, the operation parameters of the continuous operation type equipment are adjusted, electrical load distribution is optimized, energy waste is reduced, excessive interference on key equipment is avoided, and the stability of system operation is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of office building power control, and particularly relates to an office building power intelligent optimization method and system. BACKGROUND

[0002] In the modern office environment, with the wide application of various types of electrical equipment, the energy consumption problem of office buildings is increasingly prominent.

[0003] The traditional power management mode mainly relies on manual inspection and simple timing control strategy. For example, the on-off of lighting, air conditioning and other equipment is controlled through a pre-set schedule, or the running state of the equipment is manually adjusted by relying on manual operation. However, the above management mode lacks flexibility and real-time performance, and cannot dynamically optimize according to the real-time personnel distribution and equipment running state, resulting in a common phenomenon of energy waste. Especially during the peak period of electricity consumption, the total load of the office building is easy to exceed the reasonable range, which not only increases the operating cost, but also may have a certain impact on the stability of the power grid. SUMMARY

[0004] The present application provides an office building power intelligent optimization method and system to solve the technical problem that the existing technology cannot accurately optimize the power consumption in the office building.

[0005] In one aspect, the present application provides an office building power intelligent optimization method, comprising: real-time monitoring of the personnel density of each floor and the running state of the electrical equipment in the office building; based on the personnel density of each floor and the running state of the electrical equipment, generating a load heat map, and classifying the electrical equipment into interruptible type equipment and continuous running type equipment according to power adjustment characteristics; combining the load heat map, the interruptible type equipment and the continuous running type equipment, establishing a demand priority matrix of personnel density and equipment correlation degree; when the total load of the office building is greater than a preset load threshold, adjusting the operating parameters of the interruptible type equipment according to the demand priority matrix; if the adjusted total load is still greater than the preset load threshold, adjusting the operating parameters of the continuous running type equipment.

[0006] According to the office building power intelligent optimization method provided by the present application, the load heat map is generated based on the personnel density of each floor and the running state of the electrical equipment, comprising: determining the electrical load value of each floor in different time periods according to the personnel density of each floor and the running state of the electrical equipment; partitioning the electrical load value according to floor and time period to form a time-zoned load data matrix; The load data matrix is visualized to generate a time-zoned load heat map.

[0007] According to the office building power intelligent optimization method provided by the application, the power load value of each floor in different time periods is determined according to the personnel density of each floor and the running state of the power equipment, and the method comprises the following steps: Collecting real-time power data of power equipment of each floor; According to the personnel density of each floor, the influence coefficient of personnel activity on power load in different time periods is determined; The real-time power data of each power equipment is multiplied by the influence coefficient of corresponding personnel activity to obtain the power load value of each floor in different time periods.

[0008] According to the office building power intelligent optimization method provided by the application, the power load value is divided into zones according to the floor and the time period to form a time-zoned load data matrix, which comprises the following steps: The office building is divided into a plurality of floor areas, and a day is divided into a plurality of time periods; The power load value of each floor in different time periods is filled into the corresponding floor area and time period to form a time-zoned load data matrix; The time-zoned load data matrix is subjected to data verification and normalization processing.

[0009] According to the office building power intelligent optimization method provided by the application, the demand priority matrix of personnel density and equipment correlation degree is established by combining the load heat map, the interruptible equipment and the continuous running equipment, which comprises the following steps: The correlation between power equipment and personnel density is analyzed by combining the load heat map, the distribution of interruptible equipment and continuous running equipment; Based on personnel density, equipment type, equipment power and equipment running state, the demand priority weight of each power equipment is calculated; The demand priority weight of the equipment is matrix processed according to the floor and the equipment type to form the demand priority matrix of personnel density and equipment correlation degree.

