CN116470494A - Electric quantity load prediction method and device, electronic equipment and storage medium - Google Patents

Electric quantity load prediction method and device, electronic equipment and storage medium Download PDF

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Publication number
CN116470494A
CN116470494A CN202310441723.4A CN202310441723A CN116470494A CN 116470494 A CN116470494 A CN 116470494A CN 202310441723 A CN202310441723 A CN 202310441723A CN 116470494 A CN116470494 A CN 116470494A
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China
Prior art keywords
load
data
target
power grid
predicted
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李斯陶
常弘
梁置铭
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China Southern Power Grid Digital Platform Technology Guangdong Co ltd
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China Southern Power Grid Digital Platform Technology Guangdong Co ltd
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a method and a device for predicting electric quantity load, electronic equipment and a storage medium. The electric quantity load prediction method comprises the following steps: acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data is data related to the change of the load data of the power grid system; determining a target prediction model corresponding to a moment to be predicted based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data; and determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model. The technical scheme of the embodiment of the invention solves the problem of lower accuracy of power grid load prediction, and improves the accuracy of power grid load prediction.

Description

Electric quantity load prediction method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of power grid technologies, and in particular, to a method and apparatus for predicting an electric load, an electronic device, and a storage medium.
Background
The construction of the novel power system is free from the utilization of flexible loads, and the rising of technologies such as demand response, virtual power plants and the like also provides a more reasonable approach for the development of flexible load utilization.
The method for predicting the load of the power grid is very important for organizing power production and safe operation of the power grid, the traditional power grid short-term load prediction method is most widely applied by a time sequence method, the time sequence analysis method is a method for finding out the rule of the change of the power grid along with time according to past load statistical data and establishing a time sequence model to infer future load values, and the method has larger errors and leads to lower prediction accuracy.
Disclosure of Invention
The invention provides an electric quantity load prediction method, an electric quantity load prediction device, electronic equipment and a storage medium, and aims to solve the problem of low load prediction accuracy of a power grid.
According to an aspect of the present invention, there is provided a power load prediction method including:
acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data is data related to the change of the load data of the power grid system;
determining a target prediction model corresponding to a moment to be predicted based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data;
and determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model.
According to another aspect of the present invention, there is provided a power load predicting apparatus including:
the power grid data acquisition module is used for acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data are data related to the change of the load data of the power grid system;
the prediction model determining module is used for determining a target prediction model based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data;
and the power grid load prediction module is used for determining a target predicted value of the power grid load of the power grid system based on the target prediction model.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of predicting electrical loading according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for predicting electrical loading according to any embodiment of the present invention.
According to the technical scheme, historical load data of a power grid system to be predicted and load related data corresponding to the historical load data and related to the change of the load data of the power grid system are obtained; accurately establishing a relation between historical load data of a power grid system to be predicted and load associated data which corresponds to the historical load data and is associated with load data change of the power grid system; then, determining a target prediction model corresponding to the moment to be predicted based on the historical load data, the load related data and initial prediction models which are pre-constructed based on a plurality of load related data; establishing a target prediction model capable of accurately predicting load; and finally, determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model. The target prediction value of the power grid load of the power grid system at the moment to be predicted is accurately predicted based on the target prediction model with high accuracy, the problem that the load prediction accuracy of the power grid is low is solved, and the beneficial effect of improving the load prediction accuracy is achieved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predicting electrical load according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for predicting electric load according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electrical load prediction device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing a method for predicting a power load according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a power load prediction method according to an embodiment of the present invention, where the method may be applied to a load prediction situation, and the method may be performed by a power load prediction device, where the power load prediction device may be implemented in a form of hardware and/or software, and the power load prediction device may be configured in an electronic device. As shown in fig. 1, the method includes:
s110, acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data is data related to the change of the load data of the power grid system.
The historical load data can be understood as historical grid power load data.
