CN116805176A - Load prediction method, device and equipment for transformer area and storage medium - Google Patents
Load prediction method, device and equipment for transformer area and storage medium Download PDFInfo
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Abstract
The invention discloses a load prediction method, device and equipment for a platform area and a storage medium. The method comprises the following steps: acquiring target original data of a target area to be predicted; the target original data at least comprises a target load prediction curve; processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period; determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period; determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period; and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period. According to the technical scheme, the problem of under fitting caused by insufficient data of the platform area is solved, and the prediction accuracy is improved.
Description
Technical Field
The present invention relates to the field of load prediction technologies of a platform, and in particular, to a method, an apparatus, a device, and a storage medium for predicting a load of a platform.
Background
With the increasing importance of distribution network and the development and maturation of various technologies in the power industry, the work of predicting the load of a platform is an important work of power system distribution network dispatching, and the load prediction is the basis of work such as power system planning, planning and dispatching operation.
At present, the load prediction of the platform area is mainly based on a traditional prediction method, the complexity of a model is low, only the data of a single platform area can be considered, important information of adjacent or similar platform areas cannot be effectively utilized, and the data distribution of each platform area is not completely the same, so that the model construction pressure is high. And, the prediction method cannot fully utilize the load data of each zone, so that the zone load prediction effect is reduced.
Disclosure of Invention
The embodiment of the invention provides a load prediction method, device and equipment for a platform area and a storage medium, so as to improve prediction accuracy.
According to an aspect of the present invention, there is provided a load prediction method for a zone, including:
acquiring target original data of a target area to be predicted in the working process; wherein the target raw data at least comprises a target load prediction curve;
Processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period;
determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period;
determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period;
determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period;
and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period.
According to another aspect of the present invention, there is provided a load prediction apparatus for a zone, including:
the original data module is used for acquiring target original data of the target area to be predicted in the working process; wherein the target raw data at least comprises a target load prediction curve;
the target load period module is used for processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period;
The public load period module is used for determining whether the candidate load period of the target station area is the public load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period;
the public feature extraction module is used for determining a feature extraction result of the public load period according to the auxiliary load feature and the target load feature of the public load period;
the independent characteristic extraction module is used for determining characteristic extraction results of the independent load periods according to target load characteristics of the independent load periods;
and the load prediction module is used for predicting the load of the target platform area according to the characteristic extraction result of the common load period and the characteristic extraction result of the independent load period.
According to another aspect of the present invention, there is provided an electronic apparatus for load prediction of a bay, the electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
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 load prediction for a zone in accordance with 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 implement a load prediction method of a zone according to any of the embodiments of the present invention when executed.
According to the load prediction method, device and equipment for the platform region and the storage medium, target original data of a target platform region to be predicted in a working process are obtained; wherein the target raw data at least comprises a target load prediction curve; processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period; determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period; determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period; determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period; and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period. According to the technical scheme, the candidate load period of the target platform area and the target load characteristic of the candidate load period are obtained by acquiring the periodic characteristic of the target load prediction curve in the target original data, the candidate load period is divided into the public load period and the independent load period, the target load characteristic and the auxiliary load characteristic of the public load period and the target load characteristic of the independent load period are obtained through the characteristic extractor, the characteristic extraction result of the target platform area is determined, and the load of the target platform area is predicted. The problem of under fitting caused by insufficient data of the area is solved, so that the prediction accuracy is improved.
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 flowchart of a method for predicting load of a zone according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for predicting load of a station according to a second embodiment of the present invention;
fig. 3 is a flowchart of a method for predicting load of a zone according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a load prediction apparatus for a platform according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a load prediction apparatus for a transformer area according to a fifth 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.
In addition, it should be noted that, in the technical scheme of the invention, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the data to be processed all conform to the regulations of the related laws and regulations and do not violate the popular regulations.
