CN110119842B - Micro-grid short-term load prediction method - Google Patents
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Abstract
The invention relates to a micro-grid short-term load prediction method, which comprises the following steps: s1, acquiring load history data, and improving the data quality of a singular value interval by adopting a cubic spline difference value; s2, establishing a gray variation coefficient model based on amplitude compression, and introducing an improved human comfort index as an influence factor in the modeling process; s3, calculating a predicted parameter sequence of the accumulated mutation coefficient sequence and the mutation coefficient sequence; s4, optimizing the predicted parameter sequence by using a genetic simulated annealing algorithm to obtain optimal matching parameters; s5, reconstructing the optimal matching parameters to obtain an optimal variation coefficient sequence; s6, reversely solving to obtain a load predicted value, and controlling the working state of the distributed power supply. Compared with the prior art, the invention introduces the air quality condition into the human body comfort index, establishes the gray variation coefficient model, solves the problem based on the genetic simulated annealing algorithm, and has the advantages of high prediction speed and high prediction precision.
Description
Technical Field
The invention relates to the technical field of load prediction, in particular to a micro-grid short-term load prediction method.
Background
The micro-grid is a small power generation and distribution system connected to the user side, can realize self control, protection and management, and has the characteristics of low cost, low voltage, small pollution and the like. The micro-grid short-term load prediction is an important precondition for realizing efficient energy saving and optimized operation of the micro-grid, and the accurate prediction of short-term load change has important significance for the arrangement of power generation and supply plans of a power system and the benefit of a power market. Compared with a large power grid, the load of the micro-grid at the user side has the characteristics of large fluctuation, strong uncertainty and low similarity of historical load curves. The load fluctuation of the micro-grid at the user side has a plurality of influencing factors, such as climate conditions, resident electricity habits and economic development degree, wherein the influence of the meteorological factors is obvious.
The prediction method commonly adopted by the micro-grid short-term load comprises a traditional prediction method such as a time sequence method, a similar day method and the like, a modern prediction method such as a gray model, a support vector machine, a neural network and the like, and various combined prediction methods. Most of the methods do not consider or only consider the influence of certain meteorological factors in modeling, so that the accuracy of a prediction result is low, a human body comfort index is introduced for the influence of the meteorological factors on the load, a short-term load prediction method based on the human body comfort index is provided, the human body comfort index in the prior art generally comprises temperature, humidity and wind speed, however, as the industrialization process is accelerated, the urban air pollution situation is serious, the air pollution can bring serious harm to the human body, therefore, the use amount of household appliances, such as a dehumidifier and an air purifier, for improving the living environment is greatly increased, and the load change of a user side micro-grid is further influenced, so that the air quality condition is particularly important to be included in the human body comfort index.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a micro-grid short-term load prediction method based on an improved human comfort index and a gray model, which can rapidly and accurately predict the short-term load of a micro-grid at a user side.
The aim of the invention can be achieved by the following technical scheme: a micro-grid short-term load prediction method, comprising the steps of:
s1, acquiring load history data, and improving the data quality of a singular value interval by adopting a cubic spline difference value;
s2, establishing a gray variation coefficient model based on amplitude compression, and introducing an improved human comfort index considering the air quality condition as an influence factor in the modeling process;
s3, calculating a predicted parameter sequence of the accumulated mutation coefficient sequence and the mutation coefficient sequence;
s4, optimizing the predicted parameter sequence by using a genetic simulated annealing algorithm to obtain optimal matching parameters;
s5, reconstructing the optimal matching parameters to obtain an optimal variation coefficient sequence;
and S6, reversely solving to obtain a load predicted value, and controlling the working state of each distributed power supply of the micro-grid according to the load predicted value.
