CN115640909A - Intelligent energy efficiency management method and system for realizing carbon peak carbon neutralization - Google Patents
Intelligent energy efficiency management method and system for realizing carbon peak carbon neutralization Download PDFInfo
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Abstract
The application discloses a method and a system for intelligent energy efficiency management for realizing carbon peak carbon neutralization, which relate to the technical field of equipment energy efficiency management, and the method comprises the following steps: acquiring equipment data and electric power data of all electric power equipment in an equipment site; predicting the initial predicted power consumption of the equipment station after a preset time period by combining historical power data and equipment data; analyzing by combining real-time power data and equipment data to obtain a power loss parameter; adjusting initial predicted power energy consumption according to the power energy consumption parameters to obtain final predicted power energy consumption; judging whether the final predicted electric energy consumption exceeds a preset electric energy consumption threshold value; screening out lossy power equipment in the power equipment based on the equipment data if the final predicted power energy consumption exceeds the power energy consumption threshold; refurbishment information of the lossy power equipment is generated, and the refurbishment information is sent to a mobile terminal held by a site manager. The method has the advantage of high efficiency in controlling carbon emission.
Description
Technical Field
The application relates to the technical field of equipment energy efficiency management, in particular to an intelligent energy efficiency management method and system for realizing carbon peak carbon neutralization.
Background
Carbon neutralization means that enterprises, groups or individuals measure and calculate the total amount of greenhouse gas emission generated directly or indirectly within a certain time, carbon dioxide emission generated by the enterprises, the groups or the individuals is counteracted through the forms of tree planting, energy conservation, emission reduction and the like, and carbon peak-reaching means that the carbon emission enters a steady decline stage after entering a platform stage, namely, the carbon dioxide emission is balanced simply.
The carbon emission of an industrial factory is relatively high, in order to achieve carbon neutralization and carbon peak, the carbon emission of the industrial factory needs to be reasonably controlled, the carbon emission of the industrial factory needs to be controlled, the electric energy utilization rate of all equipment in the industrial factory needs to be improved, therefore, the electric energy use condition of electric equipment in each industrial factory can be monitored through a big data technology, when the electric energy consumption of the electric equipment in a certain industrial factory is too high, the electric energy consumption is too high, the power supply of the industrial factory can be interrupted in time, and a responsible person is informed to renovate the electric equipment with the too high electric energy consumption in the industrial factory in time.
With respect to the related art among the above, the inventors consider that the following drawbacks exist: because some special electric equipment in some industrial plants must complete a complete equipment starting process after being started to be powered off, if the power is interrupted, the equipment is damaged, and a great amount of property loss of the industrial plants is caused, even if the power loss of the special electric equipment is monitored to be too high, the special electric equipment cannot be immediately stopped to be started, so that the special electric equipment still generates too high carbon emission in the continuous starting process, and finally the overall efficiency of controlling the carbon emission of the industrial plants is low.
Disclosure of Invention
In order to overcome the defect that the efficiency of controlling carbon emission by monitoring the electric energy loss of electric equipment in real time is low, the intelligent energy efficiency management method and system for realizing carbon peak carbon neutralization are provided.
In a first aspect, the present application provides a method for intelligent energy efficiency management for carbon peak carbon neutralization, the method comprising the steps of:
acquiring equipment data and power data of all power equipment in an equipment site, wherein the power data comprises historical power data and real-time power data;
predicting initial predicted power consumption of the equipment station after a preset time period by combining the historical power data and the equipment data;
analyzing by combining the real-time power data and the equipment data to obtain a power consumption parameter of the power equipment;
adjusting the initial predicted electric power consumption according to the electric energy consumption parameter to obtain final predicted electric power consumption;
judging whether the final predicted power energy consumption exceeds a preset power energy consumption threshold value;
screening out lossy power equipment in the power equipment based on the equipment data if the final predicted power energy consumption exceeds the power energy consumption threshold;
and generating refurbishment information of the damaged power equipment, and sending the refurbishment information to a mobile terminal held by a manager of the equipment site.