Specifically, historical load data corresponding to a preset historical time period and data corresponding to the historical load data and associated with the change of the load data of the power grid system can be obtained and determined in a historical database of the power grid system. And establishing a corresponding relation between different load associated data and historical load data change. The load related data can be collected through the power dispatching automation system and stored in the historical database.
Optionally, the load related data includes at least one of environmental data, time data, regional power consumption data and abnormal load data, where the environmental data may be understood as environmental data of a region where the grid system to be predicted is located. The regional power usage data may be understood as power usage data of the region within a preset time. Abnormal load data may be understood as grid abnormal event data. For example, the environmental data may include temperature data and/or weather data; the time data may include holiday data and/or a preset time period of the day.
And S120, determining a target prediction model corresponding to the moment to be predicted based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data.
Specifically, the historical load data and the load related data are obtained. And carrying out association calculation on load association data such as different time periods, different temperatures, different weather, different holidays, different time points, different regional power utilization, different power grid abnormal events and the like to obtain a power grid short-term load predicted value, and carrying out classification statistics on a plurality of predicted values. And establishing an initial load prediction model. And adjusting the initial load prediction model based on the historical load data and the load related data, and taking the adjusted initial load prediction model as a target prediction model.
S130, determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model.
The time to be predicted may be understood as a specific time in the future to be predicted. The target forecast value may be understood as a forecast load value of the grid system to be forecast.
Specifically, based on a target prediction model, historical load data and load related data are input into the target prediction model, and a target prediction value of the grid load of the grid system at the moment to be predicted is determined according to the output value of the target prediction model.
Optionally, after the determining the target predicted value of the grid load of the grid system based on the target predicted model, the method further includes:
and determining a model predictive value of the power grid load of the power grid system based on the target predictive model, and determining the target predictive value of the power grid load of the power grid system based on the model predictive value.
The model predicted value can be understood as a predicted value output by the target prediction model for a plurality of load-related data corresponding to the time to be predicted.
Optionally, the determining the target predicted value of the grid load of the grid system based on the model predicted value includes:
the model predictive value is taken as a target predictive value of the power grid load of the power grid system, or,
a target predicted value of the grid load of the grid system is determined based on a difference between the model predicted value and the historical grid load.
Specifically, based on the model predictive value, comparing the model predictive value with a historical power grid load, and calculating a difference value between the model predictive value and the historical power grid load. Judging the relation between the difference value and a preset difference value threshold, if the difference value is larger than the preset difference value threshold, correcting the target predicted value to enable the difference value between the target predicted value and the historical grid load to be corrected to the upper limit of the preset difference value threshold, or enabling the difference value between the model predicted data and the historical grid load in the target area to be corrected to be within the interval of the preset difference value threshold. And taking the corrected target predicted value as a target predicted value of the power grid load of the power grid system. And if the difference value is not greater than the preset difference value threshold value, directly taking the model predicted value as a target predicted value of the power grid load of the power grid system. The preset difference threshold may be preset empirically, which is not limited in this embodiment.
Optionally, after the determining the target predicted value of the grid load of the grid system based on the target predicted model, further comprising,
acquiring an actual power grid load of the power grid system, and determining a prediction error between the actual power grid load and the target predicted value;
and under the condition that the prediction error reaches a preset error condition, updating the target prediction model.
It can be understood that the load of the power grid also changes greatly along with the influence of factors such as the improvement of living standard, the increase of electronic equipment, the adjustment of office policy and the like. When the load of the power grid changes greatly, the accuracy of the prediction result of the target prediction model is reduced, and the target prediction model needs to be updated.
Specifically, based on the error value predicted by the preset time interval and/or the monitoring model, according to the preset time interval and/or when the result error value output by the target prediction model is greater than the preset error threshold, the historical power grid load in the historical time period adjacent to the current moment and the load related data corresponding to the historical load data are re-acquired, and the corresponding relation between the parameters and the parameter values in the target prediction model is updated. And based on the corresponding relation between the parameters and the parameter values in the updated target prediction model, the target prediction value of the power grid load is obtained, and the target prediction value of the power grid load is obtained. Thus completing the updating of the target prediction model.