Preferably, the data quality improvement in step S1 is represented by an original time sequence as a first-order lagrangian interpolation polynomial:
wherein x is an independent variable, x i-1 、x i Is the independent variable of the adjacent non-abnormal value in the abnormal value interval i, f i ′(x i-1 )、f i ′(x i ) For the first derivative value of the adjacent non-outlier value over outlier interval i, f i "x" is the second derivative value of the adjacent non-outlier on outlier interval i, x i+1 Is an independent variable of a non-outlier over the outlier interval i.
Preferably, the improving the human comfort index in the step S2 includes temperature, relative humidity, wind speed and air quality conditions, and the step S2 specifically includes the following steps:
s21, setting a random oscillation sequence;
s22, introducing influencing factors for improving the human comfort index, and establishing a gray variation coefficient model.
Preferably, the random oscillation sequence in step S21 is as follows:
X (0) ＝(x (0) (1),x (0) (2),…x (0) (n))
X (0) D＝(x (0) (1)d,x (0) (2)d,…x (0) (n)d)
wherein X is (0) For randomly oscillating sequences, sequence X (0) D is random oscillation sequence X (0) Is the smoothed sequence of (2), D is the sequence X (0) Is a first order smoothness operator of (a), (0) (k) d is the smoothed sequence X (0) D is an element, k is a constant, D is an element in D, and T is an amplitude.
Preferably, the gray variation coefficient in the step S22 is:
where CV is the coefficient of variation, σ is the standard deviation, and μ is the arithmetic mean.
Preferably, the step S3 specifically includes the following steps:
s31, acquiring an original sequence of a historical variation coefficient, and calculating an oscillation sequence of the historical variation coefficient;
s32, acquiring an original sequence of the mutation variation coefficient, and calculating an oscillation sequence of the mutation variation coefficient.
Preferably, in the step S31, the original sequence of the historical variation coefficient is:
wherein CV 1 (m) is the historical variation coefficient of the previous m time data, sigma m Standard deviation, mu, of the data at the previous m time m Is the expectation of the previous m-time data;
the smoothed sequence of the historical coefficient of variation oscillation sequence is:
CV 1 (0) D＝(CV 1 (0) (1)d,CV 1 (0) (2)d,…CV 1 (0) (n)d)。
wherein CV 1 (0) For historical coefficient of variation sequences, the sequences CV 1 (0) D is oscillation sequence CV 1 (0) Is the smoothed sequence of (2), D is the sequence CV 1 (0) D is the element in D.
Preferably, the original sequence of the mutation coefficient of variation in the step S32 is:
CV 2 (0) ＝(CV 2 (0) (1),CV 2 (0) (2),…，CV 2 (0) (t))
wherein CV 2 (0) The original sequence of the mutation coefficient of variation is shown, and t is a constant;
the smooth sequence of the mutation coefficient of variation oscillation sequence is as follows:
CV 2 (0) D＝(CV 2 (0) (1)d,CV 2 (0) (2)d,…,CV 2 (0) (n)d)
wherein the sequence CV 2 (0) D is oscillation sequence CV 2 (0) Is the smoothed sequence of (2), D is the sequence CV 2 (0) D is the element in D.
Preferably, the step S4 specifically includes the following steps:
s41, randomly generating initial groups according to the predicted parameter sequences of the accumulated mutation coefficient sequence and the mutation coefficient sequence respectively;
s42, evaluating the fitness of individuals in the population;
s43, performing a genetic simulated annealing process: sequentially performing crossing and mutation operations on individuals, and receiving new individuals according to the Metropolis probability to generate next generation new individuals;
s44, judging whether a termination condition is met, executing a step S45 if the termination condition is met, otherwise, returning to the step S42;
s45, outputting the optimal matching parameters.
Compared with the prior art, the invention has the following advantages:
1. the method adopts a method of cubic spline difference value to carry out data quality improvement on the singular value interval, carries out quality improvement on the data set with abnormal values, and reduces the problems of precision deviation and iteration non-convergence caused by data quality by effectively removing the singular values in the load historical data.
