CN110688620A - Short-term load prediction method and device - Google Patents

Short-term load prediction method and device Download PDF

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CN110688620A
CN110688620A CN201910855389.0A CN201910855389A CN110688620A CN 110688620 A CN110688620 A CN 110688620A CN 201910855389 A CN201910855389 A CN 201910855389A CN 110688620 A CN110688620 A CN 110688620A
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赵蕾
杨杰
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Xinao Shuneng Technology Co Ltd
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Abstract

The invention discloses a short-term load prediction method, which is characterized by comprising the following steps: s1, acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors; s2, constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3(t) calculating components generated by random factors at the time t according to random fluctuation errors; and S3, calculating and solving according to the first short-term load prediction model to obtain a load prediction value. The short-term load prediction method and the short-term load prediction device provided by the invention take influence factors into considerationAnd the accuracy of short-term load prediction is improved.

Description

Short-term load prediction method and device
Technical Field
The invention belongs to the field of energy, relates to a short-term load forecasting method and device, and more particularly relates to a short-term load forecasting method and device considering influence factors.
Background
Load forecasting is an important basis for energy planning, economic operation, and energy management. Short-term load prediction generally refers to predicting the load of a predicted object for one day or week in the future. Short-term loading is characterized by exposure to weather, equipment conditions, and significant social activity factors.
Today, how to obtain accurate short-term load prediction results becomes an important and difficult problem in the rapid development of economy in China. The current prediction methods mainly include a conventional method represented by a time series and an intelligent method represented by an artificial neural network. The traditional method is simple in algorithm, high in speed and mature in application, but influence factors are rarely considered, and the prediction error is large on a special day with large change. Although the intelligent method is gradually applied and a large number of related field researches are carried out at present, the problems that the theoretical basis is immature, the learning is insufficient or the fitting is excessive easily occur in the training process and the like still exist.
Disclosure of Invention
In order to solve the above technical problems, the present invention provides a short-term load prediction method and apparatus, which considers influence factors and improves the accuracy of short-term load prediction.
The short-term load prediction method provided by the invention comprises the following steps:
s1, acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors;
s2, constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3(t) calculating components generated by random factors at the time t according to random fluctuation errors;
and S3, calculating and solving according to the first short-term load prediction model to obtain a load prediction value.
Preferably, the base load component at the time t
Figure BDA0002198203270000027
Wherein the content of the first and second substances,
Figure BDA0002198203270000022
to predict the predicted value of the load at time t of day, PaveIs the average value of the load at the time t of the historical day, rhotIs a periodicity factor.
In one embodiment of the invention, the load mean value at the time t of the historical day
Figure BDA0002198203270000024
The period coefficient
Figure BDA0002198203270000025
Wherein, PitThe load value is the load value at the time t of the ith historical day.
In one embodiment of the present invention, the load component y generated by the meteorological factors at the time t2(t)＝at×YTmax+bt
Wherein, YTmaxRepresenting the load value when the temperature is maximum in the temperature sequence; a istIs the regression coefficient of the t-th period, btIs the regression constant for the t-th period.
In one embodiment of the present invention, at，btAnd fitting the load sequence constructed at the time of the historical daily load value t by adopting a least square method.
In one embodiment of the present invention, the component y generated by the random factor at time t3(t)＝εk(t)，εk(t) is the random factor that causes random fluctuating errors in the load for the kth week, period t.
In one embodiment of the present invention,. epsilon.k(t) byThe method comprises the following steps:
s10, constructing a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t)；
S20, calculating and solving to obtain predicted load data according to the second short-term load prediction model;
s30, acquiring actual load data;
s40, calculating the difference value between the actual load data and the predicted load data;