According to the technical scheme, historical load data of a power grid system to be predicted and load related data corresponding to the historical load data and related to the change of the load data of the power grid system are obtained; accurately establishing a relation between historical load data of a power grid system to be predicted and load associated data which corresponds to the historical load data and is associated with load data change of the power grid system; then, determining a target prediction model corresponding to the moment to be predicted based on the historical load data, the load related data and initial prediction models which are pre-constructed based on a plurality of load related data; establishing a target prediction model capable of accurately predicting load; and finally, determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model. The target prediction value of the power grid load of the power grid system at the moment to be predicted is accurately predicted based on the target prediction model with high accuracy, the problem that the load prediction accuracy of the power grid is low is solved, and the beneficial effect of improving the load prediction accuracy is achieved.
Example two
Fig. 2 is a flowchart of a power load prediction method according to a second embodiment of the present invention, where in the embodiment, how to determine a target prediction model corresponding to a time to be predicted based on the historical load data, the load-related data, and an initial prediction model in the above embodiment is further refined. Optionally, the determining, based on the historical load data, the load-related data and the initial prediction model, a target prediction model corresponding to the time to be predicted includes: based on the historical load data in a plurality of time periods, determining the corresponding relation between the load influence coefficient corresponding to each item of load related data and the attribute value of the load related data in the initial prediction model; and assigning the load influence coefficient in the initial load prediction model based on the attribute value of the load associated data corresponding to the moment to be predicted and the corresponding relation to obtain a target prediction model corresponding to the moment to be predicted. Reference is made to the description of this example for a specific implementation. The technical features that are the same as or similar to those of the foregoing embodiments are not described herein.
As shown in fig. 2, the method includes:
s210, acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data is data related to the change of the load data of the power grid system.
S220, based on the historical load data in a plurality of time periods, determining the corresponding relation between the load influence coefficient corresponding to each item of load associated data and the attribute value of the load associated data in the initial prediction model.
The load influence factor can be understood as a numerical factor of the load-related data in a single equation. Attribute values may be understood as attribute-specific features or parameters, such as: the attribute values of the load-associated data item weather may include, but are not limited to, at least one of sunny, overcast, rainy, and thunderstorm weather; the property values of the load-related data item temperatures may include a temperature range (e.g., 1 ℃ -12 ℃).
Specifically, based on historical load data of each time period in a plurality of time periods, determining a load influence coefficient corresponding to each item of load related data in the initial prediction model and an attribute value of the load related data. And establishing a corresponding relation comprising historical load data of each time period, load influence coefficients corresponding to each item of load related data and attribute values of the load related data.
Alternatively, the correspondence between the attribute values of the load-related data and their corresponding load influence coefficients may be a positive correlation and/or a negative correlation. For example, in the case where the attribute value of the load-related data is in the first numerical range, the attribute value of the load-related data and the corresponding load influence coefficient may be in a positive correlation relationship; in the case where the attribute value of the load-related data is in the second range of values, a negative correlation may be formed between the attribute value of the load-related data and its corresponding load influence coefficient. It should be noted that the positive correlation and/or the negative correlation may be a linear correlation or a nonlinear correlation, which may be set according to actual requirements, and are not specifically limited herein.
And S230, assigning a value to the load influence coefficient in the initial load prediction model based on the attribute value of the load associated data corresponding to the moment to be predicted and the corresponding relation to obtain a target prediction model corresponding to the moment to be predicted, wherein the initial load prediction model is a model pre-constructed based on a plurality of load associated data.