2. The invention improves the human comfort index, introduces the index of the air quality condition, is more in line with the use condition of the current micro-grid load, and can effectively reflect the weak change of the initial stage of the load data by adopting the variation coefficient, thereby further improving the accuracy of load prediction.
3. The invention combines the advantages of genetic algorithm and gray model, improves the prediction effect of the historical load sequence with obvious change for short period, can ensure the precision and speed of the load prediction of the micro-grid at the user side, and has excellent practical application value.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a human comfort index model of the present invention;
FIG. 3 is a schematic flow chart of the genetic simulated annealing algorithm of the present invention;
fig. 4 is a graph comparing short-term load prediction results.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples.
As shown in fig. 1, a micro-grid short-term load prediction method includes the following steps:
s1, acquiring load history data, and improving the data quality of a singular value interval by adopting a cubic spline difference value;
s2, establishing a gray variation coefficient model based on amplitude compression, and introducing an improved human comfort index considering the air quality condition as an influence factor in the modeling process;
s3, calculating a predicted parameter sequence of the accumulated mutation coefficient sequence and the mutation coefficient sequence;
s4, optimizing the predicted parameter sequence by using a genetic simulated annealing algorithm to obtain optimal matching parameters;
s5, reconstructing the optimal matching parameters to obtain an optimal variation coefficient sequence;
and S6, reversely solving to obtain a load predicted value, and controlling the working state of each distributed power supply of the micro-grid according to the load predicted value.
In step S1, the embodiment obtains load history data within 100 days of a residential area of a certain micro-grid project in the Shanghai, and then uses a cubic spline difference value to perform the data quality improvement on a singular value interval, specifically, the original time sequence is expressed as a first-order lagrangian interpolation polynomial:
wherein x is an independent variable, x i-1 、x i Is the independent variable of the adjacent non-abnormal value in the abnormal value interval i, f i ′(x i-1 )、f i ′(x i ) For the first derivative value of the adjacent non-outlier value over outlier interval i, f i And (x) is a second derivative value of a non-abnormal value adjacent to the abnormal value interval i, and is obtained after further derivation:
wherein x is i+1 Is an independent variable of a non-outlier over the outlier interval i.
In the step S2 and the step S3, a gray variation coefficient model based on amplitude compression is established, and an improved human comfort index considering the air quality condition is introduced as an influence factor in the modeling process, so as to obtain a predicted parameter sequence of an accumulated variation coefficient sequence and an abrupt variation coefficient sequence.
First, a random oscillation sequence with an amplitude T is set as follows:
X (0) ＝(x (0) (1),x (0) (2),…x (0) (n))
in the formula, the sequence X (0) D＝(x (0) (1)d,x (0) (2)d,…x (0) (n) D) D is the sequence X (0) First order smoothness operator of (a), sequence X (0) D is random oscillation sequence X (0) Is a smooth sequence of (2);
in the method, in the process of the invention,x (0) (k) d is the smoothed sequence X (0) D is an element, k is a constant, D is an element in D, and T is an amplitude.
Then, a gray coefficient of variation model based on amplitude compression is established, and an improved human body comfort index considering the air quality condition is introduced as an influence factor in the modeling process, and fig. 2 shows the improved human body comfort index model including temperature, relative humidity, wind speed and air quality condition.
Wherein, the coefficient of variation formula is:
wherein CV is a coefficient of variation, sigma is a standard deviation, and mu is an arithmetic mean;
finally, a predicted parameter sequence of the accumulated mutation coefficient sequence and the mutation coefficient sequence is obtained:
let the original sequence of the historical coefficient of variation be CV 1 (m) then there are
In CV 1 (m) is the historical variation coefficient of the previous m time data, sigma m Standard deviation, mu, of the data at the previous m time m Is the expectation of the previous m-time data;
the smoothed sequence of the historical coefficient of variation oscillation sequence is:
CV 1 (0) D＝(CV 1 (0) (1)d,CV 1 (0) (2)d,…CV 1 (0) (n)d)
in CV 1 (0) For historical coefficient of variation sequences, the sequences CV 1 (0) D is oscillation sequence CV 1 (0) Is the smoothed sequence of (2), D is the sequence CV 1 (0) D is the element in D.
