CN110991747A - Short-term load prediction method considering wind power plant power - Google Patents
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Abstract
The invention provides a short-term load prediction method considering wind power plant power, which comprises the following steps: calling historical load data of a power grid, and determining key influence factors of the power load of the power grid according to the change condition of the historical load data and the external influence condition at the change moment; constructing a short-term power load prediction model; selecting training data from a historical power load database of a power grid; carrying out preliminary data cleaning on the selected load sequence; the method comprises the steps of carrying out constraint processing on data, setting parameters of a power load prediction model, and training the model according to training data; according to a load prediction equation obtained after model training, carrying out short-term prediction on the power load of the power grid; carrying out short-term prediction on the power of the wind power plant; and superposing the predicted power load of the power grid and the power result of the wind power plant to obtain the short-term equivalent predicted load of the power grid considering the power of the wind power plant.
Description
Technical Field
The invention belongs to the technical field of power load prediction, and particularly relates to a short-term load prediction method considering wind power plant power.
Background
The power load prediction has very important significance in the modern power system and is an important factor influencing the safety and the economy of the power system. The power load is the electric energy consumed in a unit time in a region. In an area, as long as the networked electric equipment consumes electric energy, the total energy consumed is the total load of the area. The development of the modern economic society not only has higher and higher demand on electric power, but also puts higher requirements on the quality of electric energy. The demand for influencing the total amount of electric energy mainly is the demand for electricity consumption of various industries, agriculture and residents, and the demand is influenced by not only time, but also weather, festivals and holidays, and has a very complex change rule. The factor influencing the quality of the electric energy is fluctuation of the on-line electric energy of each power plant, and although the fluctuation is greatly influenced by the power generation equipment, the key factor influencing the fluctuation is the electricity utilization habit of the electricity consumers.
In recent years, wind power generation technology is more and more concerned by people, because of the unique attributes of randomness and uncertainty of wind power, the problem of safety and stability of the wind power accessed to a power grid is caused, and the accurate wind power load prediction technology can provide more accurate data for the power grid and is beneficial to further distribution and scheduling of the power grid.
Disclosure of Invention
The invention provides a short-term load forecasting method considering wind power plant power.
The invention specifically relates to a short-term load prediction method considering wind power plant power, which specifically comprises the following steps:
calling historical load data of a power grid, and determining key influence factors of the power load of the power grid according to the change condition of the historical load data and the external influence condition at the change moment;
step (2), constructing a short-term power load prediction model according to the key influence factors of the power grid power load determined in the step (1);
selecting training data from a historical power load database of a power grid;
step (4), carrying out preliminary data cleaning on the selected load sequence, wherein the preliminary data cleaning comprises sequence noise suppression, missing data repair and abnormal data correction;
step (5), carrying out constraint processing on the data, setting parameters of the power load prediction model, and training the model according to training data;
step (6), according to a load prediction equation obtained after model training, short-term prediction is carried out on the power load of the power grid;
step (7), performing short-term prediction on the power of the wind power plant by adopting a least square support vector machine model;
and (8) superposing the predicted power load of the power grid and the power result of the wind power plant to obtain the short-term equivalent predicted load of the power grid considering the power of the wind power plant.
The key influence factors of the power grid power load in the step (1) comprise air temperature, humidity, weather type, season type, date information, policy information, load value at the previous moment and load value at the same moment in the previous day.
The short-term power load prediction model in the step (2) is determined based on a least squares support vector machine regression model and a radial basis kernel function: y is (q, phi (X)) + b, with an objective function of
Where q is the optimal weight to be sought, b is the linear function threshold, and X ═ X1,x2,…,x8]Is an 8-dimensional vector and represents the input quantity; y is output data and has the unit of MW; e.g. of the typeiF is a penalty factor for allowable error; x is the number of1A predicted air temperature at a predicted time; x is the number of2A predicted humidity for the predicted time; x is the number of3In order to predict the weather type of the day, the digital quantity 0-5 is respectively used for representing sunny days, cloudy days, rainy days, snowy days and typhoon; x is the number of4For predicting the season of the time, the spring, summer, autumn and winter are respectively represented by numerical values 0-3; x is the number of5For predicting the date information of the day, whether the day is weekend or holiday or not is represented, and working days and holidays are represented by numerical values 0 and 1 respectively; x is the number of6In order to predict the policy information of the current day, whether a major event exists or not is represented, and no major event and a major event exist are represented by numerical values 0 and 1 respectively; x is the number of7The load value at the previous moment at the predicted moment is obtained; x is the number of8For predicting the same time of day beforeThe load value of (2).