By adopting the technical scheme, the equipment data and the power data of the power equipment in the equipment site are firstly obtained, the current time of the obtained data is recorded, the initial predicted power energy consumption of the equipment site at the future time when the current time passes through the preset time period can be predicted by combining the historical power data in the power data and the equipment data, but the predicted value of the initial predicted power energy consumption is not accurate enough, so that the power consumption parameter of the power equipment needs to be analyzed by combining the real-time power data and the equipment information in the power data, the initial predicted power energy consumption is adjusted and optimized through the power consumption parameter, more accurate final predicted power energy consumption is obtained, whether the power equipment in the equipment site has overhigh power consumption at the future time or not is predicted according to the preset power energy consumption threshold, the situation that the energy consumption is overhigh is caused, if the final predicted power energy consumption exceeds the power consumption threshold, the damaged power equipment with overhigh power consumption can be screened out according to the equipment data, the refurbishment information of the damaged power equipment is regenerated, and the refurbishment information is sent to a mobile terminal held by a manager of the equipment site, so that the manager can immediately interrupt the next work of the damaged power equipment after the current work of the damaged power equipment is completed, and the damaged power equipment is refurbishment.
Optionally, the historical power data includes historical power consumption of each historical time period of the historical multiple days, and the device data includes a real-time device turn-on number and a historical device turn-on number of each historical time period of the historical multiple days.
Optionally, the step of predicting the initial predicted power consumption of the equipment site after the preset time period elapses by combining the historical power data and the equipment data includes the steps of:
acquiring current time, and calculating the predicted time of the current time after a preset time period;
determining a target historical time period in which the predicted time is located;
marking the starting number of the historical devices, which is the same as the starting number of the real-time devices, in the starting numbers of the historical devices of the historical multiple days in the target historical time period as the starting number of the target historical devices, and marking the date of the starting number of the target historical devices as a target date;
acquiring target historical power consumption of all the target days in the target historical time period;
pre-processing all of the target historical power consumption;
and calculating to obtain historical average power energy consumption based on the preprocessed target historical power energy consumption, and taking the historical average power energy consumption as the initial predicted power energy consumption of the predicted time.
By adopting the technical scheme, the prediction time for predicting the energy consumption is calculated according to the current time for acquiring the equipment data and the electric power data, and because the number of the started electric power equipment in the equipment site is not changed in the preset time period, the target date which is the same as the time period of the prediction time and the number of the started equipment can be screened out according to the real-time equipment starting number, the target historical electric power energy consumption of all the target dates is acquired, and the historical average electric power energy consumption is calculated to serve as the initial prediction electric power energy consumption of the prediction time after the pretreatment screening of the target historical electric power energy consumption.
Optionally, the real-time power data includes a real-time device power consumption of each of the power devices, and the step of analyzing the real-time power data and the device data to obtain the power consumption parameter includes the following steps:
acquiring the real-time total power consumption of the equipment site;
respectively judging whether the real-time equipment power consumption of each electric power equipment is 0;
if the electricity consumption of the real-time equipment is not 0, marking the corresponding electric power equipment as started equipment, and counting the number of the started equipment;
calculating theoretical equipment power consumption of all started equipment by combining the real-time total power consumption and the equipment quantity;
calculating the equipment electric energy loss parameter of the started equipment by combining the real-time equipment power consumption of the started equipment and the theoretical equipment power consumption;
optimizing the corresponding equipment power consumption parameters based on the equipment data;
and calculating to obtain the electric energy loss parameters by combining all the optimized electric energy loss parameters of the equipment.
By adopting the technical scheme, the started equipment which is working is screened out according to whether the real-time equipment power consumption of each electric power equipment is 0, the theoretical equipment power consumption shared by each started equipment is calculated out according to the total power consumption of the equipment site and the screened equipment quantity of the started equipment, the equipment power consumption parameter is calculated out by combining the real-time equipment power consumption of the started equipment and the theoretical equipment power consumption, the equipment state of the started equipment is analyzed through various data in the equipment data, the equipment power consumption parameter of each started equipment is optimized, and the optimized equipment power consumption parameters are averaged to obtain the power consumption parameter.
Optionally, the equipment data includes a service life of each of the power equipments and a repair time of a last repair, and the optimizing the corresponding equipment power consumption parameter based on the equipment data includes the following steps:
acquiring current time;
judging whether the service life of the started equipment exceeds a preset life threshold value or not;
if the service life of the started equipment exceeds the age threshold, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time;
judging whether the time difference value exceeds a preset first difference value threshold value or not;
if the time difference exceeds the first difference threshold, reducing the corresponding equipment electric energy loss parameter by a preset first parameter optimization value;
if the time difference does not exceed the first difference threshold, reducing the corresponding equipment electric energy loss parameter by a preset second parameter optimization value, wherein the second parameter optimization value is smaller than the first parameter optimization value;
if the service life of the started equipment does not exceed the life threshold, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time;
judging whether the time difference exceeds a preset second difference threshold value, wherein the second difference threshold value is larger than the first difference threshold value; and if the time difference does not exceed the second difference threshold, increasing the corresponding electric energy loss parameter of the equipment by the second parameter optimization value.
