CN116883033A - Medium-and-long-term electricity price prediction method and device, storage medium and electronic equipment - Google Patents

Medium-and-long-term electricity price prediction method and device, storage medium and electronic equipment Download PDF

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CN116883033A
CN116883033A CN202310673819.3A CN202310673819A CN116883033A CN 116883033 A CN116883033 A CN 116883033A CN 202310673819 A CN202310673819 A CN 202310673819A CN 116883033 A CN116883033 A CN 116883033A
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historical
target
price
intercept
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张欣
孙金龙
郑凯
干瑞
王承凯
李赟
史明亮
杜栋
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Guohua Energy Investment Co ltd
New Energy Co Ltd of China Energy Investment Corp Ltd
Shanghai Envision Innovation Intelligent Technology Co Ltd
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Guohua Energy Investment Co ltd
New Energy Co Ltd of China Energy Investment Corp Ltd
Shanghai Envision Innovation Intelligent Technology Co Ltd
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Abstract

The present disclosure relates to a medium-long term electricity price prediction method, a device, a storage medium, and an electronic apparatus, the medium-long term electricity price prediction method including: acquiring a plurality of pieces of historical month price information, wherein the historical month price information comprises a plurality of historical month price points corresponding to each historical month; obtaining a historical month slope and a historical month intercept corresponding to each historical month according to a plurality of historical month price points corresponding to each historical month; obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month; and obtaining a price mapping relation corresponding to the target month according to the target month price point, and obtaining the electric value corresponding to the target month by combining the bidding space value corresponding to the target month.

Description

Medium-and-long-term electricity price prediction method and device, storage medium and electronic equipment
Technical Field
The disclosure relates to the technical field of electricity price prediction, in particular to a medium-and-long-term electricity price prediction method, a medium-and-long-term electricity price prediction device, a storage medium and electronic equipment.
Background
The electricity price prediction has important significance for formulating a reasonable quotation strategy, maintaining the safety and stability of the electric power market and improving the economical efficiency of the system operation. With the continuous advancement of the electric power market reform, higher requirements are put on the accuracy of electricity price prediction.
The medium-and-long-term power trading is an important link in the current power market, and accurate medium-and-long-term power price prediction can enable both power generation and power consumption to accurately grasp future power price trend, and can make decisions to trade power quantity and power price, reasonably benefit and avoid loss. However, most of the electricity price predictions in the related art are short-term node electricity price predictions, and medium-long term electricity price prediction techniques are missing, so that high-precision medium-long term electricity price prediction techniques are needed in the market at present.
Disclosure of Invention
The disclosure aims to provide a medium-and-long-term electricity price prediction method, a medium-and-long-term electricity price prediction device, a storage medium and electronic equipment, so as to solve the problems in the related art.
To achieve the above object, according to a first aspect of embodiments of the present disclosure, the present disclosure provides a medium-to-long term electricity price prediction method, including:
acquiring a plurality of pieces of historical month price information, wherein the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electric value;
obtaining a historical month slope and a historical month intercept corresponding to each historical month according to the plurality of historical month price measuring points corresponding to each historical month;
obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month;
obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month;
obtaining a corresponding price mapping relation of the target month according to the target month price point;
and obtaining the electric value corresponding to the target month according to the price quantity mapping relation corresponding to the target month and the bidding space value corresponding to the target month.
Optionally, the obtaining the historical month slope and the historical month intercept corresponding to each historical month according to the plurality of historical month price measuring points corresponding to each historical month includes:
and fitting the plurality of historical month price measuring points corresponding to each historical month by a least square method to obtain a historical month slope and a historical month intercept corresponding to each historical month.
Optionally, the obtaining the historical month slope and the historical month intercept corresponding to each historical month according to the plurality of historical month price measuring points corresponding to each historical month includes:
fitting the plurality of historical month price measuring points corresponding to each historical month by a least square method to obtain a historical month slope corresponding to each historical month;
obtaining a target month slope corresponding to the target month according to the history month slope corresponding to the history month;
and obtaining the historical month intercept corresponding to each historical month according to the target month slope and the plurality of historical month price measuring points corresponding to each historical month.
