CN110728408A - Resident electricity consumption prediction method considering user source load characteristics - Google Patents

Resident electricity consumption prediction method considering user source load characteristics Download PDF

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CN110728408A
CN110728408A CN201910981521.2A CN201910981521A CN110728408A CN 110728408 A CN110728408 A CN 110728408A CN 201910981521 A CN201910981521 A CN 201910981521A CN 110728408 A CN110728408 A CN 110728408A
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user
capacity
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李振伟
赵树军
单保涛
马涛
马隽
刘义江
王晶
张正文
钟诚
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State Grid Hebei Electric Power Co Ltd Xiongan New District Power Supply Co
State Grid Corp of China SGCC
State Grid Hebei Electric Power Co Ltd
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Abstract

The invention discloses a resident electricity consumption prediction method considering user source load characteristics, wherein the increase of the types and the number of resident electricity utilization equipment leads the randomness of load to be stronger, the solving difficulty of the existing intelligent algorithm is increased continuously, the parameter estimation is more difficult, but the resident load and the user behavior habits have potential relevance with the climate periodic variation and present periodic fluctuation characteristics with different frequencies.

Description

Resident electricity consumption prediction method considering user source load characteristics
Technical Field
The invention belongs to the field of power grid planning and development decision support, and particularly relates to a residential electricity consumption prediction method considering user source load fluctuation characteristics.
Background
With the development of economy and the adjustment of economic structures in China, the proportion of the electricity consumption of residents in the electricity consumption of the whole society is gradually increased, and the electricity consumption of residents tends to be continuously increased, so that the electricity consumption of residents gradually becomes an important mark for measuring the overall realization of the well-being society in China. Therefore, it is necessary to research a prediction model and a prediction method for residential electricity consumption to meet the requirement of rapid increase of residential electricity demand, provide technical support for power grid construction of residential communities with different economic development levels in different regions, and provide decision basis for government planning of residential community construction.
The existing power consumption prediction method in China has more researches, relatively few researches are carried out on resident power consumption, for example, in the research of a resident power consumption analysis and prediction model under the market economic condition, a multivariate regression equation with virtual variables is established, economic factors and non-economic factors influencing resident power demand are considered at the same time, the influence of the non-economic factors (such as climate, living habits and consumption habits) on the resident power consumption is described quantitatively, and if 9 index factors influencing the rural resident power consumption in China are selected, the conventional BP neural network method, the principal component-BP neural network method and the principal component regression method are adopted in sequence to predict the resident power consumption in the rural areas in China in 2015-2020. In addition, an artificial neural network is used for establishing a nonlinear mapping relation between the household appliance owned quantity and the average annual power consumption of residents, and the average annual power consumption of the residents is predicted by taking the owned condition of the household appliances of the residents as an input variable.
Generally, the intelligent algorithm is adopted to consider the influence factors of the resident load, so that the prediction accuracy is improved, but the fluctuation characteristics of emerging loads are less considered.
Disclosure of Invention
In recent years, the load of residents is greatly increased, the household power consumption capacity of the original 20A current cannot meet the household power demand, and the newly increased power consumption load is various, such as geothermal heating, an air source heat pump, an instant electric water heater, a drying type washing machine, an electric oven, an electric automobile and the like, and household distributed photovoltaic power generation equipment is added.
The invention provides a residential electricity consumption prediction method considering the fluctuation characteristics of user source load, which separates the electricity load according to different fluctuation periods and predicts according to the corresponding periods so as to improve the prediction precision.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a resident electricity consumption prediction method considering user source load characteristics comprises the following steps:
1) the selected power prediction region comprises N users, each user has M-year-hour power data, wherein the mth year is Wm _ i, and the value range of i is [1,8760 ];
2) in order to facilitate the normalization processing of fluctuation characteristics, the direct prediction object is capacity utilization hour number, the indirect object is power consumption, the current capacity utilization hour number is calculated according to the formula (1) to obtain i hour K (n, m, i) of the mth year of the nth cell, the value range is [0,1], and the power consumption calculation formula (2) can be obtained by modifying the formula (1);
3) mapping the number of utilization hours K (n, m, i) of capacity per hour to be decomposed into 2j wavelet packet subspaces by adopting an equation (3), wherein j is the number of decomposition layers, and then reconstructing the decomposed K (n, m, i) by adopting an equation (4), so that all frequency bands of a time-varying signal i are divided into daily-variation low-frequency components and seasonal-variation high-frequency components;
Figure BDA0002235341500000031
Figure BDA0002235341500000032
in the formula, Pj q(i) Representing the qth wavelet packet on the jth layer as a wavelet packet coefficient; H. g is a low-pass filter bank and a high-pass filter bank for wavelet packet decomposition respectively; h. g is a low-pass filter bank and a high-pass filter bank for wavelet packet reconstruction respectively;
4) from the formula (4), the low frequency component K at the ith time of the mth year of the nth user can be obtainedL_n_m(i) And a high frequency component KH_n_m(i) Are respectively shown in a formula (5),
Figure BDA0002235341500000033
5) the low-frequency and high-frequency components of the m +1 year i hour capacity hours are calculated by adopting an autoregressive model of AR (p) as shown in a formula (6),
Figure BDA0002235341500000034
parameter in the formula
Figure BDA0002235341500000035
Can be obtained by calculating the data of the previous p years; (6)
k (n, m +1, i) can be calculated according to KH (n, m +1, i) and KL (n, m +1, i) obtained through prediction, then the number of utilization hours of capacity of each hour of the whole year of m +1 year is obtained, the data is multiplied by the capacity of each user to obtain the electric quantity of the user, and further all the electric quantities of the user are summed to obtain the electric quantity of the whole year.