By adopting the technical scheme, the started equipment is divided into high-age equipment with higher service years and low-age equipment with lower service years through the preset age threshold and the service life of the started equipment, the time difference between the overhaul time of all the started equipment and the current time is calculated, time difference thresholds with different sizes are respectively preset according to equipment with different age categories, a smaller first difference threshold is preset for the high-age equipment, and when the time difference between the high-age equipment and the last overhaul exceeds the first difference threshold, the possibility that the electric energy loss of the equipment is increased due to aging is higher, so that the electric energy loss parameter of the equipment corresponding to the equipment needs to be greatly reduced.
Optionally, screening out lossy power devices in the power devices based on the device data includes the following steps:
obtaining device current data and device voltage data of the turned-on device based on the device data;
counting the equipment running time of the started equipment;
calculating power loss data for the powered-on device in combination with the device current data, the device voltage data, and the device runtime;
calculating the electric energy loss rate of the started equipment by combining the electric energy loss data and the theoretical equipment power consumption;
judging whether the electric energy loss rate exceeds a preset loss rate threshold value or not;
if the electric energy loss rate exceeds the loss rate threshold value, the corresponding started equipment is judged to be lossy electric equipment.
By adopting the technical scheme, the electric energy loss data of the started equipment is calculated by acquiring the equipment current data, the equipment voltage data and the equipment running time of the started equipment, and the electric energy loss rate of the started equipment is further calculated by combining the theoretical equipment power consumption, so that the started equipment can be divided into the damaged electric power equipment and the lossless electric power equipment through the preset loss rate threshold value.
Optionally, the calculation formula of the power loss data of the turned-on device is:
P＝∫UIT，
in the formula: p is the power loss data, U is the device voltage data, I is the device current data, and T is the device running time.
In a second aspect, the present application further provides a system for intelligent energy efficiency management for carbon peak-to-peak carbon neutralization, comprising a processor and a memory, wherein the processor executes the method of the first aspect when executing the computer instructions stored in the memory.
By adopting the technical scheme, through calling of a program, the device data and the power data of the power device in the device site are firstly obtained, the current time of the obtained data is recorded, the initial predicted power consumption of the device site at the future time when the current time passes through the preset time period can be predicted by combining the historical power data and the device data in the power data, but the predicted value of the initial predicted power consumption is not accurate enough, so that the power consumption parameter of the power device needs to be analyzed by combining the real-time power data and the device information in the power data, the initial predicted power consumption is adjusted and optimized by the power consumption parameter, more accurate final predicted power consumption is obtained, whether the power device in the device site has overhigh power consumption at the future time or not is predicted according to the preset power consumption threshold, if the final predicted power consumption exceeds the power consumption threshold, the damaged power device with overhigh power consumption can be screened out according to the device data, the refurbishment information of the damaged power device is regenerated into refurbishment information of the damaged power device, and the refurbishment information is sent to a mobile terminal held by a manager, so that the damaged power device can be immediately overhauled under the condition that the damaged power device can be immediately repaired after the current work is completed.
To sum up, this application includes following beneficial technological effect:
the method comprises the steps of firstly obtaining device data and power data of power devices in a device site, recording the current time of the obtained data, predicting initial predicted power energy consumption of the device site when the current time passes through the future time of a preset time period by combining historical power data in the power data and the device data, wherein the predicted value of the initial predicted power energy consumption is not accurate enough, so that an electric energy loss parameter of the power devices needs to be analyzed by combining real-time power data and device information in the power data, adjusting and optimizing the initial predicted power energy consumption through the electric energy loss parameter to obtain more accurate final predicted power energy consumption, predicting whether the power devices in the device site have overhigh electric energy loss at the future time to cause overhigh energy consumption according to a preset power energy consumption threshold, screening out damaged power devices with overhigh electric energy loss according to the device data if the final predicted power energy consumption exceeds the electric energy consumption threshold, regenerating damaged power devices into renovation information of the damaged power devices, and sending renovation information to a mobile terminal held by a manager of the device site, so that the manager can immediately interrupt the next working cycle of the damaged power devices after the damaged power devices complete the current work, and timely renovate the damaged devices.