Let the mutation coefficient of variation sequence be CV 2 (0) CV for effectively reflecting short-period fluctuation of equipment 2 (0) The sliding window principle is adopted to input data in a fixed length, the length of the sliding window is selected to be 3 in consideration of the correlation of the time sequence, and then the original sequence of the mutation coefficient of variation is
CV 2 (0) ＝(CV 2 (0) (1),CV 2 (0) (2),…，CV 2 (0) (t))
In CV 2 (0) The original sequence of the mutation coefficient of variation is shown, and t is a constant;
the smooth sequence of the mutation coefficient of variation oscillation sequence is as follows:
CV 2 (0) D＝(CV 2 (0) (1)d,CV 2 (0) (2)d,…,CV 2 (0) (n)d)
in the formula, the sequence CV 2 (0) D is oscillation sequence CV 2 (0) Is the smoothed sequence of (2), D is the sequence CV 2 (0) D is the element in D.
In step S4, optimizing the obtained parameter sequence by using a genetic simulated annealing algorithm to obtain optimal matching parameters, and firstly, randomly generating initial groups by using the obtained prediction parameter sequences of the accumulated variation coefficient sequence and the mutation variation coefficient sequence respectively; secondly, evaluating the fitness of individuals in the population; then, the individuals are crossed and mutated, and new individuals are accepted according to the Metropolis probability to generate next generation new individuals; and finally, judging whether the termination condition is met or not until the optimal matching parameters are output. As shown in fig. 3, the method specifically comprises the following steps:
s41, randomly generating initial groups according to the predicted parameter sequences of the accumulated mutation coefficient sequence and the mutation coefficient sequence respectively;
s42, evaluating the fitness of individuals in the population;
s43, performing a genetic simulated annealing process: sequentially performing crossing and mutation operations on individuals, and receiving new individuals according to the Metropolis probability to generate next generation new individuals;
s44, judging whether a termination condition is met, executing a step S45 if the termination condition is met, otherwise, returning to the step S42;
s45, outputting the optimal matching parameters.
In step S5 and step S6, the optimal variation coefficient sequence is obtained by reconstructing the optimal matching parameters, the load predicted value is obtained by solving in an inverse way, the predicted result is compared with the predicted result and the actual value of the traditional gray model, the comparison effect is shown in figure 4, no matter the electric load peak period or the electric load valley period is used, compared with the traditional gray model, the predicted result obtained by adopting the method is closer to the actual value, the accuracy of the method for short-term load prediction is verified, and the working state of each distributed power supply is controlled by the micro-grid control system according to the short-term load predicted value obtained by the method, including power generation and scheduling control, so that the high-efficiency energy conservation and the optimal operation of the micro-grid are ensured.