Taking the example that the load related data comprises temperature data, when the temperature range of the attribute value is 1-12 ℃, the temperature is reduced by 1 ℃, and the load influence coefficient corresponding to the temperature data is increased by 3%; when the temperature is lower than 1 ℃, calculating a load influence coefficient corresponding to the temperature data according to the temperature of 1 ℃; when the temperature is higher than 12 ℃ and lower than 24 ℃, the load influence coefficient corresponding to the temperature data takes a value of 1; when the temperature is not less than 24 ℃, the temperature is increased by 1 ℃, and the load influence coefficient corresponding to the temperature data is increased by 3%.
It will be appreciated that the load influence coefficients corresponding to different time periods to be predicted may be different. The load influence coefficients corresponding to the same load-related data may be different, and may be changed according to time change. For example: the temperature in the morning and the temperature in the afternoon may be different during the day.
Optionally, the load related data includes temperature data, weather data, holiday data, time period of day, regional electricity consumption data and abnormal load data, and the initial prediction model is:
F 1 ＝F 2 *K 1 *K 2 *K 3 *T n *Y±F j
wherein F is 1 For the grid load of the grid system, F 2 For daily work load, K 1 For the load influence coefficient corresponding to the temperature data, K 2 K is the load influence coefficient corresponding to weather data 3 For the load influence coefficient corresponding to holiday data, T n For the load influence coefficient corresponding to the time period in one day, Y is the load influence coefficient corresponding to the regional power consumption data, F j And (5) abnormal load data of the power grid system.
Specifically, the attribute values of the load-related data may be the same or different. For example: load influence coefficient K corresponding to different temperature intervals 1 Is different in value. Based on the load related data of different attribute values, load influence coefficients corresponding to the load related data of different attribute values are calculated, and a load influence coefficient value interval corresponding to each load related data is obtained. And determining a value interval of the load influence coefficient corresponding to each load related data at the time to be predicted based on the time to be predicted, and further assigning the load influence coefficient in the initial load prediction model, wherein the assignment of the load influence coefficient is positioned in the value interval of the load influence coefficient corresponding to the load related data. Assigning load influence coefficients corresponding to each load-related data itemAnd substituting the value into the formula, and determining the grid load of the grid system according to the calculation result of the formula. Exemplary, the daily work load is related to the load influence coefficient F 2 Load influence coefficient K corresponding to temperature data 1 Load influence coefficient K corresponding to weather data 2 Load influence coefficient K corresponding to holiday data 3 Load influence coefficient T corresponding to time of day n Load influence coefficient Y corresponding to regional power utilization data and abnormal load data F of power grid system j . Substituting the short-term load predicted value F into an initial prediction model for calculation to obtain a short-term load predicted value F of the power grid 1 。
Optionally, a prediction period may be set, and after load prediction is performed on the power grid by using the initial prediction model, the initial prediction model is corrected and improved on time according to the prediction period. And substituting the historical load data in the database and the load related data corresponding to the historical load data into a calculation formula of the initial prediction model for a plurality of times, wherein the numerical values substituted each time are different. Thus, different daily work daily loads are obtained. And obtaining a final load influence coefficient value through a preset calculation mode by using a plurality of load influence coefficient values corresponding to each load associated data item. The preset calculation method includes, but is not limited to, at least one of averaging, weighted summation, and averaging after removing a highest value and a lowest value, which is not limited in this embodiment.
S240, determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model.
Optionally, after determining the target predicted value of the grid load of the grid system at the time to be predicted, generating and outputting a grid load prediction conclusion of the grid system based on the target predicted value. Specifically, the conclusion includes, but is not limited to, at least one of index name, accuracy of prediction of electricity consumption, index definition, ratio of absolute value of difference between actual electricity consumption and predicted electricity consumption to actual electricity consumption, magnitude to be reached by middle-term inspection, and magnitude to be reached by acceptance inspection.