[0010] According to the office building power intelligent optimization method provided by the application, the correlation between power equipment and personnel density is analyzed by combining the load heat map, the distribution of interruptible equipment and continuous running equipment, which comprises the following steps: According to the load heat map, the personnel density data and the distribution data of power equipment of each floor in different time periods are extracted; For each floor and time period, the correlation degree of personnel density and power equipment distribution is calculated; wherein, the correlation degree is determined by analyzing the influence degree of the power adjustment characteristics of the equipment on personnel work; According to the device type and the personnel density, the adjustment potential of the power consumption device under different personnel densities is evaluated, wherein the higher the personnel density is, the greater the influence weight of the device adjustment on the personnel work is; Based on the correlation degree and the adjustment potential, an association weight value is assigned to each power consumption device in different time periods.

[0011] According to the office building power consumption intelligent optimization method provided by the application, the demand priority weight of each power consumption device is calculated based on the personnel density, the device type, the device power and the device operating state, which comprises: According to the personnel density, the influence weight of the device on the personnel work is determined, wherein the higher the personnel density is, the greater the influence weight is; According to the device type of the power consumption device, different basic weights are assigned to the interruptible type device and the continuous operation type device respectively, wherein the basic weight of the continuous operation type device is higher than that of the interruptible type device; According to the device power of the power consumption device, the power weight of the device is calculated, wherein the greater the power is, the higher the weight is; According to the operating state of the power consumption device, the current adjustment potential weight of the device is evaluated, wherein the higher the adjustment potential is, the higher the potential weight is; Based on the influence weight, the basic weight, the power weight and the potential weight, the demand priority weight of each power consumption device is obtained.

[0012] According to the office building power consumption intelligent optimization method provided by the application, the device demand priority weight is matrix processed according to the floor and the device type, and a demand priority matrix of the personnel density and the device correlation degree is formed, which comprises: The office building is divided into multiple floor areas, and each floor area corresponds to a matrix row; Each device type corresponds to a matrix column; The calculated device demand priority weight is filled into the corresponding floor area and device type to form a demand priority matrix; The demand priority matrix is subjected to data checking and normalization processing.

[0013] According to the office building power consumption intelligent optimization method provided by the application, if the total load after adjustment is still greater than the preset load threshold, the operating parameters of the continuous operation type device are adjusted, and the method further comprises: The energy consumption comparison data of each power consumption device before and after adjustment is recorded, and the weight parameters in the demand priority matrix are dynamically updated.

[0014] On the other hand, the application further provides an office building power consumption intelligent optimization system, which comprises: The monitoring module is used for monitoring the personnel density of each floor and the running state of the power consumption equipment in the office building in real time. The classification module is used for generating a load heat map based on the personnel density of each floor and the running state of the power consumption equipment, and classifying the power consumption equipment into interruptible equipment and continuous operation equipment according to power regulation characteristics. The correlation degree module is used for establishing a demand priority matrix of personnel density and equipment correlation degree in combination with the load heat map, the interruptible equipment and the continuous operation equipment. The first adjustment module is used for adjusting the running parameters of the interruptible equipment according to the demand priority matrix when the total load of the office building is greater than a preset load threshold. The second adjustment module is used for adjusting the running parameters of the continuous operation equipment if the total load after adjustment is still greater than the preset load threshold.

[0015] The office building power consumption intelligent optimization method and system provided by the application can generate a load heat map by monitoring the personnel density of each floor and the running state of the power consumption equipment in real time, accurately analyze the power consumption load distribution, dynamically adjust the running parameters of the interruptible equipment and the continuous operation equipment according to the demand priority matrix, optimize the power consumption load distribution and reduce energy waste, and avoid excessive interference to the key equipment by adjusting the interruptible equipment first and then adjusting the continuous operation equipment, thereby ensuring the stability of system operation. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0017] Figure 1 is a flowchart of the office building power consumption intelligent optimization method provided by the embodiment of the application; Figure 2 is a structural schematic diagram of the office building power consumption intelligent optimization system provided by the embodiment of the application; Figure 3 is a structural schematic diagram of the electronic device provided by the embodiment of the application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0019] Figure 1 FIG. 1 is a flowchart of an office building power intelligent optimization method provided by an embodiment of the present application.

[0020] Referring to Figure 1 , the office building power intelligent optimization method comprises the following steps 101 to 105.