Example 1
Fig. 1 is a flowchart of a method for predicting a load of a platform according to an embodiment of the present invention, where the method may be applied to a situation of predicting a load of a platform in an electric power system, and the method may be performed by a load predicting device of a platform, where the load predicting device of a platform may be implemented in a form of hardware and/or software, and the load predicting device of a platform may be configured in a computer device, where the computer device may be a notebook, a desktop computer, an intelligent tablet, or the like.
As shown in fig. 1, the method for predicting the load of the area provided in the first embodiment specifically includes the following steps:
s101, acquiring target original data of a target area to be predicted in a working process; wherein the target raw data includes at least a target load prediction curve.
The transformer area is an important component in the power system, and refers to a power supply range or a power supply area of the transformer. By way of example, the transformer area can reduce the high-voltage electric energy of the transformer substation to the low-voltage electric energy used in the place, and distribution and comprehensive management of the power supply lines are completed. The target load prediction curve is a curve for predicting the load at the future moment by utilizing the load history characteristic of the platform region, and reflects the change rule of the load of the platform region along with time in a period of time. The target load prediction curve is divided into a daily load curve and a annual load curve, wherein the daily load curve is a curve for changing the load with time within one day, and the annual load curve represents the change of the load day by day (or ten days or months) from the beginning of the year to the end of the year.
Specifically, target original data of a target area to be predicted in a working process is obtained, for example, the target original data is collected through a data base, and data preprocessing and target original data dividing are carried out on the target original data. The data base is a framework for storing the platform region load data, is used for uniformly managing the platform region data and is used for collecting target original data.
Specifically, the data preprocessing of the target original data includes the following steps: data cleaning is carried out on the target original data; performing data conversion on the cleaned data; carrying out normalization processing after data conversion; and carrying out data division on the target original data.
The data cleaning is the process of rechecking and checking the data, and cleaning, denoising and filling the original data. For example, in the original data, due to the problem of equipment failure or communication failure in the process of collecting the load data of the area, the collected data may have errors or deletions, so that invalid data, abnormal values and noise data are removed, and filling is performed according to a filling or mean filling method, so as to reduce the interference on prediction. Data conversion is a process of converting data from one form to another, and there are logarithmic conversion and exponential conversion. Illustratively, the target raw data is a sequence, each element in the sequence being converted to logarithmic form. The normalization process is to convert the dimensionalized data into dimensionless data. Illustratively, the normalization may be (0, 1) normalization. The data is divided into a process of partitioning the data so as to facilitate training, parameter tuning and evaluation of the model. Illustratively, the data is divided into a training set, a test set, and a validation set.
S102, processing the target load prediction curve to obtain a candidate load period of the target area and a target load characteristic of the candidate load period.
In the embodiment of the disclosure, the target load prediction curve is affected by different factors, has periodicity, and determines the period with the same periodicity rule as the candidate load period of the target platform region according to the periodicity rule of the target load prediction curve. The target load characteristic is a load characteristic of the area corresponding to the area load data in the candidate load period, and for example, different candidate load periods with lengths of 7, 15 and 30 can be preset respectively. Specifically, the target load characteristics of the candidate load periods are obtained through the characteristics of the target original data in each candidate load period.
S103, determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; and if not, taking the candidate load period as an independent load period.
The common load period is the period length shared by the target station area and other station areas. The auxiliary station area is other station areas with a common load period outside the target station area. The auxiliary load characteristic is a load characteristic corresponding to the auxiliary area. The independent load period is a load period divided by the common load period in the candidate load period.
In the embodiment of the disclosure, the target station area and other station areas have respective candidate load periods. The candidate load periods for the other zones and other load characteristics of the candidate load periods may be determined as follows: auxiliary original data of other areas in the working process are obtained; wherein the auxiliary raw data at least comprises an auxiliary load prediction curve; and processing the auxiliary load curve to obtain candidate load periods of other areas and auxiliary load characteristics of the candidate load periods.