Optionally, the obtaining the target month slope corresponding to the target month according to the history month slope corresponding to the history month includes:
and calculating the average value of the historical month slopes corresponding to all the historical months to obtain the target month slope corresponding to the target month.
Optionally, the obtaining the target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month, and the bid space values of the plurality of historical month price measuring points corresponding to each historical month includes:
calculating the average value of bidding space values of a plurality of historical month price measuring points corresponding to each historical month to obtain the bidding space average value corresponding to each historical month;
obtaining a mapping relation between the intercept and the bidding space average value according to the historical month intercept corresponding to each historical month and the bidding space average value corresponding to each historical month;
averaging the bidding space values corresponding to the target month to obtain the bidding space average value corresponding to the target month;
and obtaining the target month intercept corresponding to the target month according to the mapping relation between the intercept and the bidding space mean value corresponding to the target month.
Optionally, the obtaining the target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept, and the plurality of historical month price points corresponding to each historical month includes:
calculating the difference value of the historical month intercept corresponding to each historical month and the target month intercept to obtain the intercept offset corresponding to each historical month;
and obtaining a target month price point corresponding to the target month according to the intercept variable corresponding to each history month and the plurality of history month price points corresponding to each history month.
Optionally, the obtaining the target month price point corresponding to the target month according to the intercept variable corresponding to each historical month and the plurality of historical month price points corresponding to each historical month includes:
and increasing intercept variables corresponding to the corresponding historical months for bidding space values in a plurality of historical month price points corresponding to each historical month, wherein the electric value in the plurality of historical month price points corresponding to each historical month is unchanged, and obtaining a target month price point corresponding to the target month.
According to a second aspect of the embodiments of the present disclosure, there is provided a medium-to-long term electricity price prediction apparatus including:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is configured to acquire a plurality of pieces of historical month price information, the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electricity value;
the first processing module is configured to obtain a history month slope and a history month intercept corresponding to each history month according to the plurality of history month price measuring points corresponding to each history month;
the second processing module is configured to obtain a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the history month intercept corresponding to each history month and the bid space values of a plurality of history month price measuring points corresponding to each history month;
the third processing module is configured to obtain a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month;
the fourth processing module is configured to obtain a corresponding price mapping relation of the target month according to the target month price point;
and the fifth processing module is configured to obtain the electric value corresponding to the target month according to the bid space value corresponding to the target month and the bid mapping relation corresponding to the target month.
According to a third aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the medium-to-long term electricity price prediction method provided by the first aspect of the present disclosure.
According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the medium-long term electricity price prediction method provided by the first aspect of the present disclosure.
By adopting the technical scheme, the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electricity value; obtaining a historical month slope and a historical month intercept corresponding to each historical month according to a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month; and according to the target month price point, obtaining a price mapping relation corresponding to the target month, and then combining the bidding space value corresponding to the target month to obtain the electricity price value corresponding to the target month, so as to realize the prediction of the electricity price of the whole month, thereby completing the medium-and-long-term electricity price prediction.
Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:
fig. 1 is a flowchart illustrating a medium-to-long term electricity price prediction method according to an exemplary embodiment.
FIG. 2 is a schematic diagram illustrating a monthly quantitative relationship, according to an exemplary embodiment.
Fig. 3 is a flow chart illustrating sub-steps of step S2 of fig. 1, according to an exemplary embodiment.
Fig. 4 is a flow chart illustrating sub-steps of step S3 of fig. 1, according to an exemplary embodiment.
Fig. 5 is a flow chart illustrating sub-steps of step S4 of fig. 1, according to an exemplary embodiment.
Fig. 6 is a block diagram illustrating a medium-to-long term electricity price prediction apparatus according to an exemplary embodiment.
Fig. 7 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
Specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the disclosure, are not intended to limit the disclosure.
In the following description, the words "first," "second," and the like are used merely for distinguishing between the descriptions and not for indicating or implying a relative importance or order.
The electricity price prediction has important significance for formulating a reasonable quotation strategy, maintaining the safety and stability of the electric power market and improving the economical efficiency of the system operation. With the continuous advancement of the electric power market reform, higher requirements are put on the accuracy of electricity price prediction.