[0021] Step 101, real-time monitoring of personnel density of each floor and running state of power consumption equipment in the office building.

[0022] In this step, the personnel density, i.e. the number of personnel per unit area on each floor in the office building. The change of personnel density can affect the power consumption demand, for example, more lighting, air conditioning and other equipment may be needed to run in personnel-intensive areas. The running state of power consumption equipment includes information such as on-off state, power output, running mode, etc. By monitoring the running state, the actual energy consumption of power consumption equipment and whether it has adjustment potential can be understood.

[0023] Sensor networks such as infrared sensors, cameras or intelligent access control systems can be used. These devices can sense the entry and distribution of personnel in real time and transmit data to the central control system. Through calculation, the personnel density can be obtained. Smart meters or sensors can be installed on power consumption equipment to collect real-time parameters such as power, current, voltage, etc. of the equipment, as well as the running mode (such as low-power mode, normal running mode, etc.) of the equipment, to obtain the running state.

[0024] In personnel-intensive office areas such as conference rooms and open office areas, real-time monitoring can help identify high-load areas and prioritize optimization of power consumption equipment in these areas. In areas with few or no personnel, equipment power can be turned off or reduced based on monitoring results to avoid unnecessary energy consumption.

[0025] Step 102, generating a load heat map based on the personnel density of each floor and the running state of power consumption equipment, and classifying power consumption equipment into interruptible type equipment and continuous running type equipment according to power adjustment characteristics.

[0026] Specifically, based on the personnel density of each floor and the running state of power consumption equipment, a load heat map is generated, including: Step one, determining the power consumption load value of each floor in different time periods according to the personnel density of each floor and the running state of power consumption equipment; Step two, divide the electricity load value according to floor and time period to form a time-division load data matrix; Step three, visualize the load data matrix to generate a time-division load heat map.

[0027] The load heat map can represent the size of the load by different colors. Generally, the deeper the color, the higher the load, and the lighter the color, the lower the load. For example, red represents high load, yellow represents medium load, and green represents low load. The load data can be arranged according to floor and time period. The horizontal axis can represent time (such as hours), and the vertical axis represents the floor. The color of each cell represents the electricity load of that floor in that time period. In practical applications, the heat map can also have interactive functions, such as displaying specific load values and other related information when the mouse hovers over a cell, further improving data readability.

[0028] The above steps can more accurately reflect the electricity load distribution of office buildings in different time periods and floor areas by generating a time-division load heat map, not only improving the accuracy of electricity load management, but also helping the system more intuitively identify high load areas and time periods.

[0029] Step 103, combining the load heat map, interruptible devices and continuous operation devices, establish the demand priority matrix of personnel density and device correlation.

[0030] Step 103 specifically includes: Combine the load heat map, and the distribution of interruptible devices and continuous operation devices, analyze the correlation between electricity devices and personnel density; Based on personnel density, device type, device power and device running state, calculate the demand priority weight of each electricity device; Matrix processing of device demand priority weight according to floor and device type to form the demand priority matrix of personnel density and device correlation.

[0031] The above steps can more accurately evaluate the importance and adjustment potential of each device in different time periods by considering personnel density, device type, device power and device running state, and forming a demand priority matrix, which can dynamically adjust the running parameters of the device according to real-time personnel density and device running state, avoiding rough judgment based on device power or running state.

[0032] Step 104, when the total load of the public building is greater than the preset load threshold, adjust the running parameters of the interruptible device according to the demand priority matrix.

[0033] In this step, the total load of the office building refers to the sum of power of all current electrical equipment. When the total load exceeds the preset load threshold, it indicates that the current electricity demand is too high, which may cause energy waste or pressure on the power grid. The preset load threshold is a upper limit value set in advance according to the electricity demand of the office building, the capacity of the power grid and the energy saving target. Interruptible equipment refers to those that can be suspended or reduced in power within a short time without significant impact on personnel work and equipment operation. For example, lighting equipment (such as lights in non-critical areas); air conditioning equipment (such as air conditioners in areas with fewer personnel); printers, copiers and other office equipment. The running state of the above-mentioned equipment can be flexibly adjusted according to actual demand.