Drawings
Fig. 1 is a schematic flowchart illustrating an embodiment of a method for intelligent energy efficiency management to achieve carbon peak-to-peak carbon neutralization according to an embodiment of the present disclosure.
Fig. 2 is a schematic flowchart illustrating an embodiment of a smart energy management method for implementing carbon spike-up carbon neutralization according to the present disclosure.
Fig. 3 is a flowchart illustrating an embodiment of a method for intelligent energy efficiency management to achieve carbon peak-to-peak carbon neutralization according to the present application.
Fig. 4 is a flowchart illustrating an embodiment of a smart energy management method for implementing carbon spike-up carbon neutralization according to the present disclosure.
Fig. 5 is a flowchart illustrating an embodiment of a method for intelligent energy efficiency management to achieve carbon peak-to-peak carbon neutralization according to the present application.
Detailed Description
The present application is described in further detail below with reference to figures 1 to 5.
The embodiment of the application discloses an intelligent energy efficiency management method for realizing carbon peak carbon neutralization.
Referring to fig. 1, the intelligent energy efficiency management method for realizing carbon peak carbon neutralization comprises the following steps:
s101, acquiring device data and power data of all power devices in a device site.
The equipment sites can be multiple, the equipment sites are industrial plants comprising various production equipment, industrial equipment and other equipment in each enterprise, the power equipment is electric equipment needing electric energy in the equipment sites, equipment data of the power equipment can be obtained through an enterprise industrial plant management system, the equipment data mainly comprise data such as equipment number, equipment types, equipment models, equipment service life, current equipment starting number and historical equipment service data, the power data can pass through an electric power monitoring system of the industrial plants, and the power data comprise historical power data and real-time power data.
And S102, predicting the initial predicted power consumption of the equipment station after a preset time period passes by combining the historical power data and the equipment data.
The time when the device data and the power data are acquired is the current time, the initial predicted power consumption refers to the total power consumption of the device site predicted at the future time after the current time passes a preset time period, and the preset time period can be preset according to the operation cycle of the power devices in the device site.
And S103, analyzing by combining the real-time electric power data and the equipment data to obtain the electric energy loss parameter of the electric power equipment.
The power loss parameter may represent a power loss degree of the power device.
And S104, adjusting the initial predicted electric energy consumption according to the electric energy consumption parameters to obtain the final predicted electric energy consumption.
The initial predicted power consumption is obtained by predicting according to historical power data of the power equipment, new aging conditions or other abnormal conditions can occur in the continuous use process of the power equipment to cause increase of real-time power consumption, the power consumption parameters are obtained by analyzing and calculating according to the real-time power data of the power equipment, therefore, the initial predicted power consumption can be adjusted according to the power consumption parameters, and the specific adjustment mode is that the initial predicted power consumption and the power consumption parameters are divided to obtain the final predicted power consumption, so that the predicted value of the power consumption is more accurate.
And S105, judging whether the final predicted electric energy consumption exceeds a preset electric energy consumption threshold, and executing the step S106 if the final predicted electric energy consumption exceeds the electric energy consumption threshold.
The electric power energy consumption threshold value can be preset according to the historical electric power energy consumption value of the equipment station, if the judgment result is that the final predicted electric power energy consumption does not exceed the electric power energy consumption threshold value, no refurbishment information is generated, and equipment data and electric power data are continuously acquired.
And S106, screening out the lossy power equipment in the power equipment based on the equipment data.
The power consumption rate of the power equipment can be calculated according to the specific current and voltage values of the power equipment in the equipment data, and the lossy power equipment in the power equipment is screened out through a preset loss rate threshold value, wherein the lossy power equipment is the power equipment of which the power consumption rate is higher than the loss rate threshold value.
And S107, generating renovation information of the lossy power equipment, and sending the renovation information to a mobile terminal held by a manager of the equipment site.
The renovation information includes device information of the lossy power device, and the mobile terminal held by the device site manager can be a mobile phone or a mobile tablet computer.