The medium-and-long-term power trading is an important link in the current power market, and accurate medium-and-long-term power price prediction can enable both power generation and power consumption to accurately grasp future power price trend, and can make decisions to trade power quantity and power price, reasonably benefit and avoid loss. However, most of the electricity price predictions in the related art are short-term node electricity price predictions, and medium-long term electricity price prediction techniques are missing, so that high-precision medium-long term electricity price prediction techniques are needed in the market at present.
In order to solve the technical problems, the inventor finds that a strong correlation exists between a monthly electricity value and a monthly bidding space value, the bidding space value represents the power supply amount of the thermal power generating unit, and the bidding space value is the difference between the power supply demand amount and the power supply amount except for thermal power. The present disclosure provides a medium-and-long-term electricity price prediction method, by acquiring a plurality of historical month price information, the historical month price information includes a plurality of historical month price points corresponding to each historical month, the price points include a bidding space value and an electricity value; obtaining a historical month slope and a historical month intercept corresponding to each historical month according to a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month; and according to the target month price point, obtaining a price mapping relation corresponding to the target month, and then combining the bidding space value corresponding to the target month to obtain the electricity price value corresponding to the target month, so as to realize the prediction of the electricity price of the whole month, thereby completing the medium-and-long-term electricity price prediction.
Fig. 1 is a flowchart illustrating a medium-to-long term electricity rate prediction method according to an exemplary embodiment, which may be applied to an electronic device, and which predicts an electricity rate value of a target month by combining bid space values of the target month through a plurality of historical month rate information, as shown in fig. 1, and may include steps S1 to S6:
step S1, acquiring a plurality of pieces of historical month price information, wherein the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electric value.
The plurality of historical month price information characterizes a plurality of historical month price points corresponding to each of a plurality of historical months, and the price points comprise a bidding space value and an electric value.
For example, referring to fig. 2, the history month is 1 month to 8 months, the target month is 9 months, the plurality of price points corresponding to 1 month to 7 months are not shown, the gray dots are price points corresponding to 8 months, the black dots are price points corresponding to 9 months, the abscissa of the price points represents the bid space value, and the ordinate of the price points represents the electric value. The method can divide each day into 96 time periods, and each 15 minutes is taken as one period, and the electricity price and the bidding space value at the same time every day of the whole month are averaged to obtain 96 price points, wherein the 96 price points represent the price information of the month. For example, 1 month 1 day to 1 month 31 days, 0: 00. 0:15, 0:30, 1:00 … … 23:45,1 month 1 day to 1 month 31 day 0: the electricity value at the time point 00 is averaged to obtain 0: average value of electricity price at 00 time points, 0 of 1 month, 1 day to 1 month and 31 days: calculating average value of bidding space values at 00 time points, and calculating 0: bid space average value of 00 time points, 0: electricity price mean value at time point 00 and 0: the bid spatial mean at time 00 collectively constitutes 0: price measuring point at time 00.
In other embodiments, each day may be divided into 24 time periods, or 48 time periods, without limitation.
And S2, obtaining a historical month slope and a historical month intercept corresponding to each historical month according to a plurality of historical month price points corresponding to each historical month.
The linear model of the historical month electricity value can be fitted based on the historical month bidding space value in a training mode month by month, and the slope and intercept of each historical month are obtained.
Illustratively, from a plurality of price points of 1 month, a slope of 1 month and an intercept of 1 month are obtained; obtaining a slope of 2 months and an intercept of 2 months according to a plurality of valence points of 2 months; obtaining a slope of 3 months and an intercept of 3 months according to a plurality of valence points of 3 months; obtaining a slope of 4 months and an intercept of 4 months according to a plurality of price measuring points of 4 months; obtaining a slope of 5 months and an intercept of 5 months according to a plurality of valence points of 5 months; obtaining a slope of 6 months and an intercept of 6 months according to a plurality of valence points of 6 months; obtaining a slope of 7 months and an intercept of 7 months according to a plurality of valence points of 7 months; from the multiple valence points of 8 months, a slope of 8 months and an intercept of 8 months were obtained.
And step S3, obtaining the target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the history month intercept corresponding to each history month and the bid space values of a plurality of history month price points corresponding to each history month.