And (4) suppressing the sequence noise in the step (4) by adopting a mode decomposition mode, wherein the mode obtained by the first decomposition has the smallest time scale, and the first mode is determined as the noise and removed because the power load sequence presents a random distribution characteristic.
The missing data patch in the step (4) performs patch from two aspects of similar day and time sequence, and for similar day prediction, data of a continuous period is divided by one day, so that the load sequence is converted from a row vector to a matrix form:
The abnormal data correction in the step (4) specifically comprises obvious abnormal data removing and missing data repairing, wherein sequences of the obviously abnormal data are removed, and new data are given again to replace the abnormal data in a missing data repairing mode.
In the step (5), the data is subjected to constraint processing, wherein the constraint conditions are that | < q, xi＞+b-yiI.e. not more than ε, i 1, …, l and
i is 1, …, l, where ε is the precision.
The short-term prediction of the power of the wind power plant by adopting the least square support vector machine model in the step (7) specifically comprises the following steps:
acquiring historical power data of a wind power plant, and selecting a training sample;
preprocessing sample data, filling up missing data, and correcting unreasonable data;
carrying out normalization processing on the preprocessed data;
selecting a dimension of the input variables according to the correlation between the input variables;
training a sample, and optimizing parameters of a support vector machine;
respectively predicting the short-term power of each group of fans by using the optimized support vector machine model;
and superposing the short-term power prediction data of each group of fans to obtain the total short-term prediction power of the wind power plant.
Drawings
FIG. 1 is a flow chart of a short term load prediction method considering wind farm power in accordance with the present invention.
Detailed Description
The following describes in detail a specific embodiment of the short-term load prediction method considering wind farm power according to the present invention with reference to the accompanying drawings.
As shown in fig. 1, the prediction method of the present invention includes the following steps: calling historical load data of a power grid, and determining key influence factors of the power load of the power grid according to the change condition of the historical load data and the external influence condition at the change moment; step (2), constructing a short-term power load prediction model according to the key influence factors of the power grid power load determined in the step (1); selecting training data from a historical power load database of a power grid; step (4), carrying out preliminary data cleaning on the selected load sequence, wherein the preliminary data cleaning comprises sequence noise suppression, missing data repair and abnormal data correction; step (5), carrying out constraint processing on the data, setting parameters of the power load prediction model, and training the model according to training data; step (6), according to a load prediction equation obtained after model training, short-term prediction is carried out on the power load of the power grid; step (7), performing short-term prediction on the power of the wind power plant by adopting a least square support vector machine model; and (8) superposing the predicted power load of the power grid and the power result of the wind power plant to obtain the short-term equivalent predicted load of the power grid considering the power of the wind power plant.
The key influence factors of the power grid power load comprise temperature, humidity, weather type, season type, date information, policy information, load value at the previous moment and load value at the same moment in the previous day. Using an 8-dimensional vector X ═ X1,x2,…,x8]To represent the input quantity, x1A predicted air temperature at a predicted time; x is the number of2A predicted humidity for the predicted time; x is the number of3In order to predict the weather type of the day, the digital quantity 0-5 is respectively used for representing sunny days, cloudy days, rainy days, snowy days and typhoon; x is the number of4For predicting the season of the time, the spring, summer, autumn and winter are respectively represented by numerical values 0-3; x is the number of5For predicting the date information of the day, whether the day is weekend or holiday or not is represented, and working days and holidays are represented by numerical values 0 and 1 respectively; x is the number of6In order to predict the policy information of the current day, whether a major event exists or not is represented, and no major event and a major event exist are represented by numerical values 0 and 1 respectively; x is the number of7The load value at the previous moment at the predicted moment is obtained; x is the number of8To predict the load value at the same time of the previous day.