And aiming at each other station area, if the other station area and the target station area have the same candidate load period, taking the other station area as an auxiliary station area of the target station area, taking the same candidate load period as a common load period, and determining auxiliary load characteristics of the auxiliary station area in the common load period. And if the candidate load period of the auxiliary station area or the candidate load period of the target station area does not belong to the common load period, taking the candidate load period as an independent load period. Illustratively, the candidate load period lengths of the first station area are preset to be 7, 15 and 30, the candidate load period lengths of the second station area are preset to be 7, 15 and 90, the common load period is 7 and 15, and the independent load period is 30 and 90.
S104, determining a characteristic extraction result of the common load period according to the auxiliary load characteristic and the target load characteristic of the common load period.
S105, determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period.
Specifically, the characteristic extraction result of the public load period is determined by the auxiliary load characteristic and the target load characteristic of the public load period, the source of the target load characteristic of the public load period and the target platform area are determined, and the auxiliary load characteristic of the public load period is derived from the auxiliary platform area, so that the characteristic extraction result of the public load period fuses the load characteristics of the auxiliary platform area and the target platform area, and the load data of the target platform area and the auxiliary platform area are combined. Further, the feature extraction result of the independent load period is determined by the target load feature of the independent load period.
S106, carrying out load prediction on the target station area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period.
Specifically, the characteristic extraction result of the common load period and the characteristic extraction result of the independent load period are input into a predictor, and load prediction is carried out on the target station area. Feature extraction results of different feature extractors are fused into a result which can be recognized by a predictor through feature fusion. Specifically, the feature extraction result of the public load period and the feature extraction result of the independent load period are integrated, the feature extraction result of the public load period is regarded as a channel, the number of channels is increased, the feature extraction result of the independent load period is stored, all the channels are combined, the number of feature extraction results of the feature extractor is increased, and the feature result which can be identified by the predictor is determined. And inputting the feature extraction result into a predictor, and performing model training on the input result through the predictor to obtain a load prediction result. The predictor can be a BP neural network, parameters of the model are adjusted in the model training process, and characteristic parameters of load data are trained and fed back to obtain a predictor model.
In the process of determining the load characteristics, a proper characteristic optimization strategy is adopted to have an effect on the load prediction of the platform region, and the characteristics with potential prediction capability are selected from the target original data through statistical screening and model screening. The statistical screening is a statistical screening method, and uses statistics and correlation analysis to screen, wherein the statistical screening comprises pearson correlation coefficient, mutual information, chi-square test and analysis of variance index, load characteristics of original data are ordered according to index values, and the first characteristics are taken as input. Model screening is a method for screening by using a model, and load characteristics are input into candidate models for screening. After parallel screening of statistical screening and model screening, taking intersection of statistical screening and model screening results as preferable characteristics of load characteristics.
According to the method, the target original data of the target area to be predicted in the working process are obtained; wherein the target raw data at least comprises a target load prediction curve; processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period; determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period; determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period; determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period; and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period. According to the technical scheme, periodic characteristics of a target load prediction curve in target original data are obtained, candidate load periods of a target platform area and the target load characteristics of the candidate load periods are obtained, the candidate load periods are divided into a public load period and an independent load period, the target load characteristics, auxiliary load characteristics and the target load characteristics of the independent load period of the public load period are obtained through a characteristic extractor, a characteristic extraction result of the target platform area is determined, and load of the target platform area is predicted. The problem of under fitting caused by insufficient data of the area is solved, so that the prediction accuracy is improved.
Example two
Fig. 2 is a flowchart of a method for predicting load of a platform according to a second embodiment of the present invention, which is a further refinement of the foregoing embodiments. As shown in fig. 2, the method comprises the steps of:
s201, acquiring target original data of a target area to be predicted in a working process; wherein the target raw data includes at least a target load prediction curve.