s50, judging whether the difference value is in an error allowable range; if yes, the difference is εk(t); otherwise, go to step S60;
s60, using autoregressive-moving average model ARMA (p, q) for y3(t) performing a prediction.
In one embodiment of the present invention, the base load component at the time t
Figure BDA0002198203270000031
Figure BDA0002198203270000032
Wherein the content of the first and second substances,to predict the predicted value of the load at time t of day, PaveIs the average value of the load at the time t of the historical day, rhotIs a period coefficient; the load mean value at the time t of the historical day
Figure BDA0002198203270000034
Figure BDA0002198203270000035
The period coefficient
Figure BDA0002198203270000036
Figure BDA0002198203270000037
Wherein, PitThe load value is the t moment load value of the ith historical day;
the load component y generated by the meteorological factors at the time t2(t)＝at×Tmax+bt(ii) a Wherein, TmaxRepresents the maximum temperature in the temperature sequence; a istIs the regression coefficient of the t-th period, btIs the regression coefficient of the t-th period.
The present invention also provides a short-term load prediction apparatus, comprising:
the data acquisition module is used for acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors;
the first short-term load prediction model construction module is used for constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3(t) calculating components generated by random factors at the time t according to random fluctuation errors;
and the load prediction data calculation module is used for calculating and solving to obtain load prediction data according to the first short-term load prediction model.
In an embodiment of the present invention, the first short-term load prediction model building module includes a component obtaining module for generating a random factor at time t, and the component obtaining module for generating the random factor at time t includes:
the second short-term load prediction model building module is used for building a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t)；
The second short-term load prediction model solving module is used for calculating and solving according to the second short-term load prediction model to obtain predicted load data;
the actual load data acquisition module is used for acquiring actual load data;
the difference value calculation module is used for calculating the difference value between the actual load data and the predicted load data;
a difference value judging module for judging whether the difference value is allowable errorA range; if yes, the difference is εk(t); if not, a component prediction module generated by random factors performs prediction;
a component prediction module for random factor generation using an autoregressive-moving average model ARMA (p, q) for y3(t) performing a prediction.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of a short term load prediction method according to the present invention;
FIG. 2 is a flow chart illustrating a method for obtaining components generated by random factors according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a short term load forecasting arrangement according to the present invention;
fig. 4 is a schematic structural diagram of a component obtaining module generated by the random factor at time t in an embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the short-term load prediction method provided by the present invention includes the following steps:
and S1, acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors.
S2, constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3And (t) is a component generated by the random factor at the time t and is obtained by calculation according to the random fluctuation error.
Base load component at time t
Figure BDA0002198203270000051
Wherein the content of the first and second substances,
Figure BDA0002198203270000052
to predict the predicted value of the load at time t of day, PaveIs the average value of the load at the time t of the historical day, rhotIs a periodicity factor. Historical day time t load mean
Figure BDA0002198203270000053
Coefficient of periodicity
Figure BDA0002198203270000054
Figure BDA0002198203270000055
Wherein, PitThe load value is the load value at the time t of the ith historical day.
Load component y generated by meteorological factors at time t2(t)＝at×YTmax+bt. Wherein, YTmaxRepresenting the load value when the temperature is maximum in the temperature sequence; a istIs the regression coefficient of the t-th period, btIs the regression constant for the t-th period. a ist，btAnd fitting the load sequence constructed at the time of the historical daily load value t by adopting a least square method.
