CN111786378A - Method and device for deciding electric load and electric power monitoring equipment - Google Patents
Method and device for deciding electric load and electric power monitoring equipment Download PDFInfo
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- CN111786378A CN111786378A CN202010484378.9A CN202010484378A CN111786378A CN 111786378 A CN111786378 A CN 111786378A CN 202010484378 A CN202010484378 A CN 202010484378A CN 111786378 A CN111786378 A CN 111786378A
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention relates to the technical field of power monitoring, and provides a method and a device for deciding an electric load and power monitoring equipment, wherein the method comprises the following steps: the method comprises the steps of collecting actual maximum demand in a current electricity charge settlement period, comparing the actual maximum demand with a preset alarm threshold, triggering excess demand information according to a preset alarm strategy when the actual maximum demand exceeds the preset alarm threshold, and executing demand reduction operation on incoming lines according to the excess demand information, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current electricity charge settlement period and a safety coefficient, and the excess demand information is expressed based on the actual maximum demand and is used as incoming line excess demand corresponding to the actual maximum demand.
Description
Technical Field
The invention relates to the technical field of power monitoring, in particular to a method and a device for deciding an electric load and power monitoring equipment.
Background
The contracted power load is the maximum value of the average load of power consumption per unit time in a power charge settlement period, also called as the maximum demand, the active electric meter can record the actual maximum demand of an incoming line in the power charge settlement period, under the condition that the power user agrees with a power supply enterprise in a contract mode to settle the basic power charge in a maximum demand charging mode, the maximum demand of the contract is declared in the contract, and when the actual maximum demand exceeds the product between the maximum demand of the contract and 105%, the power load exceeding the product is additionally settled by 2 times of the basic power charge in the maximum demand charging mode, so that the power consumption cost of the power user is improved.
However, the existing method for automatically deciding the power load focuses on the decision function of providing the maximum demand charging mode, and the excess early warning function is provided for the actual maximum demand in the electricity charge settlement period, so that the demand is difficult to be reduced by assisting the incoming line, and the actual maximum demand is effectively limited.
Disclosure of Invention
The invention provides a method and a device for deciding a power load and power monitoring equipment, aiming at the defect that a power load decision method in the prior art provides an excess early warning function for the actual maximum demand in a power charge settlement period.
According to a first aspect of the present invention, there is provided a method for deciding on an electrical load in a power monitoring device, comprising:
acquiring an actual maximum demand in a current electric charge settlement period, and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current electric charge settlement period and a safety coefficient;
and when the actual maximum demand exceeds the preset alarm threshold, triggering excess demand information according to a preset alarm strategy, and executing an operation of reducing demand on incoming lines according to the excess demand information, wherein the excess demand information is represented based on the actual maximum demand and is used for indicating the excess demand of the incoming lines corresponding to the actual maximum demand.
According to a second aspect of the present invention, there is provided an apparatus for deciding on an electrical load in a power monitoring device, comprising:
the power consumption load monitoring module is used for acquiring the actual maximum demand in the current power charge settlement period and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current power charge settlement period and the safety coefficient;
and the load information control module is used for triggering the excess demand information according to a preset alarm strategy and executing the operation of reducing the demand of the incoming line according to the excess demand information when the actual maximum demand exceeds the preset alarm threshold, wherein the excess demand information is represented by the actual maximum demand and is used for indicating the excess demand of the incoming line corresponding to the actual maximum demand.
According to a third aspect of the present invention, there is provided a power monitoring apparatus comprising: a non-volatile memory and at least one processor coupled with the non-volatile memory, the non-volatile memory configured to store at least one program or set of programs, the at least one processor configured to load and execute the at least one program or set of programs to implement the operational steps performed by the method of deciding an electrical load in an electrical device as described in the first aspect.
The method and the device for deciding the electric load in the electric power monitoring equipment and the electric power monitoring equipment have the advantages that: in the electric charge settlement period, the collected actual maximum demand exceeds the preset alarm threshold value as an automatic triggering condition of the excess demand information, so that the actual maximum demand is simply and effectively and automatically early-warned, the labor is saved, the early-warning efficiency is ensured, the demand is reduced by incoming lines, the actual maximum demand is favorably and effectively controlled not to exceed the preset alarm threshold value, and the support for saving electric power resources and electricity consumption cost is provided.