Compared with the prior art, the invention has the following beneficial effects:
compared with the existing intelligent residential load prediction algorithm, the method has the advantages of simpler solving method, better applicability, timely tracking in fluctuation, difficulty in generating the point that the predicted value jumps greatly, higher overall prediction precision and suitability for residential power consumption prediction under the condition of high-power source load access.
Detailed Description
The technical solutions in the embodiments of the present application are clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the application, its application, or uses. 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 application.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular is intended to include the plural unless the context clearly dictates otherwise, and furthermore, it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of the features, steps, operations, devices, components, and/or combinations thereof.
The relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Also, it should be understood that the dimensions of the various parts shown in the drawings are not drawn to scale in practice for ease of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present application unless specifically stated otherwise. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate. In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values. In the description of the present application, it is to be understood that the directions or positional relationships indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally for convenience in describing the present application and for simplicity in description, and in the case of not being stated to the contrary, these directional terms do not indicate and imply that the device or element being referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore, should not be taken as limiting the scope of the present application; the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself. Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation of the device. For example, if a device is turned over, devices described as "above" or "above" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of protection of the present application is not to be construed as being limited.
A resident electricity consumption prediction method considering user source load characteristics comprises the following steps:
1) the selected power prediction region comprises N users, each user has M-year-hour power data, wherein the mth year is Wm _ i, and the value range of i is [1,8760 ];
2) in order to facilitate the normalization processing of fluctuation characteristics, the direct prediction object is capacity utilization hour number, the indirect object is power consumption, the current capacity utilization hour number is calculated according to the formula (1) to obtain i hour K (n, m, i) of the mth year of the nth cell, the value range is [0,1], and the power consumption calculation formula (2) can be obtained by modifying the formula (1);
3) mapping the number of utilization hours K (n, m, i) of capacity per hour to be decomposed into 2j wavelet packet subspaces by adopting an equation (3), wherein j is the number of decomposition layers, and then reconstructing the decomposed K (n, m, i) by adopting an equation (4), so that all frequency bands of a time-varying signal i are divided into daily-variation low-frequency components and seasonal-variation high-frequency components;
Figure BDA0002235341500000071
Figure BDA0002235341500000072
in the formula, Pj q(i) Representing the qth wavelet packet on the jth layer as a wavelet packet coefficient; H. g is a low-pass filter bank and a high-pass filter bank for wavelet packet decomposition respectively; h. g is a low-pass filter bank and a high-pass filter bank for wavelet packet reconstruction respectively;
4) from the formula (4), the low frequency component K at the ith time of the mth year of the nth user can be obtainedL_n_m(i) And a high frequency component KH_n_m(i) Are respectively shown in a formula (5),
Figure BDA0002235341500000073
5) the low-frequency and high-frequency components of the m +1 year i hour capacity hours are calculated by adopting an autoregressive model of AR (p) as shown in a formula (6),
parameter in the formula
Figure BDA0002235341500000075
Can be obtained by calculating the data of the previous p years;
k derived from predictionH(n, m +1, i) and KL(n, m +1, i) K (n, m +1, i) can be calculated, further the utilization hours of the capacity of each hour of the whole year of m +1 year can be obtained, the data is multiplied by the capacity of each user to obtain the electric quantity of the user, further all the electric quantity of the user is summed up,
thereby obtaining the annual electric quantity.
This example is illustrated with a place city as an example:
1) the selected electric quantity prediction area comprises 340.4 ten thousand users, each user has 10-year-hour electric quantity data, wherein the mth year is Wm _ i, and the value range of i is [1,8760 ];
2) respectively obtaining the capacity utilization hours K (n, m, i) of each year and each hour of each distribution area;
3) mapping the number of utilization hours K (n, m, i) of capacity per hour to be decomposed into 2j wavelet packet subspaces by adopting an equation (3), wherein j is the number of decomposition layers, and then reconstructing the decomposed K (n, m, i) by adopting an equation (4), so that all frequency bands of a time-varying signal i are divided into daily-variation low-frequency components and seasonal-variation high-frequency components;
Figure BDA0002235341500000081
in the formula, Pj q(i) Representing the qth wavelet packet on the jth layer as a wavelet packet coefficient; H. g is a low-pass filter bank and a high-pass filter bank for wavelet packet decomposition respectively; h. g are low-pass and high-pass filter banks for wavelet packet reconstruction respectively.
4) Calculating the low-frequency component K of the ith time of each user according to the formula (5)L_n_m(i) And a high frequency component KH_n_m(i)；
5) According to the formula (6), the low-frequency and high-frequency components of the capacity hours of i hours in the next year are calculated by adopting an autoregressive model of AR (3), and parameters in the formula are shown
Figure BDA0002235341500000083
Can be obtained by calculating the data of the previous 3 years;
6) predicted next year high frequency component KH(n, m +1, i) and a low frequency component KL(n, m +1, i) the next year K (n, m +1, i) can be calculated,
7) calculating to obtain the capacity utilization hours of each hour of the whole year in m +1 year;
8) multiplying the capacity utilization hour book of each user in m +1 years by the capacity of each user to obtain the electric quantity of the user;
9) and further summing all the user electric quantities to obtain m +1 year annual electric quantity.
Compared with the existing intelligent prediction algorithm of the resident load, the method has the advantages of simpler solving method, better applicability, timely tracking in the fluctuation, difficulty in generating the point that the predicted value jumps greatly, higher overall prediction precision and suitability for predicting the power consumption of residents under the condition of high-power source load access.
The embodiments described above are only preferred embodiments of the invention and are not exhaustive of the possible implementations of the invention. Any obvious modifications to the above would be obvious to those of ordinary skill in the art, but would not bring the invention so modified beyond the spirit and scope of the present invention.