Component y generated by random factor at time t3(t)＝εk(t)，εk(t) is caused by random factors (factors other than weather)Random fluctuation error of the load at the kth time period of the kth week. As shown in FIG. 2,. epsilonk(t) is obtained by the following steps: s10, constructing a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t); s20, calculating and solving to obtain predicted load data according to the second short-term load prediction model; s30, acquiring actual load data; s40, calculating the difference value between the actual load data and the predicted load data; s50, judging whether the difference value is in an error allowable range; if yes, the difference is εk(t); otherwise, go to step S60; s60, using autoregressive-moving average model ARMA (p, q) for y3(t) predicting: ARMA is a relatively mature time series algorithm, and the prediction process is as follows: modeling data-ARMA algorithm-output result, the invention uses the algorithm packet in python to call the ARMA algorithm packet to carry out the operation:
Figure BDA0002198203270000056
a p-order moving average process; a q-order autoregressive process.
And S3, calculating and solving according to the first short-term load prediction model to obtain a load prediction value.
The present invention also provides a short-term load prediction apparatus, comprising:
and the data acquisition module 10 is used for acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors.
A first short-term load prediction model construction module 20, which constructs a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3And (t) is a component generated by the random factor at the time t and is obtained by calculation according to the random fluctuation error. The first short-term load prediction model building module comprises a random factor at the t momentThe generated component obtaining module 210, as shown in fig. 4, the component obtaining module 210 for generating the random factor at time t includes: the second short-term load prediction model construction module 211 is configured to construct a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t); the second short-term load prediction model solving module 212 is used for calculating and solving to obtain predicted load data according to the second short-term load prediction model; an actual load data acquisition module 213 that acquires actual load data; a difference calculation module 214 that calculates a difference between the actual load data and the predicted load data; a difference value judging module 215, which judges whether the difference value is within the error allowable range; if yes, the difference is εk(t); if not, the component prediction module 216 generated by the random factor performs prediction; the random factor generating component prediction module 216 uses an autoregressive-moving average model ARMA (p, q) versus y3(t) predicting:
Figure BDA0002198203270000061
and the load prediction data calculation module 30 calculates and solves the load prediction data according to the first short-term load prediction model.
Fig. 5 is a schematic structural diagram of an apparatus of a short-term load prediction method according to an embodiment of the present invention. On the hardware level, the server includes a processor 701 and a memory 702 storing execution instructions, and optionally an internal bus 703 and a network interface 704. The Memory 702 may include a Memory 7021, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory 7022 (e.g., at least 1 disk Memory); the processor 701, the network interface 704, and the memory 702 may be connected to each other by an internal bus 703, and the internal bus 703 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like; the internal bus 703 may be divided into an address bus, a data bus, a control bus, etc., and is indicated by a double-headed arrow in fig. 5 for convenience of illustration, but does not indicate only one bus or one type of bus. Of course, the server may also include hardware needed for other services. When the processor 701 executes the execution instructions stored in the memory 702, the processor 701 executes the method described in any of the embodiments of the present invention, and at least is configured to: in a possible implementation manner, the processor reads corresponding execution instructions from the nonvolatile memory into the memory and then runs the corresponding execution instructions, and corresponding execution instructions can also be obtained from other equipment, so as to form the device of the short-term load prediction method on a logic level. The processor executes the execution instructions stored in the memory to implement the short-term load prediction method provided in any embodiment of the invention through the executed execution instructions.
The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The embodiment of the invention also provides a computer-readable storage medium, which comprises an execution instruction, and when a processor of the electronic device executes the execution instruction, the electronic device executes the method provided in any embodiment of the invention. The electronic device may specifically be the device of the short-term load prediction method shown in fig. 5; the method for executing the instructions to obtain the optimized trading scheme of the microgrid is a corresponding computer program.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims (10)