S202, processing the target load prediction curve to obtain a candidate load period of the target platform area and target load characteristics of the candidate load period.
S203, determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, the candidate load period is taken as an independent load period.
S204, carrying out feature extraction on the load features of the auxiliary station areas of the target station areas in the public load period through a public feature extractor, and obtaining feature extraction results in the public load period.
The feature extractor is a learning model for extracting target load features and auxiliary load feature extraction results. The load signature is an auxiliary load signature and a target load signature of the common load cycle.
Specifically, the common feature extractor corresponds to the common load periods one by one, and for each common load period, one feature extractor of the period is built for the common load period. And inputting the auxiliary load characteristics of the auxiliary station areas in the common load period into a common characteristic extractor to obtain characteristic extraction results of the auxiliary station areas in the common load period. And determining the characteristic extraction result of the common load period according to the characteristic extraction result of the auxiliary load characteristic of the auxiliary station area and the characteristic extraction result of the target load characteristic of the target station area.
S205, determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period.
S206, carrying out load prediction on the target station area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period.
According to the technical scheme of the embodiment, the common characteristic extractor is used for extracting the target load characteristic and the auxiliary load characteristic of the common load period, so that the characteristic extraction result on the common load period is obtained, the commonality information between stations is fully considered, and the common characteristic extractor can better complete the characteristic extraction work.
In an alternative embodiment, the method further comprises: the dimension transformation is carried out on the target load characteristics of the candidate load period by the following modes:
(X,Y)＝(T,T×N/T)；
Where (X, Y) is the target load characteristic after transformation, T is the length of the candidate load period, and N is the length of the target load characteristic before transformation.
Specifically, the target load characteristic form is a one-dimensional characteristic sequence, and the one-dimensional characteristic sequence is remodeled into a two-dimensional matrix form according to the candidate load period. For example, if the target load characteristic length is 365 and the period is 7, the converted two-dimensional matrix is (X, Y) = (7, 7×365/7) or (X, Y) = (7×365/7, 7) according to the above manner, and the present embodiment is not limited thereto.
The embodiment enables the common feature extractor to complete feature extraction by explicitly introducing the period information of the target load feature.
Example III
Fig. 3 is a flowchart of a method for predicting load of a platform according to a third embodiment of the present invention, which is further refined in the foregoing embodiments. As shown in fig. 3, the method comprises the steps of:
s301, acquiring target original data of a target area to be predicted in a working process; wherein the target raw data includes at least a target load prediction curve.
S302, carrying out Fourier transform on a target load prediction curve to obtain the amplitude of a frequency domain of the target load curve; and selecting a candidate load period for the target platform region according to the amplitude of the frequency domain of the target load curve.
Specifically, the target load prediction curve is converted into load prediction curve data of a frequency domain by Fourier transformation, and the target load prediction curve is converted into superposition of sinusoidal curves by Fourier transformation, so that the amplitude of the load prediction curve data of the frequency domain is obtained. And selecting a period corresponding to the frequency with high amplitude as a candidate load period of the target load prediction curve according to the amplitude of the load prediction curve data of the frequency domain. The target load prediction curve is subjected to Fourier transformation to obtain a sinusoidal curve, the peak of the sinusoidal curve is a place with high amplitude, and the candidate load period of the target platform area is obtained through the frequency corresponding to the high amplitude.
S303, determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, the candidate load period is taken as an independent load period.
S304, determining a characteristic extraction result of the common load period according to the auxiliary load characteristic and the target load characteristic of the common load period.
S305, determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period.
S306, carrying out load prediction on the target station area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period.
In an alternative embodiment, determining a time feature of the target area, and performing load prediction on the target area according to a feature extraction result of the common load period and a feature extraction result of the independent load period, where the method includes:
determining the time characteristics of a target station area; determining the zone characteristics of the target zone according to the characteristic extraction result of the public load period, the characteristic extraction result and the time characteristic of the independent load period; and inputting the zone characteristics of the target zone into a load predictor of the target zone to obtain a load prediction result of the target zone.