The bid space value corresponding to the target month may be obtained by the power supply demand amount corresponding to the target month and the power supply amount other than the thermal power.
Obtaining a rule between the intercept and the bidding space value in the history month according to the historic month intercept and the historic month bidding space value, and carrying out calculation according to the rule of bringing the bidding space value corresponding to the target month into the space between the intercept and the bidding space value in the history month to obtain the target month intercept corresponding to the target month.
And S4, obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month.
And translating bidding space values in the historical month price points corresponding to each historical month according to the difference value between the historical month intercept corresponding to each historical month and the target month intercept, and keeping the electricity price values in the historical month price points unchanged to obtain a plurality of new price points which are regarded as target month price points corresponding to the target month.
And S5, obtaining a corresponding price mapping relation of the target month according to the target month price point.
And S6, obtaining the electric value corresponding to the target month according to the price quantity mapping relation corresponding to the target month and the bidding space value corresponding to the target month.
And fitting a mapping relation between the bidding space value and the electricity price value in the target month, namely, the price mapping relation corresponding to the target month, and carrying the bidding space value corresponding to the target month into the mapping relation for calculation to obtain the electricity value corresponding to the target month.
By adopting the technical scheme, the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electricity value; obtaining a historical month slope and a historical month intercept corresponding to each historical month according to a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month; obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month; and according to the target month price point, obtaining a price mapping relation corresponding to the target month, and then combining the bidding space value corresponding to the target month to obtain the electricity price value corresponding to the target month, so as to realize the prediction of the electricity price of the whole month, thereby completing the medium-and-long-term electricity price prediction.
In one possible embodiment, step S2 may include:
and fitting a plurality of historical month price points corresponding to each historical month by a least square method to obtain a historical month slope and a historical month intercept corresponding to each historical month.
In one possible implementation, referring to fig. 3, step S2 may include steps S21 to S23:
and S21, fitting a plurality of historical month price measuring points corresponding to each historical month through a least square method to obtain a historical month slope corresponding to each historical month.
And S22, obtaining a target month slope corresponding to the target month according to the historical month slope corresponding to the historical month.
The target month slope corresponding to the target month is obtained according to the history month slope corresponding to the history month, and it can be understood that the target month slope corresponding to the target month is obtained according to the history month slope corresponding to part of the history months, or the target month slope corresponding to the target month is obtained according to the history month slopes corresponding to all the history months.
And obtaining a target month slope corresponding to the target month according to the historical month slope corresponding to the partial historical month, namely calculating the average value of the historical month slopes corresponding to the partial history to obtain the target month slope corresponding to the target month.
And obtaining a target month slope corresponding to the target month according to the historical month slopes corresponding to all the historical months, namely calculating the average value of the historical month slopes corresponding to all the historical months to obtain the target month slope corresponding to the target month.
For example, the target month slope corresponding to the target month may be obtained according to the average value of the historical month slopes corresponding to 5-8 months, or the target month slope corresponding to the target month may be obtained according to the average value of the historical month slopes corresponding to 1-8 months.
In other embodiments, the target month slope may be calculated by using the history month slope of the last history month, or the median of the history month slope may be used as the target month slope corresponding to the target month. Wherein a portion of the historic months may be based on a selection of weather information, e.g., a historic month for which the selected weather information is similar to the weather information of the target month.
Step S23, obtaining the historical month intercept corresponding to each historical month according to the target month slope and a plurality of historical month price measuring points corresponding to each historical month.
Calculating the average value of the electricity price values in the plurality of historical month price points corresponding to each historical month to obtain the average value of the electricity price corresponding to each historical month, and calculating the average value of the bidding space values in the plurality of historical month price points corresponding to each historical month to obtain the bidding space average value corresponding to each historical month.
And taking the target month slope as a fixed slope of each historical month, and carrying into a quantitative price calculation formula to obtain a historical month intercept corresponding to each historical month. The calculation formula of the quantitative price is as follows:
wherein,,for the mean value of electricity price corresponding to the ith history month, < > for>The bidding space average value corresponding to the ith historical month is represented by k, which is the target month slope, b i The intercept corresponding to the i-th history month.