In support vector regression theory, assume xi∈RnTo input, yie.R is the corresponding output, the regression problem is to find the mapping f from input to output Rn→ R, so that f (x) is y. A simple linear regression problem is that y ═ f (x) ═ q · x + b, the purpose is to find the optimal weight q, so that the fitted curve reflects the change law of the data set as much as possible, and for the optimization problem, all training samples can be fitted with the precision ∈. The regression problem is converted into an optimization problem as follows:
the constraint condition is that | < q, xi＞+b-yi|≤ε,i＝1,…,l。
Short-term power load prediction model in the invention is based on minimumDetermining a regression model of a two-times support vector machine and a radial basis kernel function: y is (q, phi (X)) + b, with an objective function of
Where q is the optimal weight to be sought, b is the linear function threshold, and X ═ X1,x2,…,x8]Is an 8-dimensional vector and represents the input quantity; y is output data and has the unit of MW; e.g. of the typeiTo allow for errors, F is a penalty factor. The constraint condition is that | < q, xi＞+b-yiI.e. not more than ε, i 1, …, l and
i is 1, …, l, where ε is the precision.
The sequence noise suppression adopts a mode decomposition mode, the mode obtained by the first decomposition has the minimum time scale, and the first mode is considered as noise and removed because the power load sequence presents a random distribution characteristic. The missing data patching carries out patching from two aspects of similar day and time sequence, for similar day prediction, data in a continuous period is divided according to one day, and then a load sequence is converted into a matrix form from a row vector:
each column represents a similar day sequence; suppose that
And
respectively representing missing data xtThe correction results in both vertical and horizontal directions are the final correction result
For the repair of non-continuous missing data, the repair is only performed from the aspect of similar days. The abnormal data correction specifically comprises obvious abnormal data elimination and missing data repair, wherein sequences of the obvious abnormal data are eliminated, and missing data are adoptedThe way of lost data patching gives new data to replace the abnormal data.
The invention also adopts the least square support vector machine model to carry out short-term prediction on the power of the wind power plant, and the method specifically comprises the following steps: acquiring historical power data of a wind power plant, and selecting a training sample; preprocessing sample data, filling up missing data, correcting unreasonable data, and using the specific method of data preprocessing to refer to the method for repairing missing data and correcting abnormal data; carrying out normalization processing on the preprocessed data; selecting a dimension of the input variables according to the correlation between the input variables; training a sample, and optimizing parameters of a support vector machine; respectively predicting the short-term power of each group of fans by using the optimized support vector machine model; and superposing the short-term power prediction data of each group of fans to obtain the total short-term prediction power of the wind power plant. The output power of the wind turbine is related to historical power, wind speed and air density, and the air density is related to temperature, humidity and pressure. Therefore, the variables input by the wind power prediction model should take several factors influencing the wind power into consideration. However, from the actual operation data of the wind field, the influence of factors such as air density on the output power is not obvious; the measurement of wind direction data has larger influence factors (the sensitivity of a wind vane and a yaw system of a cabin) and cannot correspond to real power in real time, so that the power is predicted by adopting more key wind speed data.
After the short-term power load prediction data of the power grid and the short-term power prediction data of the wind power plant are obtained respectively, the predicted power load of the power grid and the power result of the wind power plant are superposed to obtain the short-term equivalent prediction load of the power grid considering the power of the wind power plant. Compared with the power load directly predicted without considering the power of the wind power plant, the equivalent predicted load has more accurate and practical predicted data.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (8)
1. A short-term load prediction method considering wind power plant power is characterized by comprising the following steps:
calling historical load data of a power grid, and determining key influence factors of the power load of the power grid according to the change condition of the historical load data and the external influence condition at the change moment;
step (2), constructing a short-term power load prediction model according to the key influence factors of the power grid power load determined in the step (1);
selecting training data from a historical power load database of a power grid;
step (4), carrying out preliminary data cleaning on the selected load sequence, wherein the preliminary data cleaning comprises sequence noise suppression, missing data repair and abnormal data correction;
step (5), carrying out constraint processing on the data, setting parameters of the power load prediction model, and training the model according to training data;
step (6), according to a load prediction equation obtained after model training, short-term prediction is carried out on the power load of the power grid;
step (7), performing short-term prediction on the power of the wind power plant by adopting a least square support vector machine model;
and (8) superposing the predicted power load of the power grid and the power result of the wind power plant to obtain the short-term equivalent predicted load of the power grid considering the power of the wind power plant.
2. The method for short-term load forecasting considering wind farm power according to claim 1, characterized in that the key influence factors of the grid power load in step (1) comprise air temperature, humidity, weather type, season type, date information, policy information, load value at the previous moment, and load value at the same moment in the previous day.