The implementation principle of one implementation mode of the embodiment of the application is as follows:
the method comprises the steps of firstly obtaining device data and power data of power devices in a device site, recording the current time of the obtained data, predicting initial predicted power energy consumption of the device site when the current time passes through the future time of a preset time period by combining historical power data in the power data and the device data, wherein the predicted value of the initial predicted power energy consumption is not accurate enough, so that an electric energy loss parameter of the power devices needs to be analyzed by combining real-time power data and device information in the power data, adjusting and optimizing the initial predicted power energy consumption through the electric energy loss parameter to obtain more accurate final predicted power energy consumption, predicting whether the power devices in the device site have overhigh electric energy loss at the future time to cause overhigh energy consumption according to a preset power energy consumption threshold, screening out damaged power devices with overhigh electric energy loss according to the device data if the final predicted power energy consumption exceeds the electric energy consumption threshold, regenerating damaged power devices into renovation information of the damaged power devices, and sending renovation information to a mobile terminal held by a manager of the device site, so that the manager can immediately interrupt the next working cycle of the damaged power devices after the damaged power devices complete the current work, and timely renovate the damaged devices.
In one implementation manner of the embodiment of the present application, the historical power data includes historical power energy consumption of each historical time period of the historical multiple days, the device data includes a real-time device turn-on number and a historical device turn-on number of each historical time period of the historical multiple days, and referring to fig. 2, the step S102 that is to predict the initial predicted power energy consumption of the device site after the preset time period passes by combining the historical power data and the device data specifically includes the following steps:
s201, obtaining the current time, and calculating the predicted time after the current time passes a preset time period.
The current time is the universal time when the equipment data and the electric power data of the electric power equipment in the equipment site are acquired, and the future time obtained by adding the current time to a preset time period is the predicted time.
S202, determining a target historical time period where the predicted time is located.
The storage mode of the historical power data and the historical device opening number in the device data is time-division storage every day, for example, if data storage is performed every other hour, the data stored every day is data 0 to 00,1 to 00,2 for each of the following time-divisions 00 to 00, 23, and if the calculated predicted time is 17.
S203, the historical device opening number which is the same as the real-time device opening number in the historical device opening number of the historical multiple days in the target historical time period is marked as the target historical device opening number, and the date where the target historical device opening number is located is marked as the target date.
Wherein, assuming that the target historical time period is 17 00 to 18, and the real-time device opening number is 10, the target historical device opening number is also 10, and a date with the historical device opening number also 10 is screened out from a period of 17 to 18.
And S204, acquiring target historical electric energy consumption of all target dates in the target historical time period.
Here, taking the example in step S203, the historical power consumption stored in the period from 17.
S205, preprocessing all target historical power consumption.
The preprocessing process is a screening process, and the target dates with the target historical electric energy consumption lower than the preset energy consumption value in all the target dates are screened out, wherein the screened out target dates may be holidays or downtime days of equipment sites.
S206, historical average power energy consumption is calculated based on the preprocessed target historical power energy consumption, and the historical average power energy consumption is used as initial prediction power energy consumption of prediction time.
And calculating to obtain historical average power energy consumption by comprehensively dividing the historical power energy consumption by the total days of the target date.
The implementation principle of one implementation mode in the embodiment of the application is as follows:
the energy consumption prediction time is calculated according to the current time of the acquired device data and the current time of the acquired power data, and the number of the started power devices in the device station is not changed in the preset time period, so that the target dates which are the same as the time period of the prediction time and the number of the started devices can be screened out according to the number of the started devices in real time, the target historical power energy consumption of all the target dates is acquired, and after the target historical power energy consumption is preprocessed and screened, the historical average power energy consumption is calculated to serve as the initial prediction power energy consumption of the prediction time.
In one implementation manner of the embodiment of the present application, the real-time power data includes real-time device power consumption of each power device, and referring to fig. 3, the step S103 of obtaining the power loss parameter by combining the real-time power data and the device data analysis specifically includes the following steps:
and S301, acquiring the real-time total power consumption of the equipment site.
The real-time total power consumption of the equipment site is obtained through a total electric meter of the equipment site, and the real-time total power consumption is the sum of the real-time power consumptions of all electric power equipment in the equipment site.
S302, respectively judging whether the real-time equipment power consumption of each electric power equipment is 0, and if the real-time equipment power consumption is not 0, executing a step S303.
And if the electricity consumption of the real-time equipment is 0, marking the corresponding electric equipment as unopened equipment.
And S303, marking the corresponding power equipment as started equipment, and counting the number of the started equipment.
And S304, calculating the theoretical equipment power consumption of all started equipment by combining the real-time total power consumption and the equipment quantity.
All the electric power equipment in the equipment station supplies power through the power supply bus, all the electric power equipment and the power supply bus are in parallel connection, the real-time total power consumption is electric quantity data obtained through a total electric meter of the power supply bus, and therefore the theoretical equipment power consumption of all started equipment can be calculated by dividing the real-time total power consumption by the total equipment quantity.