[0034] The demand priority matrix records the demand priority weight of each equipment in different floors and time periods. When the total load exceeds the preset threshold, the system will adjust the interruptible equipment with lower priority first according to the demand priority matrix. The adjustment method may include: turning off the equipment (such as turning off part of the lighting fixtures); reducing the power of the equipment (such as increasing or decreasing the temperature of the air conditioner); suspending the operation of the equipment for a period of time (such as suspending the printer); The adjustment sequence starts from the lowest priority equipment and gradually adjusts upwards until the total load is below the preset threshold.

[0035] Step 105, if the total load after adjustment is still greater than the preset load threshold, adjust the running parameters of the continuous running equipment.

[0036] In this step, if the total load still exceeds the preset threshold after adjusting the interruptible equipment, it indicates that the load needs to be further reduced. At this time, the system will consider adjusting the running parameters of the continuous running equipment. Continuous running equipment refers to those that need to run continuously to ensure normal work or service and cannot be easily interrupted. For example: servers (such as servers in data centers), elevators (such as elevators in critical areas), ventilation systems (such as ventilation equipment in critical areas), etc. The running state of the above-mentioned equipment usually needs to be adjusted more carefully to avoid affecting normal work.

[0037] The adjustment method may include: reducing the power output of the equipment (such as lowering the CPU frequency of the server); adjusting the running mode of the equipment (such as switching the elevator to energy saving mode); optimizing the running parameters of the equipment (such as adjusting the wind speed of the ventilation system). When adjusting the continuous running equipment, the demand priority matrix is still referred to, and the equipment with less impact on personnel work is adjusted first. The adjustment sequence starts from the lowest priority continuous running equipment and gradually adjusts upwards until the total load is below the preset threshold.

[0038] In this embodiment, by monitoring the personnel density and the running state of the power-consuming equipment of each floor in real time, a load heat map is generated to accurately analyze the power consumption load distribution; according to the demand priority matrix, the running parameters of the interruptible and continuous running type equipment are dynamically adjusted to optimize the power consumption load distribution and reduce energy waste; the interruptible equipment is adjusted first, and then the continuous running type equipment is adjusted to avoid excessive interference to the key equipment and ensure the stability of the system operation.

[0039] In an embodiment of the present specification, the power consumption load value of each floor in different time periods is determined according to the personnel density and the running state of the power-consuming equipment of each floor, including: Step one, collecting real-time power data of power-consuming equipment of each floor; In this step, the power data of the equipment can be collected in real time through the smart meter or sensor installed on the equipment. The power data can include instantaneous power, cumulative power and other information of the power-consuming equipment.

[0040] Step two, determining the influence coefficient of personnel activity on power consumption load in different time periods according to the personnel density of each floor; In this step, the law of personnel activity in different time periods is analyzed according to the personnel density of each floor. For example, in the area with high personnel density, the use frequency and power demand of the power-consuming equipment may be higher; while in the area with low personnel density, the power demand of the power-consuming equipment may be lower. By introducing the influence coefficient, the influence of personnel activity is taken into account in the calculation of power consumption load, so that the calculation result is more in line with the actual demand.

[0041] The influence coefficient is used to quantify the influence degree of personnel activity on power consumption load, which is between 0 and 1, indicating the adjustment effect of personnel activity on equipment power demand in a specific time period. The calculation method of the influence coefficient can be to normalize the personnel density and multiply the adjustment parameter to quantify the influence degree of personnel activity on power consumption load. The adjustment parameter can be any value between 0.5 and 1.

[0042] Step three, multiplying the real-time power data of each power-consuming equipment by the corresponding influence coefficient of personnel activity to obtain the power consumption load value of each floor in different time periods; In this step, by comprehensively considering the influence of equipment power and personnel activity, more accurate power consumption load value is obtained, avoiding the error of simple power accumulation, which can more truly reflect the actual power demand of each floor in different time periods.