The characteristics of the platform region are characteristics of platform region load prediction data of target original data, and the characteristics comprise time characteristics, weather characteristics, holiday characteristics and historical load characteristics, wherein the weather characteristics, holiday characteristics and historical load characteristics are characteristics contained in a target load prediction curve.
Specifically, weather features are weather conditions and weather phenomena within a period of time, and the weather features can influence the electricity consumption behavior of the transformer area. And (3) carrying out data cleaning treatment on the weather features, and extracting the weather features of temperature, humidity, air pressure and wind direction. For the weather characteristic data with granularity of day, the directly extracted weather characteristics of temperature, humidity, air pressure and wind direction, and for the weather characteristic data with granularity smaller than that of day, such as the temperature value of each hour, the statistical characteristics of average value, maximum value, deviation value and the like of the characteristic of each day are further processed and extracted to be used as new characteristics. Holiday characteristics are date characteristics of the load data of the platform, and population flow in the holiday period influences electricity utilization behaviors of the platform. And coding different holiday characteristics through binary variables, and calculating the time difference before and after the holiday, wherein the time difference specifically comprises whether the previous day is the holiday or not and the number of days from the next holiday. The load forecasting result of the target platform area is obtained through inputting the platform area characteristics of the target platform area into the load forecasting device of the target platform area and training of the forecasting device.
In an alternative embodiment, determining the temporal characteristics of the target zone includes: and (3) periodically encoding the time feature to obtain periodic discrete features of the time feature.
Specifically, the time characteristic is a continuous periodic characteristic, the original timestamp is converted into a discrete characteristic of year, month, day and week by a periodic coding mode according to a sine function and a cosine function, two new values are obtained, the two new values are spliced into a vector to be used as periodic discrete codes, and the periodic discrete characteristic of the time characteristic is obtained, wherein the formula is as follows:
wherein t is 0 As the reference time, T is the current time, T is the candidate load period, and X (T) is the periodic discrete code of the time feature. Illustratively, taking month as an example, the value range isFrom 1 month to 12 months, the month is encoded as a vector. First subtracting a reference value, for example 6 months, with a reference value of 6, to obtain a month centered at 0, and dividing this month by a period, i.e. 12, to obtain a value centered at 0 and ranging from-1 to 1. Then substituting the value into a sine function and a cosine function respectively to obtain two new values which respectively represent the positions of the year in the period. Finally, the two values are spliced into a vector to be used as the month code.
According to the method, candidate load periods are determined through Fourier transformation of the target load prediction curve, and the area characteristics of the target area are determined through the characteristic extraction result of the common load period, the characteristic extraction result and the time characteristics of the independent load period, so that a data basis is provided for load prediction of the predictor.
Example IV
Fig. 4 is a schematic structural diagram of a load prediction device for a platform according to a fourth embodiment of the present invention, where the present embodiment may be used in the case of predicting a load of a platform. The device can realize the load prediction method of the platform area, is executed in a software and/or hardware mode, and can be integrated in a system with terminal equipment.
As shown in fig. 4, the load prediction apparatus for a station area disclosed in this embodiment includes: a raw data module 401, a target duty cycle module 402, a common duty cycle module 403, a common feature extraction module 404, an independent feature extraction module 405, and a load prediction module 406.
The original data module 401 is configured to obtain target original data of a target area to be predicted in a working process; wherein the target raw data includes at least a target load prediction curve.
And the target load period module 402 is configured to process the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period.
A common load period module 403, configured to determine whether a candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, the candidate load period is taken as an independent load period.
The common feature extraction module 404 is configured to determine a feature extraction result of the common load period according to the auxiliary load feature and the target load feature of the common load period.
The independent feature extraction module 405 is configured to determine a feature extraction result of the independent load period according to the target load feature of the independent load period.