S305, calculating the electric energy loss parameter of the started equipment by combining the real-time equipment power consumption and the theoretical equipment power consumption of the started equipment.
The real-time equipment power consumption of the started equipment is divided by the theoretical equipment power consumption, so that the equipment power consumption parameter of the started equipment can be calculated, and two decimal numbers are reserved for the equipment power consumption parameter.
And S306, optimizing corresponding equipment electric energy loss parameters based on the equipment data.
And optimizing the electric energy loss parameters of the equipment according to the equipment service life, the equipment service time, the equipment overhaul data and other data of the power equipment in the equipment data.
And S307, calculating to obtain an electric energy loss parameter by combining all the optimized equipment electric energy loss parameters.
The optimized equipment power loss parameters of all started equipment are added to obtain a parameter sum, and then the parameter sum is divided by the total equipment amount of the started equipment to obtain the power loss parameters.
The implementation principle of one implementation mode in the embodiment of the application is as follows:
the method comprises the steps of screening the started devices which are working according to whether the real-time device power consumption of each electric device is 0, calculating theoretical device power consumption shared by each started device according to the total power consumption of a device site and the screened number of the started devices, calculating a device power consumption parameter by combining the real-time device power consumption and the theoretical device power consumption of the started devices, analyzing the device states of the started devices according to various data in device data, optimizing the device power consumption parameter of each started device, and averaging all optimized device power consumption parameters to obtain the power consumption parameter.
In one implementation manner of the embodiment of the present application, the device data includes a service life of each power device and a latest overhaul time, and referring to fig. 4, the step S306 of optimizing a corresponding device electrical energy loss parameter based on the device data specifically includes the following steps:
s401, obtaining the current time.
The current time is universal time when the equipment data and the electric power data of the electric power equipment in the equipment site are acquired.
S402, judging whether the service life of the started equipment exceeds a preset service life threshold, and if the service life of the started equipment exceeds the service life threshold, executing a step S403; if the service life of the turned-on device does not exceed the age threshold, step S407 is executed.
Wherein the service life of the power equipment refers to the number of years of use of the power equipment.
And S403, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time.
And subtracting the overhaul time from the current time to obtain a time difference value.
S404, judging whether the time difference exceeds a preset first difference threshold, and if so, executing a step S405; if the time difference does not exceed the first difference threshold, step S406 is performed.
If the service life of the power equipment exceeds the age threshold, it is determined whether the time difference of the power equipment exceeds the first difference threshold, and if the time difference exceeds the first difference threshold, it indicates that the possibility of an increase in power consumption due to aging of the power equipment is higher.
S405, reducing the corresponding equipment electric energy loss parameter by a preset first parameter optimization value.
And reducing the corresponding equipment electric energy loss parameter by a preset first parameter optimization value, namely subtracting the first parameter optimization value from the equipment electric energy loss parameter.
And S406, reducing the corresponding equipment electric energy loss parameter by a preset second parameter optimization value.
And if the corresponding equipment electric energy loss parameter is reduced by the preset second parameter optimization value, the second parameter optimization value is subtracted from the equipment electric energy loss parameter, and the second parameter optimization value is smaller than the first parameter optimization value.
And S407, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time.
And subtracting the overhaul time from the current time to obtain a time difference value.
S408, judging whether the time difference value exceeds a preset second difference threshold value, and if the time difference value does not exceed the second difference threshold value, executing the step S409.
And if the judgment result is that the time difference exceeds the second difference threshold, the corresponding equipment electric energy loss parameter is not optimized.
And S409, improving the corresponding equipment electric energy loss parameter by a second parameter optimization value.
And increasing the corresponding equipment electric energy loss parameter by a preset second parameter optimization value, namely adding the equipment electric energy loss parameter to the second parameter optimization value.
The implementation principle of one implementation mode in the embodiment of the application is as follows:
the method comprises the steps of dividing started equipment into high-age equipment with higher service years and low-age equipment with lower service years through a preset age threshold and the service years of the started equipment, calculating time difference values of overhaul time and current time of all the started equipment, respectively presetting time difference value thresholds with different sizes according to equipment with different age categories, presetting a smaller first difference threshold for the high-age equipment, and when the time difference value of the high-age equipment from last overhaul exceeds the first difference threshold, indicating that the possibility of electric energy loss increase of the equipment due to aging is larger, so that equipment electric energy loss parameters corresponding to the equipment need to be greatly reduced.