3. According to the rightThe method for predicting the short-term load considering the power of the wind power plant, according to claim 2, wherein the short-term power load prediction model in the step (2) is determined based on a least squares support vector machine regression model and a radial basis kernel function: y is (q, phi (X)) + b, with an objective function of
Where q is the optimal weight to be sought, b is the linear function threshold, and X ═ X1,x2,…,x8]Is an 8-dimensional vector and represents the input quantity; y is output data and has the unit of MW; e.g. of the typeiF is a penalty factor for allowable error; x is the number of1A predicted air temperature at a predicted time; x is the number of2A predicted humidity for the predicted time; x is the number of3In order to predict the weather type of the day, the digital quantity 0-5 is respectively used for representing sunny days, cloudy days, rainy days, snowy days and typhoon; x is the number of4For predicting the season of the time, the spring, summer, autumn and winter are respectively represented by numerical values 0-3; x is the number of5For predicting the date information of the day, whether the day is weekend or holiday or not is represented, and working days and holidays are represented by numerical values 0 and 1 respectively; x is the number of6In order to predict the policy information of the current day, whether a major event exists or not is represented, and no major event and a major event exist are represented by numerical values 0 and 1 respectively; x is the number of7The load value at the previous moment at the predicted moment is obtained; x is the number of8To predict the load value at the same time of the previous day.
4. The method for predicting short-term load considering wind farm power according to claim 3, wherein the sequence noise suppression in step (4) adopts a mode decomposition mode, the mode obtained by the first decomposition has the smallest time scale, and the first mode is considered as noise to be removed due to the random distribution characteristic of the power load sequence.
5. The method for short-term load prediction considering wind farm power according to claim 4, wherein the missing data patch in the step (4) is patched from the aspect of similar days and the aspect of time series, and for the similar day prediction, the data of a continuous period is divided by one day, and then the load series is converted from a row vector to a matrix form:
6. The method for predicting the short-term load considering the power of the wind farm according to claim 5, wherein the abnormal data correction in the step (4) specifically comprises obvious abnormal data elimination and missing data repair, the obvious abnormal data is eliminated from a sequence, and new data is given again in a missing data repair mode to replace the abnormal data.
7. The method for predicting short-term load considering wind farm power according to claim 6, wherein the data in the step (5) is subjected to constraint processing, and the constraint condition is | < q, xi＞+b-yiI.e. not more than ε, i 1, …, l and
where ε is the precision.
8. The method for predicting the short-term load considering the power of the wind farm according to claim 7, wherein the step (7) of performing the short-term prediction on the power of the wind farm by using a least squares support vector machine model specifically comprises the following steps:
acquiring historical power data of a wind power plant, and selecting a training sample;
preprocessing sample data, filling up missing data, and correcting unreasonable data;
carrying out normalization processing on the preprocessed data;
selecting a dimension of the input variables according to the correlation between the input variables;
training a sample, and optimizing parameters of a support vector machine;
respectively predicting the short-term power of each group of fans by using the optimized support vector machine model;
and superposing the short-term power prediction data of each group of fans to obtain the total short-term prediction power of the wind power plant.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559502A (en) * | 2020-12-01 | 2021-03-26 | 国能日新科技股份有限公司 | Wind power plant data management system based on time sequence database platform |
| CN115130776A (en) * | 2022-07-13 | 2022-09-30 | 江南大学 | Wind power plant load prediction method based on Laplace asymmetric v-type TSVR |
| CN116258355A (en) * | 2023-05-15 | 2023-06-13 | 国网浙江省电力有限公司永康市供电公司 | Distribution area load curve decomposition method and device suitable for multipoint power estimation |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559502A (en) * | 2020-12-01 | 2021-03-26 | 国能日新科技股份有限公司 | Wind power plant data management system based on time sequence database platform |
| CN115130776A (en) * | 2022-07-13 | 2022-09-30 | 江南大学 | Wind power plant load prediction method based on Laplace asymmetric v-type TSVR |
| CN116258355A (en) * | 2023-05-15 | 2023-06-13 | 国网浙江省电力有限公司永康市供电公司 | Distribution area load curve decomposition method and device suitable for multipoint power estimation |
| CN116258355B (en) * | 2023-05-15 | 2023-08-11 | 国网浙江省电力有限公司永康市供电公司 | Distribution area load curve decomposition method and device suitable for multipoint power estimation |
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