In one possible implementation, referring to fig. 4, step S3 may include steps S31 to S34:
and S31, averaging bidding space values of a plurality of historical month price points corresponding to each historical month to obtain a bidding space average value corresponding to each historical month.
Illustratively, the bidding space values of a plurality of price points of 1 month are averaged to obtain a bidding space average value of 1 month; averaging the bidding space values of a plurality of bidding points of 2 months to obtain a bidding space average value of 2 months; and averaging the bidding space values of a plurality of price points of 3 months to obtain the bidding space average value of 2 months, and the like.
And step S32, obtaining a mapping relation between the intercept and the bidding space average value according to the historical month intercept corresponding to each historical month and the bidding space average value corresponding to each historical month.
There is a linear relationship between the historical month bid spatial mean and the historical month intercept. The mapping relationship between the intercept and the bidding space mean can be fitted by a least square method.
For example, the mapping relationship between the intercept and the bidding space average is obtained by the least square method based on the intercept of 1 to 8 months and the bidding space average, that is, 8 sets of data.
And step S33, averaging the bidding space values corresponding to the target month to obtain the bidding space average value corresponding to the target month.
And step S34, obtaining the target month intercept corresponding to the target month according to the mapping relation between the intercept and the bid space mean value corresponding to the target month.
And carrying out calculation on the bid space average value corresponding to the target month by taking in the mapping relation between the intercept and the bid space average value, so as to obtain the target month intercept corresponding to the target month.
In one possible implementation, referring to fig. 5, step S4 may include step S41 and step S42:
step S41, calculating the difference value of the historical month intercept corresponding to each historical month and the target month intercept to obtain the intercept offset corresponding to each historical month.
And step S42, obtaining a target month price point corresponding to the target month according to the intercept variable corresponding to each history month and the plurality of history month price points corresponding to each history month.
And increasing intercept variables corresponding to the corresponding historical months for bidding space values in a plurality of historical month price points corresponding to each historical month, wherein the electric value in the plurality of historical month price points corresponding to each historical month is unchanged, and obtaining a target month price point corresponding to the target month.
In other embodiments, a predicted target month value point model may be previously established, which outputs a target month value point according to the input historical month intercept corresponding to each historical month, the target month intercept, and a plurality of historical month value points corresponding to each historical month.
To implement the above-described method class embodiments, referring to fig. 6, fig. 6 is a block diagram of a medium-to-long term electricity price prediction apparatus according to an exemplary embodiment, and the medium-to-long term electricity price prediction apparatus 600 includes an acquisition module 601, a first processing module 602, a second processing module 603, a third processing module 604, a fourth processing module 605, and a fifth processing module 606.
The acquiring module 601 is configured to acquire a plurality of historical month price information, wherein the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electricity value;
the first processing module 602 is configured to obtain a history month slope and a history month intercept corresponding to each history month according to a plurality of history month price measuring points corresponding to each history month;
the second processing module 603 is configured to obtain a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month, and the bid space values of the plurality of historical month price measuring points corresponding to each historical month;
a third processing module 604, configured to obtain a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept, and the plurality of historical month price points corresponding to each historical month;
a fourth processing module 605 configured to obtain a corresponding price mapping relationship of the target month according to the target month price point;
and a fifth processing module 606, configured to obtain an electrical value corresponding to the target month according to the price mapping relationship corresponding to the target month and the bid space value corresponding to the target month.
Optionally, the first processing module 602 is specifically configured to:
and fitting a plurality of historical month price points corresponding to each historical month by a least square method to obtain a historical month slope and a historical month intercept corresponding to each historical month.
Optionally, the first processing module may include a first sub-processing module, a second sub-processing module, and a third sub-processing module:
the first sub-processing module is configured to fit a plurality of historical month price measuring points corresponding to each historical month through a least square method to obtain a historical month slope corresponding to each historical month;
the second sub-processing module is configured to obtain a target month slope corresponding to the target month according to the historical month slope corresponding to the historical month;
and the third sub-processing module is configured to obtain a historical month intercept corresponding to each historical month according to the target month slope and a plurality of historical month price points corresponding to each historical month.