Drawings
Fig. 1 is a schematic flowchart of a method for deciding an electrical load in an electrical monitoring device according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of another method for deciding an electrical load in an electrical monitoring device according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of another method for deciding an electrical load in an electrical monitoring device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for deciding an electrical load in a power monitoring device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of another apparatus for deciding an electrical load in an electrical monitoring device according to an embodiment of the present invention
Fig. 6 is a schematic diagram of communication connection of a power monitoring device according to an embodiment of the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Example one
Referring to fig. 1, a method for deciding an electrical load in an electrical device includes:
step 101, respectively obtaining a capacity charging model, a demand charging model, a combined prediction model and a historical maximum demand sequence, wherein the historical maximum demand sequence comprises a plurality of historical maximum demands which are arranged according to a historical time sequence before a current electric charge settlement period;
102, obtaining at least one target maximum demand corresponding to at least one target electricity charge settlement period one by one based on a historical maximum demand sequence and a combined prediction model;
103, charging the sum of the target maximum demands of all the target maximum demands based on a demand charging model to obtain target demand electric charges;
104, carrying out charging processing on the total number of cycles of all the target electric charge settlement cycles based on a capacity charging model to obtain target capacity electric charges;
step 105, comparing the target demand electric charge with the target capacity electric charge;
step 1a6, when the target demand electric charge is less than the target capacity electric charge, configuring a demand charging model as a target charging model corresponding to all target electric charge settlement periods, and respectively configuring each target maximum demand as a contract maximum demand corresponding to each target electric charge settlement period one by one;
step 1b6, when the target demand electric charge is larger than or equal to the target capacity electric charge, configuring the capacity charging model as a target charging model corresponding to the settlement period of all the target electric charges and avoiding configuring the maximum demand of all the contracts;
step 107, collecting the actual maximum demand in the current electricity fee settlement period, and comparing the actual maximum demand with a preset alarm threshold;
and 108, when the actual maximum demand exceeds a preset alarm threshold, triggering the excess demand information according to a preset alarm strategy, and executing the operation of reducing the demand of the incoming line according to the excess demand information.
In a specific mode, the step 101 and the step 108 are executed in time sequence by the intelligent concentrator serving as the power monitoring equipment, and the capacity charging model, the demand charging model, the combined prediction model and the historical maximum demand sequence are all configured in the intelligent concentrator in advance, so that compared with the step 101 and the step 108 which are executed in parallel, the power load decision-making difficulty is reduced, and the stability and the reliability of the power load decision-making method are improved.
In a specific manner, one day before the current electric charge settlement period, the intelligent concentrator may be connected to the active electric meter through a communication cable, and the intelligent concentrator may read the historical maximum demand recorded by the active electric meter through the communication cable in each historical electric charge settlement period, and store each historical electric charge settlement period and the corresponding historical maximum demand in the electric load record table according to the reading time, for example: 12 historical electricity charge settlement periods from 2019, 1 month to 2019, 12 months are recorded in the electricity load recording table, the current electricity charge calculation period can be 2020, 1 month, and the day before 2020, 1 month is 31 days in 2019, 12 months and 31 months.
In a specific mode, the intelligent concentrator can predict a target maximum demand sequence belonging to three target electric charge settlement periods by combining a prediction model and applying all historical maximum demands in the electric load record table, the target maximum demand sequence comprises target maximum demands corresponding to each target electric charge settlement period one by one, the maximum demands can be automatically obtained for a plurality of future electric charge settlement periods, compared with a manual estimation mode, manpower is saved, and obtaining efficiency and accuracy are guaranteed.
In a specific manner, the intelligent concentrator can sum all the target maximum demands through a summation function to obtain a corresponding target maximum demand sum value, and perform cumulative counting on each target electricity fee settlement period to obtain a total period number, for example: the three target electricity charge settlement periods are respectively 1 month in 2020, 2 months in 2020, and 3 months in 2020.
The method has the advantages that the target demand electric charge is smaller than the target capacity electric charge and serves as an automatic decision-making condition for the demand charging model and the maximum contract demand, the demand charging model and the maximum contract demand are prevented from being selected under the condition that the target demand electric charge is larger than or equal to the target capacity electric charge, or the target demand electric charge is larger than or equal to the target capacity electric charge and serves as an automatic decision-making condition for the capacity charging model and a decision-free condition for the maximum total contracts, the capacity charging model is prevented from being selected and the maximum contract demand is prevented from being decided under the condition that the target demand electric charge is smaller than the target capacity electric charge, and the accuracy and the efficiency of automatically deciding the basic electric charge charging model and the maximum contract demand are effectively guaranteed.