Claims (4)
1. A micro-grid short-term load prediction method, comprising the steps of:
s1, acquiring load history data, and improving the data quality of a singular value interval by adopting a cubic spline difference value;
s2, establishing a gray variation coefficient model based on amplitude compression, and introducing an improved human comfort index considering the air quality condition as an influence factor in the modeling process;
s3, calculating a predicted parameter sequence of the accumulated mutation coefficient sequence and the mutation coefficient sequence;
s4, optimizing the predicted parameter sequence by using a genetic simulated annealing algorithm to obtain optimal matching parameters;
s5, reconstructing the optimal matching parameters to obtain an optimal variation coefficient sequence;
s6, reversely solving to obtain a load predicted value, and making a micro-grid power generation and scheduling plan by a micro-grid control system according to the load predicted value;
the improvement of the human body comfort index in the step S2 comprises the following steps of temperature, relative humidity, wind speed and air quality condition, wherein the step S2 specifically comprises the following steps:
s21, setting a random oscillation sequence;
s22, introducing influencing factors for improving the human comfort index, and establishing a gray variation coefficient model;
the random oscillation sequence in the step S21 is as follows:
X (0) ＝(x (0) (1),x (0) (2),…x (0) (n))
X (0) D＝(x (0) (1)d,x (0) (2)d,…x (0) (n)d)
wherein X is (0) For randomly oscillating sequences, sequence X (0) D is random oscillation sequence X (0) Is the smoothed sequence of (2), D is the sequence X (0) Is a first order smoothness operator of (a), (0) (k) d is the smoothed sequence X (0) The element of D, k is a constant, D is the element in D, and T is the amplitude;
the gray variation coefficient in step S22 is as follows:
wherein CV is a coefficient of variation, σ is a standard deviation, and μ is an arithmetic mean;
the step S3 specifically comprises the following steps:
s31, acquiring an original sequence of a historical variation coefficient, and calculating an oscillation sequence of the historical variation coefficient;
s32, acquiring an original sequence of the mutation variation coefficient, and calculating an oscillation sequence of the mutation variation coefficient;
the step S4 specifically includes the following steps:
s41, randomly generating initial groups according to the predicted parameter sequences of the accumulated mutation coefficient sequence and the mutation coefficient sequence respectively;
s42, evaluating the fitness of individuals in the population;
s43, performing a genetic simulated annealing process: sequentially performing crossing and mutation operations on individuals, and receiving new individuals according to the Metropolis probability to generate next generation new individuals;
s44, judging whether a termination condition is met, executing a step S45 if the termination condition is met, otherwise, returning to the step S42;
s45, outputting the optimal matching parameters.
2. The method according to claim 1, wherein the data quality improvement in step S1 is represented by a first-order lagrangian interpolation polynomial:
wherein x is an independent variable, x i-1 、x i Is the independent variable of the adjacent non-abnormal value in the abnormal value interval i, f i ′(x i-1 )、f i ′(x i ) For the first derivative value of the adjacent non-outlier value over outlier interval i, f i "x" is the second derivative value of the adjacent non-outlier on outlier interval i, x i+1 Is an independent variable of a non-outlier over the outlier interval i.
3. The method for predicting short-term load of a micro-grid according to claim 1, wherein the original sequence of the historical variation coefficients in step S31 is:
wherein CV 1 (m) is the historical variation coefficient of the previous m time data, sigma m Standard deviation, mu, of the data at the previous m time m Is the expectation of the previous m-time data;
the smoothed sequence of the historical coefficient of variation oscillation sequence is:
CV 1 (0) D＝(CV 1 (0) (1)d,CV 1 (0) (2)d,…CV 1 (0) (n)d)
wherein CV 1 (0) For historical coefficient of variation sequences, the sequences CV 1 (0) D is oscillation sequence CV 1 (0) Is the smoothed sequence of (2), D is the sequence CV 1 (0) D is the element in D.
4. The method for predicting short-term load of a micro-grid according to claim 1, wherein the original sequence of mutation coefficient of variation in step S32 is:
CV 2 (0) ＝(CV 2 (0) (1),CV 2 (0) (2),…，CV 2 (0) (t))
wherein CV 2 (0) The original sequence of the mutation coefficient of variation is shown, and t is a constant;
the smooth sequence of the mutation coefficient of variation oscillation sequence is as follows:
CV 2 (0) D＝(CV 2 (0) (1)d,CV 2 (0) (2)d,…,CV 2 (0) (n)d)
wherein the sequence CV 2 (0) D is oscillation sequence CV 2 (0) Is the smoothed sequence of (2), D is the sequence CV 2 (0) First order of (2)And a smoothness operator, wherein D is an element in D.
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