And the load prediction module 406 is configured to perform load prediction on the target area according to the feature extraction result of the common load period and the feature extraction result of the independent load period.
According to the technical scheme, target original data of the target area to be predicted in the working process are obtained; wherein the target raw data at least comprises a target load prediction curve; processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period; determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period; determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period; determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period; and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period. According to the technical scheme, through mutual coordination among the modules, the periodic characteristics of the target load prediction curve in the target original data are obtained, the candidate load period of the target platform area and the target load characteristics of the candidate load period are obtained, the candidate load period is divided into a public load period and an independent load period, the target load characteristics of the public load period, the auxiliary load characteristics and the target load characteristics of the independent load period are obtained through the characteristic extractor, the characteristic extraction result of the target platform area is determined, and the load of the target platform area is predicted. The problem of under fitting caused by insufficient data of the area is solved, so that the prediction accuracy is improved.
Optionally, the common feature extraction module 404 includes:
and the public load extraction unit is used for carrying out characteristic extraction on the load characteristics of the auxiliary station area of the target station area in the public load period through a public characteristic extractor, so as to obtain a characteristic extraction result in the public load period.
Optionally, the target duty cycle module 402 includes:
the curve Fourier transform unit is used for carrying out Fourier transform on the target load prediction curve to obtain the amplitude of the frequency domain of the target load curve;
and the candidate load period unit is used for selecting a candidate load period for the target platform area according to the amplitude of the frequency domain of the target load curve.
Optionally, the load prediction module 406 includes:
and the characteristic determining unit is used for determining the time characteristic of the target station area.
The platform region characteristic unit is used for determining the platform region characteristics of the target platform region according to the characteristic extraction result of the public load period, the characteristic extraction result and the time characteristic of the independent load period.
The load prediction unit is used for inputting the platform region characteristics of the target platform region into the load predictor of the target platform region to obtain a load prediction result of the target platform region.
Optionally, the time feature determining unit specifically includes:
And (3) periodically encoding the time feature to obtain periodic discrete features of the time feature.
Optionally, the device for predicting the load of the platform area further comprises:
the feature transformation module performs dimension transformation on the target load features of the candidate load period in the following manner:
(X,Y)＝(T,T×N/T)；
where (X, Y) is the target load characteristic after transformation, T is the length of the candidate load period, and N is the length of the target load characteristic before transformation.
The device for predicting the load of the platform area provided by the embodiment of the invention can execute the method for predicting the load of the platform area provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example five
Fig. 5 is a schematic structural diagram of a load prediction device for a transformer area according to a fifth embodiment of the present invention. The load prediction device 50 of a bay is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable 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. 5, the load predicting device 50 of the bay includes at least one processor 51, and a memory such as a Read Only Memory (ROM) 52, a Random Access Memory (RAM) 53, etc. communicatively connected to the at least one processor 51, wherein the memory stores a computer program executable by the at least one processor, and the processor 51 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 52 or the computer program loaded from the storage unit 58 into the Random Access Memory (RAM) 53. In the RAM53, various programs and data required for the operation of the electronic device 50 can also be stored. The processor 51, ROM52 and RAM53 are connected to each other by a bus 55. An input/output (I/O) interface 55 is also connected to bus 55.
A plurality of components in the load predicting apparatus 50 of the bay are connected to the I/O interface 55, including: an input unit 56 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 58 such as a magnetic disk, an optical disk, or the like; and a communication unit 59 such as a network card, modem, wireless communication transceiver, etc. The communication unit 59 allows the electronic device 50 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks.
The processor 51 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 51 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 51 performs the respective methods and processes described above, for example, a load prediction method of a station area.