Claims (1)

1. A resident electricity consumption prediction method considering user source load characteristics is characterized by comprising the following steps:
1) the selected power prediction region comprises N users, each user has M-year-hour power data, wherein the M-year is Wm_iI has a value range of [1,8760]]；
2) In order to facilitate the normalization processing of fluctuation characteristics, the direct prediction object is capacity utilization hours, the indirect object is power consumption, the current capacity utilization hours are calculated according to the formula (1) to obtain i hours K (n, m, i) of the mth year of the nth cell, the value range is [0,1], and the power consumption calculation formula (2) can be obtained by modifying the formula (1);
3) mapping the number of utilization hours K (n, m, i) of capacity per hour to be decomposed into 2j wavelet packet subspaces by adopting an equation (3), wherein j is the number of decomposition layers, and then reconstructing the decomposed K (n, m, i) by adopting an equation (4), so that all frequency bands of a time-varying signal i are divided into daily-variation low-frequency components and seasonal-variation high-frequency components;
Figure FDA0002235341490000011
Figure FDA0002235341490000012
in the formula, Pj q(i) Representing the qth wavelet packet on the jth layer as a wavelet packet coefficient; H. g is a low-pass filter bank and a high-pass filter bank for wavelet packet decomposition respectively; h. g is a low-pass filter bank and a high-pass filter bank for wavelet packet reconstruction respectively;
4) from the formula (4), the low frequency component K at the ith time of the mth year of the nth user can be obtainedL_n_m(i) And a high frequency component KH_n_m(i) Are respectively shown in a formula (5),
Figure FDA0002235341490000013
5) the low-frequency and high-frequency components of the capacity hours of i hours in the m +1 year are calculated by adopting an autoregressive model of AR (p) as shown in a formula (6),
Figure FDA0002235341490000021
parameter in the formula
Figure FDA0002235341490000022
Can be obtained by calculating the data of the previous p years;
k derived from predictionH(n, m +1, i) and KL(n, m +1, i) K (n, m +1, i) can be calculated, so that the number of utilization hours of the capacity of each hour of the whole year in m +1 year is obtained, the data is multiplied by the capacity of each user to obtain the electric quantity of the user, and the electric quantities of all the users are further summed to obtain the electric quantity of the whole year.
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