In a specific manner, the preset alarm policy may be preset in the power consumption load record table, and when the intelligent concentrator compares that the actual maximum demand exceeds the preset alarm threshold in the power consumption settlement period, the preset alarm policy is read from the power consumption load record table, the difference between the actual maximum demand and the preset alarm threshold is calculated according to the prediction alarm policy, the excess demand information is constructed based on the difference and the actual maximum demand, and the excess demand information is sent to the service server or the user terminal.
In the electric charge settlement period, the actual maximum demand exceeds the preset alarm threshold value as the automatic triggering condition of the excess demand information, so that the actual maximum demand is simply and effectively and automatically early-warned, the labor is saved, the early-warning efficiency is ensured, the incoming line is facilitated to reduce the demand, the actual maximum demand is favorably and effectively controlled not to exceed the preset alarm threshold value, and the support for saving the electric power resource and the electric charge is provided.
In a specific manner, the combined prediction model includes a K-fold cross-validation model, a mesh search model, and at least one initial cubic exponential smoothing model, such as: one initial cubic exponential smoothing model is an additive model, or two initial cubic exponential smoothing models are respectively an additive model or a multiplicative model.
Step 102 specifically includes:
step 1021, respectively obtaining a smoothing index sequence and a grid number, wherein the smoothing index sequence comprises at least two mutually unequal smoothing indexes;
step 1022, performing equal-quantity uniform processing on the historical maximum demand sequence according to the grid number to obtain a demand grid sequence;
step 1023, respectively searching each smooth index in the smooth index sequence by utilizing a grid search model, respectively substituting each smooth index into each initial cubic index smooth model to obtain a corresponding to-be-verified cubic index smooth model, wherein the total number of all to-be-verified cubic index smooth models is equal to the product obtained by multiplying the total number of all smooth indexes by the total number of all initial cubic index smooth models;
step 1024, performing multi-fold cross validation processing on each to-be-validated cubic exponential smoothing model by using the K-fold cross validation model and the application demand grid sequence to obtain corresponding candidate cubic exponential smoothing models and prediction accuracy rates corresponding to the candidate cubic exponential smoothing models one to one;
step 1025, selecting the maximum prediction accuracy rate from all the prediction accuracy rates, and setting the candidate cubic exponential smoothing model corresponding to the maximum prediction accuracy rate as the optimal cubic exponential smoothing model;
and step 1026, performing multi-period fitting prediction processing on the historical maximum demand sequence by using the optimal cubic exponential smoothing model to obtain a predicted maximum demand sequence, wherein the predicted maximum demand sequence is a sequence formed by arranging a plurality of target maximum demands according to the time sequence of all target electricity fee settlement periods.
In a specific mode, compared with the parallel execution of the step 1021 and the step 1026, the intelligent concentrator time sequence execution step 1021 and the step 1026 are beneficial to the stability and the reliability of the combined prediction model in the operation process, the difficulty in obtaining the predicted maximum demand sequence is reduced, the obtaining precision of the predicted maximum demand sequence is ensured, the optimal cubic exponential smoothing model is selected from the multiple candidate cubic exponential smoothing models through the matching of the grid search model and the K-fold cross verification model, the maximum demand sequence is preset through the optimal cubic exponential smoothing model, and the self-learning and the iterative optimization are continuously carried out along with the updating of historical data.
In a specific manner, the combined prediction model may further include an autoregressive moving average model, a support vector machine, a gray prediction algorithm, and other algorithms, the smoothing index sequence and the number of grid parts may be preset in the parameter configuration table, and the smoothing index sequence is a sequence formed by arranging a plurality of smoothing indexes in order from small to large, for example: 0.2-0.4-0.5-0.6-0.9, and the number of meshes can be 5 or 10.
For example, the number of grid sets is equal to 10, the historical maximum demand sequence is equally divided according to 10 to obtain a demand grid sequence, the demand grid sequence has 10 equal parts of historical maximum demand quantum sequences, and each historical maximum demand quantum sequence is a corresponding grid set.