Optionally, the second sub-processing module is specifically configured to:
and calculating the average value of the historical month slopes corresponding to all the historical months to obtain the target month slope corresponding to the target month.
Optionally, the second processing module 603 includes a fourth sub-processing module, a fifth sub-processing module, a sixth sub-processing module, and a seventh sub-processing module.
The fourth sub-processing module is configured to average the bidding space values of a plurality of historical month price measuring points corresponding to each historical month to obtain the bidding space average value corresponding to each historical month;
the fifth sub-processing module is configured to obtain a mapping relation between the intercept and the bidding space average value according to the historical month intercept corresponding to each historical month and the bidding space average value corresponding to each historical month;
the sixth sub-processing module is configured to average the bidding space value corresponding to the target month to obtain the bidding space average value corresponding to the target month;
and the seventh sub-processing module is configured to obtain a target month intercept corresponding to the target month according to the mapping relation between the intercept and the bid space mean value corresponding to the target month.
Optionally, the third processing module 604 includes an eighth sub-processing module and a ninth sub-processing module.
An eighth sub-processing module configured to calculate a difference value between the historical month intercept corresponding to each historical month and the target month intercept, and obtain an intercept offset corresponding to each historical month;
and the ninth sub-processing module is configured to obtain a target month price point corresponding to the target month according to the intercept variable corresponding to each historical month and the plurality of historical month price points corresponding to each historical month.
Optionally, the ninth sub-processing module is specifically configured to:
and increasing intercept variables corresponding to the corresponding historical months for bidding space values in a plurality of historical month price points corresponding to each historical month, wherein the electric value in the plurality of historical month price points corresponding to each historical month is unchanged, and obtaining a target month price point corresponding to the target month.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 7 is a block diagram of an electronic device 700, according to an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may also include one or more of a multimedia component 703, an input/output (I/O) interface 704, and a communication component 705.
Wherein the processor 701 is configured to control the overall operation of the electronic device 700 to perform all or part of the steps in the medium-to-long-term electricity price prediction method described above. The memory 702 is used to store various types of data to support operation on the electronic device 700, which may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, and so forth. The Memory 702 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 703 can include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 702 or transmitted through the communication component 705. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 705 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.
In an exemplary embodiment, the electronic device 700 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated ASIC), digital signal processor (Digital Signal Processor, abbreviated DSP), digital signal processing device (Digital Signal Processing Device, abbreviated DSPD), programmable logic device (Programmable Logic Device, abbreviated PLD), field programmable gate array (Field Programmable Gate Array, abbreviated FPGA), controller, microcontroller, microprocessor, or other electronic components for performing the medium-to-long term power rate prediction method described above.
In another exemplary embodiment, a non-transitory computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the medium-to-long term electricity price prediction method described above. For example, the computer readable storage medium may be the memory 702 including program instructions described above that are executable by the processor 701 of the electronic device 700 to perform the medium-to-long term electricity price prediction method described above.
In another exemplary embodiment, a computer program product is also provided, comprising a computer program executable by a programmable apparatus, the computer program having code portions for performing the medium-to-long term electricity price prediction method described above when executed by the programmable apparatus.
The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. The various possible combinations are not described further in this disclosure in order to avoid unnecessary repetition.
Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.

Claims (10)

1. A medium-to-long term electricity price prediction method, comprising:
acquiring a plurality of pieces of historical month price information, wherein the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electric value;
obtaining a historical month slope and a historical month intercept corresponding to each historical month according to the plurality of historical month price measuring points corresponding to each historical month;
obtaining a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month and the bid space values of a plurality of historical month price measuring points corresponding to each historical month;
obtaining a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month;
obtaining a corresponding price mapping relation of the target month according to the target month price point;
and obtaining the electric value corresponding to the target month according to the price quantity mapping relation corresponding to the target month and the bidding space value corresponding to the target month.
2. The method for predicting medium-to-long-term electricity prices according to claim 1, wherein the obtaining a history month slope and a history month intercept corresponding to each history month according to the plurality of history month price points corresponding to each history month comprises:
and fitting the plurality of historical month price measuring points corresponding to each historical month by a least square method to obtain a historical month slope and a historical month intercept corresponding to each historical month.