In one implementation manner of the embodiment of the present application, referring to fig. 5, the step S106, namely, screening out the lossy power devices in the power devices from the device data specifically includes the following steps:
s501, obtaining device current data and device voltage data of the started devices based on the device data.
And screening out the device current data and the device voltage data in the device data of the started device through data screening.
And S502, counting the equipment running time of the started equipment.
And the equipment running time is the running time of the started equipment running at this time.
And S503, calculating the electric energy loss data of the started equipment by combining the equipment current data, the equipment voltage data and the equipment running time.
The calculation formula of the power loss data of the started equipment is as follows:
P＝∫UIT，
in the formula: p is the power loss data, U is the device voltage data, I is the device current data, and T is the device running time.
And S504, calculating the electric energy loss rate of the started equipment by combining the electric energy loss data and the theoretical equipment power consumption.
And dividing the electric energy consumption data of the started equipment by the theoretical equipment power consumption to obtain the electric energy consumption rate of the started equipment.
S505, judging whether the electric energy loss rate exceeds a preset loss rate threshold value, and if the electric energy loss rate exceeds the loss rate threshold value, executing the step S506.
And if the electric energy loss rate does not exceed the loss rate threshold, determining that the corresponding started equipment is lossless power equipment.
S506, the corresponding started equipment is judged to be the lossy power equipment.
The implementation principle of one implementation mode of the embodiment of the application is as follows:
the method comprises the steps of obtaining equipment current data, equipment voltage data and equipment running time of started equipment to calculate electric energy loss data of the started equipment, and further calculating the electric energy loss rate of the started equipment by combining theoretical equipment power consumption, so that the started equipment can be divided into damaged electric equipment and lossless electric equipment through a preset loss rate threshold value.
The embodiment of the application also discloses an intelligent energy efficiency management system for realizing carbon peak-to-peak carbon neutralization, which comprises a processor and a memory, wherein the processor executes the method shown in the figures 1 to 5 when executing the computer instructions stored in the memory.
The implementation principle of the embodiment is as follows:
the method comprises the steps of firstly obtaining equipment data and power data of the power equipment in an equipment station and recording the current time of the obtained data, predicting initial predicted power energy consumption of the equipment station at the future time when the current time passes through a preset time period by combining historical power data in the power data and the equipment data, wherein the predicted value of the initial predicted power energy consumption is not accurate enough, so that a power consumption parameter of the power equipment needs to be analyzed by combining real-time power data in the power data and the equipment information, optimizing the initial predicted power energy consumption through the power consumption parameter to obtain more accurate final predicted power energy consumption, predicting whether the power equipment in the equipment station has overhigh power consumption at the future time according to a preset power energy consumption threshold value to cause overhigh energy consumption, screening out damaged power equipment with overhigh power consumption according to the equipment data if the final predicted power energy consumption exceeds the power consumption threshold value, regenerating renovating information of the damaged power equipment, and sending the renovating information to a mobile terminal held by a manager of the equipment station manager, so that the manager can immediately interrupt next work of the damaged power equipment after the current work of the damaged power equipment, and timely renovate the repaired equipment.
The above embodiments are preferred embodiments of the present application, and the protection scope of the present application is not limited by the above embodiments, so: equivalent changes in structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. An intelligent energy efficiency management method for realizing carbon peak carbon neutralization is characterized by comprising the following steps:
acquiring equipment data and power data of all power equipment in an equipment site, wherein the power data comprises historical power data and real-time power data;
predicting initial predicted power consumption of the equipment station after a preset time period by combining the historical power data and the equipment data;
analyzing by combining the real-time power data and the equipment data to obtain a power consumption parameter of the power equipment;
adjusting the initial predicted electric power consumption according to the electric energy consumption parameter to obtain final predicted electric power consumption;
judging whether the final predicted power energy consumption exceeds a preset power energy consumption threshold value;
screening out lossy power equipment in the power equipment based on the equipment data if the final predicted power energy consumption exceeds the power energy consumption threshold;
and generating refurbishment information of the damaged power equipment, and sending the refurbishment information to a mobile terminal held by a manager of the equipment site.
2. The method according to claim 1, wherein the historical power data includes historical power consumption for each historical period of the historical days, and the device data includes a real-time device turn-on number and a historical device turn-on number for each historical period of the historical days.