For example, the three smoothing indices are 0.4, 0.6, and 0.9, respectively, 0.4 is configured as a first smoothing index, 0.6 is configured as a second smoothing index, 0.9 is configured as a third smoothing index, the two initial cubic exponential smoothing models are an addition model and a multiplication model, respectively, the addition model is configured as a first initial cubic exponential smoothing model, and the multiplication model is configured as a second initial cubic exponential smoothing model; substituting the first smoothing index into the first initial cubic index smoothing model to obtain an 11 th cubic index smoothing model to be verified, and substituting the first smoothing index into the second initial cubic index smoothing model to obtain a 12 th cubic index smoothing model to be verified; substituting the second smoothing index into the first initial cubic index smoothing model to obtain a 21 st to-be-verified cubic index smoothing model, and substituting the second smoothing index into the second initial cubic index smoothing model to obtain a 22 nd to-be-verified cubic index smoothing model; and substituting the third smoothing index into the first initial cubic exponential smoothing model to obtain a 31 th cubic exponential smoothing model to be verified, and substituting the third smoothing index into the second initial cubic exponential smoothing model to obtain a 32 th cubic exponential smoothing model to be verified.
The target demand electricity rate is specifically expressed as:
wherein, Y1The target required amount of electricity charge is expressed,
represents the sum of the target maximum demands, FnIndicates a target maximum demand corresponding to the nth target electricity charge settlement period, N indicates the total number of periods, P1Representing a preset demand electricity rate.
The target capacity electricity rate is specifically expressed as: y is2＝N×S×P2Wherein Y is2Representing target capacity electricity charge, S representing preset user transformer capacity, P2Indicating a preset capacity electricity rate.
Example two
Referring to fig. 2, a method for deciding on an electrical load in an electrical device includes: acquiring the actual maximum demand in the current electricity fee settlement period, and comparing the actual maximum demand with a preset alarm threshold; and when the actual maximum demand exceeds a preset alarm threshold, triggering the excess demand information according to a preset alarm strategy, and executing the demand reduction operation on the incoming line according to the excess demand information.
The preset alarm threshold is equal to the product of the maximum contract demand corresponding to the current electricity charge settlement period and a safety factor, and the safety factor can be set between 80% and 105%, for example, the safety factor is 80% or 100% or 105%.
The excess demand information is information that is based on the actual maximum demand representation and is used to refer to the incoming line excess corresponding to the actual maximum demand, which can be used by the incoming line to reduce demand, for example: the demand of the incoming line A is reduced by transferring the electrical load generated by the incoming line A to the incoming line B.
The preset alarm strategy comprises a report generation sub-strategy, an event construction sub-strategy and a load information triggering sub-strategy, and the excess demand information is triggered according to the preset alarm strategy, and the method further comprises the following steps: respectively inquiring each device coupled on the incoming line according to the report generation sub-strategy, respectively recording the running state and the active power change value of each device at the moment corresponding to the actual maximum demand exceeding the preset alarm threshold value, and generating a corresponding demand overrun analysis report based on the running state and the active power; processing the actual maximum demand and the difference value between the actual maximum demand and a preset alarm threshold according to an event construction sub-strategy to obtain an excess demand alarm event; and carrying out correlation processing on the excess demand alarm event, the switch state information corresponding to the incoming line and the load rate corresponding to the switch state information according to the load information trigger sub-strategy to obtain the excess demand information, and sending the excess demand information.
The method comprises the steps that an excess limit reason is given by a demand excess limit analysis report, the excess limit reason is classified and summarized, and an optimized management suggestion is provided for equipment, so that the equipment is supported to stagger the peak time power load, the basic power cost is saved, after the demand excess limit analysis report is generated and an excess demand alarm event is constructed, excess demand information is constructed according to the demand excess limit analysis report and the excess demand alarm event, and the reliability and the comprehensiveness of the excess demand information can be improved.
EXAMPLE III
Referring to fig. 3, a method for deciding on an electrical load in an electrical device includes: acquiring the actual maximum demand in the current electricity fee settlement period, and comparing the actual maximum demand with a preset alarm threshold; when the actual maximum demand exceeds a preset alarm threshold, triggering the excess demand information according to a preset alarm strategy, and executing the demand reduction operation on the incoming line according to the excess demand information; or, when the actual maximum demand is less than or equal to the preset alarm threshold, trigger-free excess demand information according to a preset alarm policy, for example: the actual maximum demand is ignored or deleted in order not to trigger the excess demand information.
In the electricity fee settlement period, the condition that the actual maximum demand exceeds the preset alarm threshold value is taken as the automatic triggering condition of the excess demand information, so that the excess demand information is prevented from being automatically triggered under the condition that the actual maximum demand is smaller than or equal to the preset alarm threshold value, or the condition that the actual maximum demand is smaller than or equal to the preset alarm threshold value is taken as the trigger-free condition of the excess demand information, the excess demand information is prevented from being triggered under the condition that the actual maximum demand exceeds the preset alarm threshold value, and the triggering precision of the excess demand information and the utilization rate of the preset alarm strategy are effectively improved.