[0043] In this embodiment, by introducing the personnel activity influence coefficient, the electricity consumption load of each floor in different time periods can be calculated more accurately, avoiding rough estimation of electricity consumption load. The combination of real-time power data and personnel activity influence coefficient makes the calculation results dynamically reflect the actual electricity demand, providing a more scientific basis for subsequent optimization strategies. Accurate electricity consumption load values are the basis data for generating time-zone-based load thermal maps, providing reliable support for intelligent optimization strategies of the system, which helps to achieve more efficient and energy-saving electricity management.

[0044] In an embodiment of the present specification, the electricity consumption load values are divided by floor and time period to form a time-zone-based load data matrix, including: Step one, divide the office building into multiple floor areas, and divide a day into multiple time periods; In this step, the office building is divided into multiple floor areas, for example, it can be divided according to the actual floor number, such as 1st floor, 2nd floor, 3rd floor, etc. A day is divided into multiple time periods, for example, it can be divided by hour (such as 00:00-01:00, 01:00-02:00, etc.), or it can be divided by a finer time granularity (such as 15 minutes per time period), the specific division method can be determined according to actual demand and monitoring accuracy.

[0045] Step two, fill the electricity consumption load values of each floor in different time periods into the corresponding floor area and time period to form a time-zone-based load data matrix; In this step, according to the calculated electricity consumption load values of each floor in different time periods, these load values are filled into the corresponding floor area and time period. For example, if the electricity consumption load value of a certain floor in a certain time period is 100kW, this value is filled into the cell corresponding to the floor area and time period.

[0046] Step three, data verification and normalization processing of the time-zone-based load data matrix; In this step, the formed time-zone-based load data matrix is subjected to data verification to check the integrity and accuracy of the data, such as whether there are data missing, abnormal values, etc. Normalization processing is performed to adjust the load data to a unified range or format, facilitating subsequent analysis and processing. For example, the load values can be normalized to between 0 and 1, or standardized to conform to a certain statistical distribution.

[0047] In this embodiment, by dividing the electricity consumption load values by floor and time period, a structured data matrix is formed, which can more intuitively show the electricity consumption load distribution of the office building in different floors and time periods. For example, by viewing the load data matrix, it can be quickly found out which floors have higher electricity consumption load in which time periods, thereby providing a basis for the development of optimization strategies.

[0048] In an embodiment of the present specification, the correlation between the power consumption equipment and the personnel density is analyzed in combination with the load heat map and the distribution of interruptible equipment and continuous operation equipment, including: Step one, according to the load heat map, the personnel density data and the distribution data of the power consumption equipment in different time periods of each floor are extracted; Step two, for each floor and time period, the correlation degree between the personnel density and the power consumption equipment distribution is calculated; wherein the correlation degree is determined by analyzing the influence degree of the power adjustment characteristics of the equipment on the personnel work; In this step, for example, the influence of some equipment (such as air conditioning, lighting) on personnel work is greater, while the influence of some other equipment (such as background server) is smaller. The calculation method of the correlation degree is shown in the following formula (1): (1); Wherein, is the correlation degree of the power consumption equipment i in the time period j; N is the total number of floor areas; is the equipment power adjustment influence factor (weight); is the distribution density of the power consumption equipment i in the floor area k; is the personnel density of the floor area k in the time period j.

[0049] Step three, according to the equipment type and the personnel density, the adjustment potential of the power consumption equipment under different personnel densities is evaluated; wherein the higher the personnel density, the greater the influence weight of the equipment adjustment on the personnel work; In this step, the adjustment potential refers to the power range that can be adjusted by the equipment without affecting the normal work of the personnel. The higher the personnel density, the greater the influence weight of the equipment adjustment on the personnel work, so the adjustment potential needs to consider the personnel density and the equipment type. The calculation method of the adjustment potential is shown in the following formula (2): (2); Wherein, is the adjustment potential of the power consumption equipment i in the time period j; is the maximum adjustable power of the equipment i; is the personnel density in the time period j; is the preset maximum personnel density (for normalization); is the personnel influence weight of the equipment i.