According to the technical scheme of the embodiment, the corresponding relation between the load influence coefficient corresponding to each item of load associated data and the attribute value of the load associated data in the initial prediction model is determined based on the historical load data in a plurality of time periods; the relation between the attribute values of different load associated data and the load influence coefficient values corresponding to the load associated data can be accurately obtained, and then the load influence coefficients in the initial load prediction model are assigned based on the attribute values of the load associated data corresponding to the time to be predicted and the corresponding relation, so that a target prediction model corresponding to the time to be predicted is obtained. The method can calculate the unknown parameters in the initial coincidence prediction model after assignment to obtain the target prediction value corresponding to the moment to be predicted, solves the problem of lower load prediction accuracy of the power grid, and achieves the beneficial effect of improving the load prediction accuracy.
Example III
Fig. 3 is a schematic structural diagram of an electrical load prediction device according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: a grid data acquisition module 310, a predictive model determination module 320, and a grid load prediction module 330.
The power grid data obtaining module 310 is configured to obtain historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, where the load related data is data related to a change of the load data of the power grid system; the prediction model determining module 320 is configured to determine a target prediction model based on the historical load data, the load-related data, and an initial prediction model, where the initial load prediction model is a model that is pre-constructed based on a plurality of load-related data; the grid load prediction module 330 is configured to determine a target predicted value of the grid load of the grid system based on the target prediction model.
According to the technical scheme, historical load data of a power grid system to be predicted and load related data corresponding to the historical load data and related to the change of the load data of the power grid system are acquired through a power grid data acquisition module; accurately establishing a relation between historical load data of a power grid system to be predicted and load associated data which corresponds to the historical load data and is associated with load data change of the power grid system; then, determining a target prediction model corresponding to the moment to be predicted based on the historical load data, the load related data and an initial prediction model pre-constructed based on a plurality of load related data by a prediction model determining module; establishing a target prediction model capable of accurately predicting load; and finally, determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model through a grid load predicting module. The target prediction value of the power grid load of the power grid system at the moment to be predicted is accurately predicted based on the target prediction model with high accuracy, the problem that the load prediction accuracy of the power grid is low is solved, and the beneficial effect of improving the load prediction accuracy is achieved.
Optionally, the prediction model determining module includes:
a relationship determining unit configured to determine, based on the historical load data in a plurality of time periods, a correspondence between a load influence coefficient corresponding to each item of the load-related data and an attribute value of the load-related data in the initial prediction model;
the target prediction model obtaining unit is used for assigning the load influence coefficient in the initial load prediction model based on the attribute value of the load associated data corresponding to the moment to be predicted and the corresponding relation to obtain a target prediction model corresponding to the moment to be predicted.
Optionally, the load related data includes temperature data, weather data, holiday data, time period of day, regional power consumption data and abnormal load data, and the initial prediction model is:
F 1 ＝F 2 *K 1 *K 2 *K 3 *T n *Y±F j
wherein F is 1 For the grid load of the grid system, Y is daily work load, K 1 For the load influence coefficient corresponding to the temperature data, K 2 K is the load influence coefficient corresponding to weather data 3 For the load influence coefficient corresponding to holiday data, T n For the load influence coefficient corresponding to the time period in one day, Y is the load influence coefficient corresponding to the regional power consumption data, F j And (5) abnormal load data of the power grid system.
Optionally, the apparatus further includes: and a target prediction value determining module.
The target prediction value determining module is configured to determine a model prediction value of a grid load of the grid system based on the target prediction model after determining the target prediction value of the grid load of the grid system based on the target prediction model, and determine the target prediction value of the grid load of the grid system based on the model prediction value.
Optionally, the target prediction value determining module includes:
a first target prediction value determination unit, configured to take the model prediction value as a target prediction value of a grid load of the grid system, or,
and the second target predicted value determining unit is used for determining a target predicted value of the power grid load of the power grid system based on the difference value between the model predicted value and the historical power grid load.
The electric quantity load prediction device further comprises a prediction error determination module and a model updating module.