Example four
Referring to fig. 4, the apparatus for deciding on an electrical load in an electrical device includes: the system comprises an electricity load monitoring module and a load information control module.
And the power consumption load monitoring module is used for acquiring the actual maximum demand in the current power charge settlement period and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current power charge settlement period and the safety factor.
And the load information control module is used for triggering the excess demand information according to a preset alarm strategy and executing the operation of reducing the demand of the incoming line according to the excess demand information when the actual maximum demand exceeds a preset alarm threshold, wherein the excess demand information is represented based on the actual maximum demand and is used for indicating the excess demand of the incoming line corresponding to the actual maximum demand.
The preset alarm strategy comprises a report generation sub-strategy, an event construction sub-strategy and a load information triggering sub-strategy, and the load information control module is specifically used for: respectively inquiring each device coupled on the incoming line according to the report generation sub-strategy, respectively recording the running state and the active power change value of each device at the moment corresponding to the actual maximum demand exceeding the preset alarm threshold value, and generating a corresponding demand overrun analysis report based on the running state and the active power; processing the actual maximum demand and the difference value between the actual maximum demand and a preset alarm threshold according to an event construction sub-strategy to obtain an excess demand alarm event; and carrying out correlation processing on the excess demand alarm event, the switch state information corresponding to the incoming line and the load rate corresponding to the switch state information according to the load information trigger sub-strategy to obtain the excess demand information.
EXAMPLE five
Referring to fig. 4, the apparatus for deciding on an electrical load in an electrical device includes: the system comprises an electricity load monitoring module and a load information control module.
And the power consumption load monitoring module is used for acquiring the actual maximum demand in the current power charge settlement period and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current power charge settlement period and the safety factor.
The load information control module is used for triggering the excess demand information according to a preset alarm strategy when the actual maximum demand exceeds a preset alarm threshold, and executing the operation of reducing the demand of the incoming line according to the excess demand information; or when the actual maximum demand is less than or equal to the preset alarm threshold, triggering-free excess demand information according to a preset alarm strategy.
EXAMPLE six
Referring to fig. 5, the apparatus for deciding on an electrical load in an electrical device includes: the system comprises a maximum demand prediction module, a basic electric charge calculation module, a charging mode selection module, an electric load monitoring module and a load information control module.
The maximum demand forecasting module is used for respectively acquiring a capacity billing model, a demand billing model, a combined forecasting model and a historical maximum demand sequence, wherein the historical maximum demand sequence is a sequence formed by a plurality of historical maximum demands following the time sequence arrangement of a plurality of historical electric charge settlement periods before the current electric charge settlement period; and calculating at least one target maximum demand corresponding to at least one target electricity charge settlement period one by one based on the historical maximum demand sequence and the combined prediction model.
The basic electric charge calculation module is used for charging the sum value of the target maximum demand to which all the target maximum demands belong based on the demand charging model to obtain the target demand electric charge; and carrying out charging processing on the total number of cycles to which all the target electric charge settlement cycles belong based on the capacity charging model to obtain the target capacity electric charge.
The charging mode selection module is used for comparing the target demand electric charge with the target capacity electric charge; when the target demand electric charge is smaller than the target capacity electric charge, configuring a demand charging model as a target charging model corresponding to all target electric charge settlement periods, and respectively configuring each target maximum demand as a contract maximum demand corresponding to each target electric charge settlement period one by one; or, when the target demand electric charge is greater than or equal to the target capacity electric charge, configuring the capacity charging model as the target charging model corresponding to the settlement period of all the target electric charges and avoiding configuring the maximum demand of all the contracts.
And the power consumption load monitoring module is used for acquiring the actual maximum demand in the current power charge settlement period and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current power charge settlement period and the safety factor.
And the load information control module is used for triggering the excess demand information according to a preset alarm strategy and executing the operation of reducing the demand of the incoming line according to the excess demand information when the actual maximum demand exceeds a preset alarm threshold, wherein the excess demand information is represented based on the actual maximum demand and is used for indicating the excess demand of the incoming line corresponding to the actual maximum demand.