[0050] Step four, based on the correlation degree and the adjustment potential, an association weight value is assigned to each power consumption equipment in different time periods; In this step, the calculation method of the association weight value is shown in the following formula (3): (3); wherein, is the relevance weight value of the device i in the time period j, is the basic weight of the device i (which can be determined according to the device type).

[0051] In this embodiment, by calculating the correlation degree and adjusting potential, this quantitative method considers the influence of the device on the work of the personnel and the actual adjusting capacity of the device, and can accurately quantify the relationship between the power consumption device and the personnel density. It can avoid excessive adjustment of key devices, thereby minimizing energy waste under the premise of not affecting the normal work of the personnel.

[0052] In an embodiment of the present specification, the demand priority weight of each power consumption device is calculated based on the personnel density, the device type, the device power and the device operating state, including: Step one, determining the influence weight of the device on the work of the personnel according to the personnel density; wherein the higher the personnel density, the greater the influence weight; the calculation method of the influence weight is shown in the following formula (4): (4); wherein, is the influence weight of the device i.

[0053] Step two, assigning different basic weights to interruptible devices and continuous operation devices according to the device type of the power consumption device; wherein the basic weight of the continuous operation device is higher than that of the interruptible device; In this step, the basic weight of the interruptible device is lower, for example, it can be between 0.4 and 0.6; the basic weight of the continuous operation device is higher, which can be between 0.8 and 1.0.

[0054] Step three, calculating the power weight of the device according to the device power of the power consumption device; wherein the greater the power, the higher the weight; the calculation formula (5) of the power weight is as follows: (5); wherein, is the power weight, is the power of the device i, is the preset maximum device power.

[0055] Step four, evaluating the current adjusting potential weight of the device according to the operating state of the power consumption device; wherein the higher the adjusting potential, the higher the potential weight; the calculation formula (6) of the adjusting potential weight is as follows: (6); wherein, represents the potential weight of the device i.

[0056] Step five, based on the influence weight, the basic weight, the power weight, the potential weight, the demand priority weight of each electrical equipment is obtained, and the calculation formula (7) is as follows: (7); Wherein, is the demand priority weight of the equipment i in the time period j.

[0057] In an embodiment of the present specification, the equipment demand priority weight is matrix processed according to floor and equipment type, and a demand priority matrix of personnel density and equipment correlation degree is formed, including: Step one, the office building is divided into multiple floor areas, and each floor area corresponds to a matrix row; In this step, for example, 1st floor, 2nd floor, 3rd floor, etc. Each row corresponds to a floor area. For example, the first row stores the demand priority weight of all equipment types in the first floor; the second row stores the demand priority weight of all equipment types in the second floor; the third row stores the demand priority weight of all equipment types in the third floor.

[0058] Step two, each equipment type corresponds to a matrix column; In this step, for example, the first column stores the demand priority weight of lighting equipment in all floors; the second column stores the demand priority weight of air conditioning equipment in all floors; the third column stores the demand priority weight of printers in all floors.

[0059] Step three, the calculated equipment demand priority weight is filled into the corresponding floor area and equipment type to form a demand priority matrix; Step four, data checking and normalization processing is performed on the demand priority matrix; The specific way of data checking and normalization processing in this step can refer to the foregoing embodiments, which will not be described here.

[0060] In an embodiment of the present specification, if the adjusted total load is still greater than the preset load threshold, the running parameters of the continuous running type equipment are adjusted, and then the method further includes: Record the energy consumption comparison data of each electrical equipment before and after adjustment, and dynamically update the weight parameters in the demand priority matrix.

[0061] In the embodiment, the energy consumption change amount of each device is calculated to evaluate the adjustment effect. The greater the energy consumption change amount, the better the adjustment effect. For example, if the energy consumption of device 1 is reduced from 100 kWh to 80 kWh, the energy consumption change amount is -20 kWh, indicating that the adjustment effect is good. According to the adjustment effect, the weight parameter in the demand priority matrix is dynamically adjusted. If the adjustment effect of a certain device is good, the weight of the device can be appropriately increased; otherwise, if the adjustment effect is poor, the weight of the device can be appropriately reduced.