The prediction error determining module is used for acquiring the actual grid load of the grid system after determining the target predicted value of the grid load of the grid system based on the target prediction model, and determining the prediction error between the actual grid load and the target predicted value;
and the model updating module is used for updating the target prediction model under the condition that the prediction error reaches a preset error condition.
Optionally, the load related data includes at least one of environmental data, time data, regional power consumption data and abnormal load data, wherein the environmental data includes temperature data and/or weather data; the time data includes holiday data and/or a preset time period of the day.
The electric quantity load prediction device provided by the embodiment of the invention can execute the electric quantity load prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as method electrical load prediction.
In some embodiments, the method electrical load prediction may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more of the steps of the method power load prediction described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method power load prediction in any other suitable way (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method of predicting electrical load, comprising:
acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data is data related to the change of the load data of the power grid system;
determining a target prediction model corresponding to a moment to be predicted based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data;
and determining a target predicted value of the grid load of the grid system at the moment to be predicted based on the target predicted model.
2. The method of claim 1, wherein the determining a target prediction model corresponding to a time instant to be predicted based on the historical load data, the load-related data, and an initial prediction model comprises:
based on the historical load data in a plurality of time periods, determining the corresponding relation between the load influence coefficient corresponding to each item of load related data and the attribute value of the load related data in the initial prediction model;
and assigning the load influence coefficient in the initial load prediction model based on the attribute value of the load associated data corresponding to the moment to be predicted and the corresponding relation to obtain a target prediction model corresponding to the moment to be predicted.
3. The method of claim 2, wherein the load-related data includes temperature data, weather data, holiday data, time of day, regional power usage data, and abnormal load data, and wherein the initial predictive model is:
F 1 ＝F 2 *K 1 *K 2 *K 3 *T n *Y±F j
wherein F is 1 For the grid load of the grid system, F 2 For daily work load, K 1 For the load influence coefficient corresponding to the temperature data, K 2 K is the load influence coefficient corresponding to weather data 3 For the load influence coefficient corresponding to holiday data, T n For the load influence coefficient corresponding to the time period in one day, Y is the load influence coefficient corresponding to the regional power consumption data, F j And (5) abnormal load data of the power grid system.
4. The method of claim 1, further comprising, after the determining a target predicted value of a grid load of the grid system based on the target prediction model:
and determining a model predictive value of the power grid load of the power grid system based on the target predictive model, and determining the target predictive value of the power grid load of the power grid system based on the model predictive value.
5. The method of claim 4, wherein the determining a target forecast value of a grid load of the grid system based on the model forecast value comprises:
the model predictive value is taken as a target predictive value of the power grid load of the power grid system, or,
a target predicted value of the grid load of the grid system is determined based on a difference between the model predicted value and the historical grid load.
6. The method of claim 1, further comprising, after said determining a target forecast value of a grid load of said grid system based on said target forecast model,
acquiring an actual power grid load of the power grid system, and determining a prediction error between the actual power grid load and the target predicted value;
and under the condition that the prediction error reaches a preset error condition, updating the target prediction model.
7. The method of claim 1, wherein the load-related data comprises at least one of environmental data, time data, area electricity data, and abnormal load data, wherein the environmental data comprises temperature data and/or weather data; the time data includes holiday data and/or a preset time period of the day.
8. An electrical load prediction apparatus, comprising:
the power grid data acquisition module is used for acquiring historical load data of a power grid system to be predicted and load related data corresponding to the historical load data, wherein the load related data are data related to the change of the load data of the power grid system;
the prediction model determining module is used for determining a target prediction model based on the historical load data, the load related data and an initial prediction model, wherein the initial load prediction model is a model pre-constructed based on a plurality of load related data;
and the power grid load prediction module is used for determining a target predicted value of the power grid load of the power grid system based on the target prediction model.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the electrical load prediction method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the electrical load prediction method of any one of claims 1-7 when executed.
CN202310441723.4A 2023-04-23 2023-04-23 Electric quantity load prediction method and device, electronic equipment and storage medium Pending CN116470494A (en)

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