The combined prediction model comprises a K-fold cross validation model, a grid search model and at least one initial cubic exponential smoothing model, the maximum demand prediction module executes at least one target maximum demand process which is one-to-one corresponding to at least one target electricity charge settlement period based on a historical maximum demand sequence and the combined prediction model, and the combined prediction model specifically comprises the following steps: respectively obtaining a smoothing index sequence and a grid number, wherein the smoothing index sequence comprises at least two smoothing indexes which are not equal to each other; carrying out equal-quantity uniform processing on the historical maximum demand sequence by utilizing the grid number to obtain a demand grid sequence; respectively searching each smoothing index in the smoothing index sequence according to the grid searching model, and respectively substituting each smoothing index into each initial cubic index smoothing model to obtain a corresponding cubic index smoothing model to be verified; performing multi-fold cross validation processing on each to-be-validated cubic exponential smoothing model by using a K-fold cross validation model and applying a demand grid sequence to obtain corresponding candidate cubic exponential smoothing models and prediction accuracy rates corresponding to the candidate cubic exponential smoothing models one to one; selecting the maximum prediction accuracy rate from all the prediction accuracy rates, and setting a candidate cubic exponential smoothing model corresponding to the maximum prediction accuracy rate as an optimal cubic exponential smoothing model; and performing multi-period fitting prediction processing on the historical maximum demand sequence by using an optimal cubic exponential smoothing model to obtain a predicted maximum demand sequence, wherein the predicted maximum demand sequence is a sequence formed by arranging a plurality of target maximum demands according to the time sequence of all target electricity charge settlement periods.
The target demand electricity rate is specifically expressed as:
wherein, Y1The target required amount of electricity charge is expressed,
represents the sum of the target maximum demands, FnIndicates a target maximum demand corresponding to the nth target electricity charge settlement period, N indicates the total number of periods, P1Representing a preset demand electricity price;
the target capacity electricity rate is specifically expressed as: y is2＝N×S×P2Wherein Y is2Representing target capacity electricity charge, S representing preset user transformer capacity, P2Indicating a preset capacity electricity rate.
EXAMPLE seven
Referring to fig. 6, the power monitoring apparatus includes: the system comprises a universal serial bus, and a processor, a wireless module, a nonvolatile memory and a serial communication interface which are respectively connected with the universal serial bus in a communication mode, wherein the processor can be respectively coupled with the wireless module, the nonvolatile memory and the serial communication interface through the universal serial bus, the nonvolatile memory is configured to store at least one program or program set, the processor is configured to load and execute the at least one program or program set to realize any operation step executed by the method for deciding the electric load in the power equipment according to the first embodiment or the second embodiment or the third embodiment, and the nonvolatile memory is used for realizing any operation step executed by the method for deciding the electric load in the power equipment, and the nonvolatile memory is used for realizing the operation step.
In a specific mode, the serial communication interface is connected with the active electric meter through a communication cable, the active electric meter can read the actual maximum demand generated by the incoming line, the serial communication interface can acquire the actual maximum demand from the active electric meter at regular time, the processor can compare the actual maximum demand with a preset alarm threshold at regular time, and under the condition that the actual maximum demand exceeds the preset alarm threshold, the super demand information is constructed according to a preset alarm strategy, and the wireless module is instructed to send the super demand information to the cloud server or the user terminal.
The reader should understand that in the description of this specification, reference to the description of the terms "aspect," "embodiment," or "detailed description" or the like means that a particular feature, step, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention, and the terms "first" and "second," or the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second", etc., may explicitly or implicitly include at least one of the feature.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method of deciding on an electrical load in a power monitoring device, comprising:
acquiring an actual maximum demand in a current electric charge settlement period, and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current electric charge settlement period and a safety coefficient;
and when the actual maximum demand exceeds the preset alarm threshold, triggering excess demand information according to a preset alarm strategy, and executing an operation of reducing demand on incoming lines according to the excess demand information, wherein the excess demand information is represented based on the actual maximum demand and is used for indicating the excess demand of the incoming lines corresponding to the actual maximum demand.
2. The method for deciding on an electrical load in an electrical device according to claim 1, wherein the preset alarm policy includes a report generation sub-policy, an event construction sub-policy, and a load information triggering sub-policy, and triggering the excess demand information according to the preset alarm policy specifically includes:
respectively inquiring each device coupled on the incoming line according to the report generation sub-strategy, respectively recording the running state and the active power change value of each device at the moment corresponding to the actual maximum demand exceeding the preset alarm threshold, and generating a corresponding demand overrun analysis report based on the running state and the active power;
processing the actual maximum demand and the difference value between the actual maximum demand and the preset alarm threshold according to the event construction sub-strategy to obtain an excess demand alarm event;
and carrying out correlation processing on the demand overrun analysis report, the overload alarm event, the switch state information corresponding to the incoming line and the load rate corresponding to the switch state information according to the load information trigger sub-strategy to obtain the overload information.