[0062] Based on the same overall inventive concept, the present application also protects an intelligent optimization system for office building electricity, such as Figure 2 as shown, Figure 2 is a structural schematic diagram of the intelligent optimization system for office building electricity provided by the embodiment of the present application. The intelligent optimization system for office building electricity provided by the present application is described below, and the intelligent optimization system for office building electricity described below can be correspondingly referred to the intelligent optimization method for office building electricity described above.

[0063] The intelligent optimization system for office building electricity includes a monitoring module 201, a classification module 202, a correlation degree module 203, a first adjustment module 204, and a second adjustment module 205.

[0064] The monitoring module 201 is used to monitor the personnel density of each floor and the running state of the electrical equipment in the office building in real time. The classification module 202 is used to generate a load heat map based on the personnel density of each floor and the running state of the electrical equipment, and classify the electrical equipment into interruptible type equipment and continuous running type equipment according to the power adjustment characteristics. The correlation degree module 203 is used to establish a demand priority matrix of personnel density and equipment correlation degree in combination with the load heat map, the interruptible type equipment, and the continuous running type equipment. The first adjustment module 204 is used to adjust the running parameters of the interruptible type equipment according to the demand priority matrix when the total load of the office building is greater than a preset load threshold. The second adjustment module 205 is used to adjust the running parameters of the continuous running type equipment if the total load after adjustment is still greater than the preset load threshold.

[0065] Figure 3 is a structural schematic diagram of the electronic device provided by the embodiment of the present application.

[0066] As Figure 3As shown, the electronic device can include a processor 310, a communications interface 320, a memory 330, and a communications bus 340, wherein the processor 310, the communications interface 320, and the memory 330 complete communications with each other through the communications bus 340. The processor 310 can invoke a logical instruction in the memory 330 to execute the intelligent optimization method for office building power consumption.

[0067] In addition, the logical instruction in the memory 330 described above can be implemented in the form of a software functional unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0068] On the other hand, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the intelligent optimization method for office building power consumption provided by the above-mentioned methods.

[0069] In yet another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the intelligent optimization method for office building power consumption provided by the above-mentioned methods.

[0070] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment. Those skilled in the art can understand and implement it without creative labor.

[0071] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0072] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent optimization method for office building electricity consumption, characterized in that: include: Real-time monitoring of the population density and operating status of electrical equipment on each floor of the office building; Based on the occupant density of each floor and the operating status of the electrical equipment, a load heat map is generated, and the electrical equipment is classified into interruptible equipment and continuously operating equipment according to power regulation characteristics; In combination with the load thermal map, the interruptible equipment and the continuously operating equipment, a demand priority matrix of personnel density and equipment correlation is established; When the total load of the public building is greater than a preset load threshold, adjusting the operating parameters of the interruptible device according to the demand priority matrix; If the adjusted total load is still greater than the preset load threshold, the operating parameters of the continuously operating equipment are adjusted.

2. The intelligent optimization method for office building electricity consumption according to claim 1, characterized in that: The generating of a load heat map based on the density of people on each floor and the operating status of the electrical equipment includes: Determining the power load value of each floor in different time periods according to the population density of each floor and the operating status of the electrical equipment; Divide the power load value into partitions according to floors and time periods to form a time-divided and partitioned load data matrix; The load data matrix is ​​visualized to generate a time-sharing and zone-sharing load heat map.

3. The intelligent optimization method for office building electricity consumption according to claim 2, characterized in that: The determining of the power load value of each floor in different time periods according to the population density of each floor and the operating status of the electrical equipment includes: Collect real-time power data of electrical equipment on each floor; According to the density of people on each floor, determine the impact coefficient of people's activities on power load in different time periods; The real-time power data of each electrical device is multiplied by the influence coefficient of the corresponding personnel activities to obtain the power load value of each floor in different time periods.