3. The method for predicting medium-to-long-term electricity prices according to claim 2, wherein the obtaining the history month slope and the history month intercept corresponding to each history month according to the plurality of history month price points corresponding to each history month comprises:
fitting the plurality of historical month price measuring points corresponding to each historical month by a least square method to obtain a historical month slope corresponding to each historical month;
obtaining a target month slope corresponding to the target month according to the history month slope corresponding to the history month;
and obtaining the historical month intercept corresponding to each historical month according to the target month slope and the plurality of historical month price measuring points corresponding to each historical month.
4. The medium-and-long-term electricity price prediction method according to claim 3, wherein the obtaining the target month slope corresponding to the target month according to the history month slope corresponding to the history month comprises:
and calculating the average value of the historical month slopes corresponding to all the historical months to obtain the target month slope corresponding to the target month.
5. The medium-and-long-term electricity price prediction method according to claim 1, wherein the obtaining the target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the historical month intercept corresponding to each historical month, and the bid space values of the plurality of historical month price measuring points corresponding to each historical month comprises:
calculating the average value of bidding space values of a plurality of historical month price measuring points corresponding to each historical month to obtain the bidding space average value corresponding to each historical month;
obtaining a mapping relation between the intercept and the bidding space average value according to the historical month intercept corresponding to each historical month and the bidding space average value corresponding to each historical month;
averaging the bidding space values corresponding to the target month to obtain the bidding space average value corresponding to the target month;
and obtaining the target month intercept corresponding to the target month according to the mapping relation between the intercept and the bidding space mean value corresponding to the target month.
6. The medium-and-long-term electricity price prediction method according to claim 1, wherein the obtaining the target month-amount price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept, and the plurality of historical month-amount price points corresponding to each historical month comprises:
calculating the difference value of the historical month intercept corresponding to each historical month and the target month intercept to obtain the intercept offset corresponding to each historical month;
and obtaining a target month price point corresponding to the target month according to the intercept variable corresponding to each history month and the plurality of history month price points corresponding to each history month.
7. The method for predicting medium-and-long-term electricity prices according to claim 6, wherein the obtaining the target month value point corresponding to the target month according to the intercept variable corresponding to each historical month and the plurality of historical month value points corresponding to each historical month includes:
and increasing intercept variables corresponding to the corresponding historical months for bidding space values in a plurality of historical month price points corresponding to each historical month, wherein the electric value in the plurality of historical month price points corresponding to each historical month is unchanged, and obtaining a target month price point corresponding to the target month.
8. A medium-to-long-term electricity price prediction device, characterized in that the medium-to-long-term electricity price prediction device includes:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is configured to acquire a plurality of pieces of historical month price information, the historical month price information comprises a plurality of historical month price points corresponding to each historical month, and the price points comprise a bidding space value and an electricity value;
the first processing module is configured to obtain a history month slope and a history month intercept corresponding to each history month according to the plurality of history month price measuring points corresponding to each history month;
the second processing module is configured to obtain a target month intercept corresponding to the target month according to the bid space value corresponding to the target month, the history month intercept corresponding to each history month and the bid space values of a plurality of history month price measuring points corresponding to each history month;
the third processing module is configured to obtain a target month price point corresponding to the target month according to the historical month intercept corresponding to each historical month, the target month intercept and a plurality of historical month price points corresponding to each historical month;
the fourth processing module is configured to obtain a corresponding price mapping relation of the target month according to the target month price point;
and the fifth processing module is configured to obtain the electric value corresponding to the target month according to the bid space value corresponding to the target month and the bid mapping relation corresponding to the target month.
9. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the program when executed by a processor implements the steps of the medium-long term electricity price prediction method of any of claims 1-7.
10. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to implement the steps of the medium-long term electricity price prediction method of any one of claims 1-7.
CN202310673819.3A 2023-06-07 2023-06-07 Medium-and-long-term electricity price prediction method and device, storage medium and electronic equipment Pending CN116883033A (en)

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CN202310673819.3A CN116883033A (en) 2023-06-07 2023-06-07 Medium-and-long-term electricity price prediction method and device, storage medium and electronic equipment

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