3. The method for deciding on an electrical load in an electrical power plant according to claim 1, further comprising, after comparing the actual maximum demand with a preset alarm threshold:
and when the actual maximum demand is less than or equal to the preset alarm threshold, triggering-free the excess demand information according to the preset alarm strategy.
4. A method for deciding on an electrical load in an electrical power installation according to any of claims 1-3, characterized in that before the current electricity charge settlement period, it comprises the steps of:
respectively acquiring a capacity charging model, a demand charging model, a combined prediction model and a historical maximum demand sequence, wherein the historical maximum demand sequence is a sequence formed by a plurality of historical maximum demands following the time sequence of a plurality of historical electric charge settlement periods before the current electric charge settlement period;
calculating at least one target maximum demand corresponding to at least one target electric charge settlement period one by one based on the historical maximum demand sequence and the combined prediction model;
charging the sum value of the target maximum demand to which all the target maximum demands belong based on the demand charging model to obtain target demand electric charge;
charging the total number of cycles of all the target electric charge settlement cycles based on the capacity charging model to obtain target capacity electric charges;
comparing the target demand electricity rate with the target capacity electricity rate;
when the target demand electric charge is smaller than the target capacity electric charge, configuring the demand charging model as a target charging model corresponding to all target electric charge settlement periods, and respectively configuring each target maximum demand as a contract maximum demand corresponding to each target electric charge settlement period one by one;
or, when the target demand electric charge is greater than or equal to the target capacity electric charge, configuring the capacity charging model as a target charging model corresponding to the settlement period of all the target electric charges without configuring the maximum demand of all the contracts.
5. The method for deciding on an electrical load in an electrical power device according to claim 4, wherein the combined prediction model includes a K-fold cross validation model, a grid search model and at least one initial cubic exponential smoothing model, and the step of finding at least one target maximum demand corresponding to at least one target electricity rate settlement period one-to-one based on the historical maximum demand sequence and the combined prediction model comprises:
respectively obtaining a smoothing index sequence and a grid number, wherein the smoothing index sequence comprises at least two mutually unequal smoothing indexes;
performing equal-quantity uniform processing on the historical maximum demand sequence according to the grid number to obtain a demand grid sequence;
respectively searching each smoothing index in the smoothing index sequence by utilizing the grid searching model, and respectively substituting each smoothing index into each initial cubic index smoothing model to obtain a corresponding cubic index smoothing model to be verified;
performing multi-fold cross validation processing on each to-be-validated cubic exponential smoothing model by using the K-fold cross validation model and the demand grid sequence to obtain corresponding candidate cubic exponential smoothing models and prediction accuracy rates corresponding to the candidate cubic exponential smoothing models one to one;
selecting the maximum prediction accuracy rate from all the prediction accuracy rates, and setting a candidate cubic exponential smoothing model corresponding to the maximum prediction accuracy rate as an optimal cubic exponential smoothing model;
performing multi-period fitting prediction processing on the historical maximum demand sequence by using the optimal cubic exponential smoothing model to obtain a predicted maximum demand sequence, wherein the predicted maximum demand sequence is a sequence formed by arranging a plurality of target maximum demands according to the time sequence of all target electricity charge settlement periods;
the target demand electricity rate is specifically expressed as:
wherein, Y1The target demand electricity rate is represented,
represents the target maximum demand sum value, FnIndicating a target maximum demand corresponding to an nth target electricity charge settlement period, N indicating the total number of periods, P1Representing a preset demand electricity price;
the target capacity electricity rate is specifically expressed as:
Y2＝N×S×P2
wherein, Y2Representing the target capacity electricity charge, S representing a preset user transformer capacity, P2Indicating a preset capacity electricity rate.
6. An apparatus for determining an electrical load in a power monitoring device, comprising:
the power consumption load monitoring module is used for acquiring the actual maximum demand in the current power charge settlement period and comparing the actual maximum demand with a preset alarm threshold, wherein the preset alarm threshold is equal to the product of the contract maximum demand corresponding to the current power charge settlement period and the safety coefficient;
and the load information control module is used for triggering the excess demand information according to a preset alarm strategy and executing the operation of reducing the demand of the incoming line according to the excess demand information when the actual maximum demand exceeds the preset alarm threshold, wherein the excess demand information is represented by the actual maximum demand and is used for indicating the excess demand of the incoming line corresponding to the actual maximum demand.