1. A method for short term load prediction, comprising the steps of:
s1, acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors;
s2, constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) is the base load component at time t, having a periodThe instantaneity is obtained by calculation according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3(t) calculating components generated by random factors at the time t according to random fluctuation errors;
and S3, calculating and solving according to the first short-term load prediction model to obtain a load prediction value.
2. The method of claim 1, wherein the base load component at time t is the load component
Figure FDA0002198203260000011
Wherein the content of the first and second substances,
Figure FDA0002198203260000012
to predict the predicted value of the load at time t of day, PaveIs the average value of the load at the time t of the historical day, rhotIs a periodicity factor.
3. The short term load prediction method according to claim 2,
the load mean value at the time t of the historical day
Figure FDA0002198203260000013
The period coefficient
Figure FDA0002198203260000014
Figure FDA0002198203260000015
Wherein, PitThe load value is the load value at the time t of the ith historical day.
4. The short term load prediction method as claimed in claim 1, wherein the time t is a gasLoad component y generated by image factor2(t)＝at×YTmax+bt
Wherein, YTmaxRepresenting the load value when the temperature is maximum in the temperature sequence; a istIs the regression coefficient of the t-th period, btIs the regression constant for the t-th period.
5. The short term load prediction method as claimed in claim 1, wherein a ist，btAnd fitting the load sequence constructed at the time of the historical daily load value t by adopting a least square method.
6. The method of claim 1, wherein the component y generated by the random factor at time t is the component y3(t)＝εk(t)，εk(t) is the random factor that causes random fluctuating errors in the load for the kth week, period t.
7. The short term load prediction method as claimed in claim 6, characterized in that ∈ is givenk(t) is obtained by the following steps:
s10, constructing a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t)；
S20, calculating and solving to obtain predicted load data according to the second short-term load prediction model;
s30, acquiring actual load data;
s40, calculating the difference value between the actual load data and the predicted load data;
s50, judging whether the difference value is in an error allowable range; if yes, the difference is εk(t); otherwise, go to step S60;
s60, using autoregressive-moving average model ARMA (p, q) for y3(t) performing a prediction.
8. The short term load prediction method as claimed in claim 7,
the base load component at the time t
Figure FDA0002198203260000021
Wherein the content of the first and second substances,
Figure FDA0002198203260000022
to predict the predicted value of the load at time t of day, PaveIs the average value of the load at the time t of the historical day, rhotIs a period coefficient; the load mean value at the time t of the historical dayThe period coefficient
Figure FDA0002198203260000024
Figure FDA0002198203260000025
Wherein, PitThe load value is the t moment load value of the ith historical day;
the load component y generated by the meteorological factors at the time t2(t)＝at×Tmax+bt(ii) a Wherein, TmaxRepresents the maximum temperature in the temperature sequence; a istIs the regression coefficient of the t-th period, btIs the regression coefficient of the t-th period.
9. A short-term load prediction apparatus, comprising:
the data acquisition module is used for acquiring historical daily load values, temperature maximum values in the temperature sequence and random fluctuation errors;
the first short-term load prediction model construction module is used for constructing a first short-term load prediction model: y (t) ═ y1(t)+y2(t)+y3(t); wherein, y1(t) the basic load component at the moment t has periodicity, and is calculated according to the historical daily load value; y is2(t) load components generated by meteorological factors at the time t are calculated according to the maximum temperature value in the temperature sequence; y is3(t) calculating components generated by random factors at the time t according to random fluctuation errors;
and the load prediction data calculation module is used for calculating and solving to obtain load prediction data according to the first short-term load prediction model.
10. The short-term load prediction device of claim 9, wherein the first short-term load prediction model building module comprises a t-time random element generation component obtaining module, and the t-time random element generation component obtaining module comprises:
the second short-term load prediction model building module is used for building a second load prediction model of the periodic component and the weather component: y (t) ═ y1(t)+y2(t)；
The second short-term load prediction model solving module is used for calculating and solving according to the second short-term load prediction model to obtain predicted load data;
the actual load data acquisition module is used for acquiring actual load data;
the difference value calculation module is used for calculating the difference value between the actual load data and the predicted load data;
the difference value judging module is used for judging whether the difference value is within an error allowable range or not; if yes, the difference is εk(t); if not, a component prediction module generated by random factors performs prediction;
a component prediction module for random factor generation using an autoregressive-moving average model ARMA (p, q) for y3(t) performing a prediction.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111598303A (en) * 2020-04-20 2020-08-28 广东电网有限责任公司电力调度控制中心 Summer short-term load prediction method based on meteorological component decomposition
CN113033016A (en) * 2021-04-12 2021-06-25 北京信息科技大学 Hybrid-based central air conditioner load prediction method and device
CN113028610A (en) * 2021-04-12 2021-06-25 北京信息科技大学 Method and device for global optimization and energy-saving control of dynamic load of central air conditioner
CN113033016B (en) * 2021-04-12 2021-10-22 北京信息科技大学 Hybrid-based central air conditioner load prediction method and device
CN117154740A (en) * 2023-10-31 2023-12-01 国网浙江省电力有限公司宁波供电公司 Load regulation and control method and device for heat accumulating type electric heating participating power distribution network
CN117154740B (en) * 2023-10-31 2024-01-26 国网浙江省电力有限公司宁波供电公司 Load regulation and control method and device for heat accumulating type electric heating participating power distribution network

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