3. The method as claimed in claim 2, wherein the step of predicting the initial predicted power consumption of the equipment site after a predetermined period of time has elapsed by combining the historical power data and the equipment data comprises the steps of:
acquiring current time, and calculating the predicted time of the current time after a preset time period;
determining a target historical time period in which the predicted time is located;
marking the starting number of the historical devices, which is the same as the starting number of the real-time devices in the starting number of the historical devices of the historical multi-day at the target historical time period, as the starting number of the target historical devices, and marking the date of the starting number of the target historical devices as a target date;
acquiring target historical power consumption of all the target days in the target historical time period;
pre-processing all of the target historical power consumption;
and calculating to obtain historical average power energy consumption based on the preprocessed target historical power energy consumption, and taking the historical average power energy consumption as the initial predicted power energy consumption of the predicted time.
4. The method as claimed in claim 1, wherein the real-time power data includes real-time power consumption of each of the electric devices, and the step of analyzing the real-time power data and the device data to obtain the power consumption parameter comprises the steps of:
acquiring the real-time total power consumption of the equipment site;
respectively judging whether the real-time equipment power consumption of each electric power equipment is 0;
if the electricity consumption of the real-time equipment is not 0, marking the corresponding electric power equipment as started equipment, and counting the number of the started equipment;
calculating theoretical equipment power consumption of all started equipment by combining the real-time total power consumption and the equipment quantity;
calculating the equipment electric energy loss parameter of the started equipment by combining the real-time equipment power consumption of the started equipment and the theoretical equipment power consumption;
optimizing the corresponding equipment power consumption parameter based on the equipment data;
and calculating by combining all the optimized equipment electric energy loss parameters to obtain electric energy loss parameters.
5. The method as claimed in claim 4, wherein the equipment data includes an age of each of the power equipments and a service time of a latest service, and the optimizing the corresponding equipment power loss parameter based on the equipment data includes the following steps:
acquiring current time;
judging whether the service life of the started equipment exceeds a preset life threshold or not;
if the service life of the started equipment exceeds the age threshold, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time;
judging whether the time difference exceeds a preset first difference threshold value or not;
if the time difference exceeds the first difference threshold, reducing the corresponding equipment electric energy loss parameter by a preset first parameter optimization value;
if the time difference does not exceed the first difference threshold, reducing the corresponding equipment electric energy loss parameter by a preset second parameter optimization value, wherein the second parameter optimization value is smaller than the first parameter optimization value;
if the service life of the started equipment does not exceed the life threshold, calculating a time difference value between the overhaul time corresponding to the started equipment and the current time;
judging whether the time difference exceeds a preset second difference threshold value, wherein the second difference threshold value is larger than the first difference threshold value; and if the time difference does not exceed the second difference threshold, increasing the corresponding equipment electric energy loss parameter by the second parameter optimization value.
6. The method as claimed in claim 4, wherein the step of screening out the lossy power devices in the power device based on the device data comprises the steps of:
obtaining device current data and device voltage data of the turned-on device based on the device data;
counting the device running time of the started device;
calculating power loss data for the powered-on device in combination with the device current data, the device voltage data, and the device runtime;
calculating the electric energy loss rate of the started equipment by combining the electric energy loss data and the theoretical equipment power consumption;
judging whether the electric energy loss rate exceeds a preset loss rate threshold value or not;
and if the electric energy loss rate exceeds the loss rate threshold value, judging that the corresponding started equipment is the lossy power equipment.
7. The method of claim 6, wherein the electrical energy loss data of the powered-on device is calculated by the following formula:
P＝∫UIT，
in the formula: p is the power loss data, U is the device voltage data, I is the device current data, and T is the device running time.
8. An intelligent energy efficiency management system for achieving carbon peak-to-peak carbon neutralization, comprising a processor and a memory, the processor executing the method of any one of claims 1 to 7 when executing computer instructions stored in the memory.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116128323A (en) * | 2023-04-07 | 2023-05-16 | 阿里巴巴达摩院(杭州)科技有限公司 | Power transaction decision processing method, storage medium and electronic equipment |
| CN117522168A (en) * | 2023-11-23 | 2024-02-06 | 北京清远博创科技有限公司 | Asset management method and system based on big data |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116128323A (en) * | 2023-04-07 | 2023-05-16 | 阿里巴巴达摩院(杭州)科技有限公司 | Power transaction decision processing method, storage medium and electronic equipment |
| CN117522168A (en) * | 2023-11-23 | 2024-02-06 | 北京清远博创科技有限公司 | Asset management method and system based on big data |
| CN117522168B (en) * | 2023-11-23 | 2024-05-14 | 北京清远博创科技有限公司 | Asset management method and system based on big data |
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