7. The apparatus for deciding on an electrical load in an electrical power monitoring device according to claim 6, wherein the preset alarm policy includes a report generation sub-policy, an event construction sub-policy, and a load information trigger sub-policy, and the load information control module is specifically configured to:
respectively inquiring each device coupled on the incoming line according to the report generation sub-strategy, respectively recording the running state and the active power change value of each device at the moment corresponding to the actual maximum demand exceeding the preset alarm threshold, and generating a corresponding demand overrun analysis report based on the running state and the active power;
processing the actual maximum demand and the difference value between the actual maximum demand and the preset alarm threshold according to the event construction sub-strategy to obtain an excess demand alarm event;
carrying out correlation processing on the excess demand alarm event, the switch state information corresponding to the incoming line and the load rate corresponding to the switch state information according to the load information trigger sub-strategy to obtain the excess demand information;
the load information control module is further configured to avoid triggering the excess demand information according to the preset alarm policy when the actual maximum demand is less than or equal to the preset alarm threshold.
8. The apparatus for deciding the electric load in the power monitoring device according to claim 6 or 7, further comprising:
a maximum demand prediction module for respectively obtaining a capacity billing model, a demand billing model, a combined prediction model, and a historical maximum demand sequence, the historical maximum demand sequence including a plurality of historical maximum demands arranged according to a historical time sequence before the current electricity charge settlement period; calculating at least one target maximum demand corresponding to at least one target electric charge settlement period one by one based on the historical maximum demand sequence and the combined prediction model;
the basic electric charge calculation module is used for carrying out charging processing on the sum value of the target maximum demands of all the target maximum demands based on the demand charging model to obtain the target demand electric charge; charging the total number of cycles of all the target electric charge settlement cycles based on the capacity charging model to obtain target capacity electric charges;
the charging mode selection module is used for comparing the target demand electric charge with the target capacity electric charge; when the target demand electric charge is smaller than the target capacity electric charge, configuring the demand charging model as a target charging model corresponding to all target electric charge settlement periods, and respectively configuring each target maximum demand as a contract maximum demand corresponding to each target electric charge settlement period one by one; or, when the target demand electric charge is greater than or equal to the target capacity electric charge, configuring the capacity charging model as a target charging model corresponding to the settlement period of all the target electric charges without configuring the maximum demand of all the contracts.
9. The apparatus for deciding on an electrical load in a power monitoring device according to claim 8, wherein the combined prediction model comprises a K-fold cross validation model, a grid search model and at least one initial cubic exponential smoothing model, and the maximum demand prediction module is specifically configured to:
respectively obtaining a smoothing index sequence and a grid number, wherein the smoothing index sequence comprises at least two mutually unequal smoothing indexes;
performing equal-quantity uniform processing on the historical maximum demand sequence according to the grid number to obtain a demand grid sequence;
respectively searching each smoothing index in the smoothing index sequence by utilizing the grid searching model, and respectively substituting each smoothing index into each initial cubic index smoothing model to obtain a corresponding cubic index smoothing model to be verified;
performing multi-fold cross validation processing on each to-be-validated cubic exponential smoothing model by using the K-fold cross validation model and the demand grid sequence to obtain corresponding candidate cubic exponential smoothing models and prediction accuracy rates corresponding to the candidate cubic exponential smoothing models one to one;
selecting the maximum prediction accuracy rate from all the prediction accuracy rates, and setting a candidate cubic exponential smoothing model corresponding to the maximum prediction accuracy rate as an optimal cubic exponential smoothing model;
performing multi-period fitting prediction processing on the historical maximum demand sequence by using the optimal cubic exponential smoothing model to obtain a predicted maximum demand sequence, wherein the predicted maximum demand sequence is a sequence formed by arranging a plurality of target maximum demands according to the time sequence of all target electricity charge settlement periods;
the target demand electricity rate is specifically expressed as:
wherein, Y1The target demand electricity rate is represented,
represents the target maximum demand sum value, FnIndicating a target maximum demand corresponding to an nth target electricity charge settlement period, N indicating the total number of periods, P1Representing a preset demand electricity price;
the target capacity electricity rate is specifically expressed as:
Y2＝N×S×P2
wherein, Y2Representing the target capacity electricity charge, S representing a preset user transformer capacity, P2Indicating a preset capacity electricity rate.
10. A power monitoring device, comprising: a non-volatile memory and at least one processor coupled with the non-volatile memory, the non-volatile memory configured to store at least one program or set of programs, the at least one processor configured to load and execute the at least one program or set of programs to implement the operational steps performed by the method of deciding an electrical load in an electrical power device according to any of claims 1 to 5.
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