In some embodiments, the load prediction method of a zone may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 58. In some embodiments, part or all of the computer program may be loaded and/or installed onto the load predicting device 50 of the bay via the ROM52 and/or the communication unit 59. When the computer program is loaded into RAM53 and executed by processor 51, one or more steps of the method of load prediction of a method bay described above may be performed. Alternatively, in other embodiments, processor 51 may be configured to perform the load prediction method of the zone in any other suitable manner (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 for predicting load of a station, comprising:
acquiring target original data of a target area to be predicted in the working process; wherein the target raw data at least comprises a target load prediction curve;
processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period;
determining whether the candidate load period of the target station area is a common load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period;
Determining a characteristic extraction result of the public load period according to the auxiliary load characteristic and the target load characteristic of the public load period;
determining a characteristic extraction result of the independent load period according to the target load characteristic of the independent load period;
and carrying out load prediction on the target platform area according to the characteristic extraction result of the public load period and the characteristic extraction result of the independent load period.
2. The method of claim 1, wherein determining the signature extraction result for the common load cycle based on the auxiliary load signature and the target load signature for the common load cycle comprises:
and carrying out feature extraction on the load features of the auxiliary station areas of the target station areas in the public load period by using a public feature extractor, so as to obtain feature extraction results on the public load period.
3. The method according to claim 1, wherein the method further comprises:
performing dimension transformation on the target load characteristics of the candidate load period by the following steps:
(X，Y)＝(T，T×N/T)；
where (X, Y) is the target load characteristic after transformation, T is the length of the candidate load period, and N is the length of the target load characteristic before transformation.
4. The method of claim 1, wherein processing the target load prediction curve to obtain candidate load cycles for a target zone comprises:
Performing Fourier transform on the target load prediction curve to obtain the amplitude of the frequency domain of the target load curve;
and selecting a candidate load period for the target platform region according to the amplitude of the frequency domain of the target load curve.
5. The method of claim 4, wherein determining the temporal characteristics of the target zone comprises:
and periodically encoding the time feature to obtain a periodic discrete feature of the time feature.
6. The method of claim 1, wherein the load prediction for the target site based on the feature extraction result of the common load period and the feature extraction result of the independent load period comprises:
determining the time characteristics of a target station area;
determining the zone characteristics of the target zone according to the characteristic extraction result of the public load period, the characteristic extraction result and the time characteristic of the independent load period;
and inputting the region characteristics of the target region into a load predictor of the target region to obtain a load prediction result of the target region.
7. A load prediction apparatus for a station, comprising:
the original data module is used for acquiring target original data of the target area to be predicted in the working process; wherein the target raw data at least comprises a target load prediction curve;
The target load period module is used for processing the target load prediction curve to obtain a candidate load period of the target platform area and a target load characteristic of the candidate load period;
the public load period module is used for determining whether the candidate load period of the target station area is the public load period between the target station area and other station areas; if yes, taking other areas as auxiliary areas, and determining auxiliary load characteristics of the auxiliary areas in a common load period; if not, taking the candidate load period as an independent load period;
the public feature extraction module is used for determining a feature extraction result of the public load period according to the auxiliary load feature and the target load feature of the public load period;
the independent characteristic extraction module is used for determining characteristic extraction results of the independent load periods according to target load characteristics of the independent load periods;
and the load prediction module is used for predicting the load of the target platform area according to the characteristic extraction result of the common load period and the characteristic extraction result of the independent load period.
8. The apparatus of claim 7, wherein the load prediction module comprises:
the feature determining unit is used for determining the time feature of the target station area;
The platform region characteristic unit is used for determining the platform region characteristics of the target platform region according to the characteristic extraction result of the public load period, the characteristic extraction result and the time characteristic of the independent load period;
the load prediction unit is used for inputting the platform region characteristics of the target platform region into a load predictor of the target platform region to obtain a load prediction result of the target platform region.
9. A load prediction apparatus of a station, characterized in that the load prediction apparatus of a station comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
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 load prediction of a bay as claimed in any one of claims 1 to 6.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the load prediction method of a bay as claimed in any one of claims 1 to 6 when executed.
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