4. The intelligent optimization method for office building electricity consumption according to claim 3, characterized in that: Partitioning the power load value according to floors and time periods to form a time-divided and partitioned load data matrix includes: Divide office buildings into multiple floor zones and divide the day into multiple time periods; Fill the power load values ​​of each floor in different time periods into the corresponding floor area and time period to form a time-sharing and zone-sharing load data matrix; Perform data verification and normalization on the time-sharing and partitioned load data matrix.

5. The intelligent optimization method for office building electricity consumption according to claim 1, characterized in that: The step of establishing a demand priority matrix of personnel density and equipment correlation by combining the load heat map, the interruptible equipment, and the continuously operating equipment includes: Analyze the correlation between electrical equipment and personnel density by combining the load heat map and the distribution of interruptible equipment and continuously operating equipment; Calculate the demand priority weight of each power-consuming device based on personnel density, equipment type, equipment power, and equipment operating status; The equipment demand priority weights are matrixed by floor and equipment type to form a demand priority matrix based on the correlation between personnel density and equipment.

6. The intelligent optimization method for office building electricity consumption according to claim 5, characterized in that: The analysis of the correlation between electrical equipment and personnel density based on the load heat map and the distribution of interruptible equipment and continuously operating equipment includes: Extracting the personnel density data and the distribution data of the electrical equipment on each floor in different time periods according to the load thermal map; For each floor and time period, calculate the correlation between the density of people and the distribution of electrical equipment; wherein the correlation is determined by analyzing the degree to which the power regulation characteristics of the equipment affect the work of people; Based on equipment type and personnel density, the regulation potential of electrical equipment at different personnel densities is evaluated; the higher the personnel density, the greater the impact of equipment regulation on personnel work; Based on the relevance and the regulation potential, a relevance weight value is assigned to each electric device in different time periods.

7. The intelligent optimization method for office building electricity consumption according to claim 5, characterized in that: The calculation of the demand priority weight of each power-consuming device based on personnel density, device type, device power, and device operating status includes: Determine the weight of the impact of equipment on personnel work based on personnel density; the higher the personnel density, the greater the impact weight; Different basic weights are assigned to interruptible devices and continuously operating devices according to the device type of the power-consuming device; among them, the basic weight of continuously operating devices is higher than that of interruptible devices; Calculate the power weight of the device based on the device power of the electrical device; the greater the power, the higher the weight; According to the operating status of the electrical equipment, the current regulation potential weight of the equipment is evaluated; the higher the regulation potential, the higher the potential weight; Based on the impact weight, basic weight, power weight and potential weight, the demand priority weight of each electrical equipment is obtained.

8. The intelligent optimization method for office building electricity consumption according to claim 5, characterized in that: The equipment demand priority weights are matrixed according to floors and equipment types to form a demand priority matrix of personnel density and equipment correlation, including: Divide the office building into multiple floor areas, each floor area corresponds to a matrix row; Assign each device type to a matrix column; Fill the calculated equipment demand priority weights into the corresponding floor areas and equipment types to form a demand priority matrix; Perform data verification and normalization on the demand priority matrix.

9. The intelligent optimization method for office building electricity consumption according to claim 1, characterized in that: After adjusting the operating parameters of the continuously operating device if the adjusted total load is still greater than the preset load threshold, the method further includes: The energy consumption comparison data of each electrical device before and after adjustment is recorded, and the weight parameters in the demand priority matrix are dynamically updated.

10. An intelligent optimization system for electricity consumption in office buildings, characterized in that: include: Monitoring module, used to monitor the density of people on each floor of the office building and the operating status of electrical equipment in real time; a classification module, configured to generate a load heat map based on the occupant density of each floor and the operating status of the electrical equipment, and classify the electrical equipment into interruptible equipment and continuously operating equipment according to power regulation characteristics; A correlation module, configured to establish a demand priority matrix of personnel density and equipment correlation by combining the load heat map, the interruptible equipment, and the continuously operating equipment; a first adjustment module, configured to adjust the operating parameters of the interruptible device according to the demand priority matrix when the total load of the public building is greater than a preset load threshold; The second adjustment module is configured to adjust the operating parameters of the continuously operating equipment if the adjusted total load is still greater than the preset load threshold.

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