CN113592156A - Power plant coal quantity scheduling method and device, terminal equipment and storage medium - Google Patents
Power plant coal quantity scheduling method and device, terminal equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for scheduling coal amount of a power plant, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring historical sample data; improving an ARI MA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future; forecasting according to the historical sample data and the power consumption forecasting model to obtain a power consumption forecasting value of each power transmission area; analyzing the coal consumption and the coal amount scheduling condition according to the predicted value of the power consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area; adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain an overall predicted coal consumption and an overall predicted scheduling value of the power plant; and controlling the coal quantity scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value. The method has the effects of improving the accuracy of coal consumption prediction of the power plant and further reasonably scheduling the coal consumption.
Description
Technical Field
The invention relates to the technical field of coal consumption prediction, in particular to a method and a device for scheduling coal consumption of a power plant, terminal equipment and a storage medium.
Background
At present, with the pace of power development being accelerated, the scale of a power grid is increased continuously, and multiple indexes are in the top of the world. The construction and development of smart power grids and extra-high voltage power grids have become new characteristics of power grids due to high voltage level, large transmission capacity and extremely complex operating characteristics. Under the policy requirements of new energy utilization, environmental protection, energy-saving power generation dispatching, electric power supervision and the like, the power grid equipment monitoring and power grid dispatching services are also rapidly fused.
Aiming at the aspect of a thermal power plant in a power grid system, the traditional manual experience method cannot accurately judge the coal consumption of the power plant, and meanwhile, the method is lack of strict scientific demonstration and easily causes subjective and one-sided prediction results. Because the coal consumption of the power plant has the characteristic of uncertainty, the problems of resource waste or insufficient reserve are easily caused during the scheduling of the produced coal, so that the abnormal operation of a power grid is caused, and even a major power accident is caused.
Disclosure of Invention
The invention provides a method and a device for scheduling coal consumption of a power plant, terminal equipment and a storage medium, which are used for improving the accuracy of coal consumption prediction of the power plant, further reasonably scheduling the coal consumption and maintaining the normal operation of a power grid.
In a first aspect, an embodiment of the present invention provides a power plant coal quantity scheduling method, including the following steps:
acquiring historical sample data; the historical sample data comprises power utilization time, power utilization type, power utilization address, power utilization amount, coal amount scheduling cost data and coal consumption amount of each power transmission area of the power plant;
improving the ARIMA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future;
predicting according to the historical sample data and a power consumption prediction model to obtain a power consumption prediction value of each power transmission area;
analyzing the coal consumption and the coal amount scheduling condition according to the predicted value of the power consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area;
adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain an overall predicted coal consumption and an overall predicted scheduling value of the power plant;
and controlling the coal quantity scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
In a first implementation manner of the first aspect, after acquiring the historical sample data, the method further includes:
preprocessing the historical sample data; wherein the preprocessing comprises one or more of generating columns, appending, aggregating, deleting, renaming, sorting, filtering, padding, format conversion, and type conversion.
In a second implementation manner of the first aspect, the method further includes:
classifying the historical sample data to obtain discrete data and continuous data;
analyzing the deviation degree between the actual observed value and the theoretical inferred value of the discrete data according to a chi-square test algorithm to obtain the relevance between any two discrete data;
and analyzing the degree of dependence between the continuous data according to a spearman correlation coefficient algorithm to obtain the correlation between any two continuous data.
In a third implementation manner of the first aspect, the analyzing the coal consumption and the coal scheduling condition according to the predicted value of the power consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area specifically includes:
optimizing the predicted value of the power consumption according to the Cplex technology, the coal quantity scheduling cost data and the coal consumption of each power transmission area;
and obtaining the predicted coal consumption and the predicted scheduling value of each power transmission area according to the optimization result.
In a fourth implementation manner of the first aspect, the improving the ARIMA model to obtain the power consumption prediction model specifically includes:
calculating an approximate calculation value of historical sample data at a certain time according to a Nowton-Cotes product-solving formula;
and improving an ARIMA model according to the approximate calculation value to obtain a power consumption prediction model.
In a fifth implementation manner of the first aspect, the power consumption prediction model is specifically:
wherein, ytIs a predicted value of the electricity consumption at a certain moment,
is the autoregressive coefficient to be estimated, theta1,θ2,…θpIs the moving average coefficient to be estimated, etIs noise.
In a second aspect, an embodiment of the present invention provides a power plant coal quantity scheduling device, including:
the sample acquisition module is used for acquiring historical sample data; the historical sample data comprises power utilization time, power utilization type, power utilization address, power utilization amount, coal amount scheduling cost data and coal consumption amount of each power transmission area of the power plant;
the model improvement module is used for improving the ARIMA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future;
the electric quantity prediction module is used for predicting according to the historical sample data and the power consumption prediction model to obtain a power consumption prediction value of each power transmission area;
the coal quantity prediction module is used for analyzing the coal consumption and the coal quantity scheduling condition according to the power consumption prediction value to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area;
the accumulation module is used for adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain the overall predicted coal consumption and the overall predicted scheduling value of the power plant;
and the control module is used for controlling the coal amount scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
In a first implementation form of the second aspect, the model improvement module comprises:
the approximate value calculation unit is used for calculating an approximate calculation value of historical sample data of a certain period of time according to a Nowton-Cotes product calculation formula;
and the model improvement unit is used for improving the ARIMA model according to the approximate calculation value to obtain a power consumption prediction model.
In a third aspect, an embodiment of the present invention further provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the power plant coal quantity scheduling method described in any one of the above is implemented.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, where the computer program, when executed, controls an apparatus where the computer-readable storage medium is located to perform the method for predicting power plant coal consumption according to any one of the above.
The invention provides a method and a device for scheduling coal amount of a power plant, terminal equipment and a storage medium, wherein one embodiment of the method has the following beneficial effects:
1. the classical ARIMA model generally predicts only one time point in prediction, and the prediction is easy to be inaccurate. According to the method, the ARIMA model is improved, so that a power consumption prediction model capable of predicting the power consumption in a certain period of time in the future is obtained, the power consumption is predicted, and the coal consumption scheduling of the power plant are predicted according to the power consumption prediction value.
2. And optimizing the obtained predicted value of the power consumption by adopting a Cplex technology, and then obtaining the coal consumption of the power plant and the coal scheduling prediction result, so that the prediction result is closer to a true value, and the accuracy of the prediction of the coal consumption of the power plant is further improved.
3. The method adopts a spearman correlation coefficient algorithm to judge the dependence degree of two continuous variables, adopts a chi-square test algorithm to count the deviation degree between the actual observed value and the theoretical inferred value of the data, can verify whether the continuous data are correlated or not and whether the discrete data are correlated or not, and further adjusts the power transmission amount and the coal storage amount of the power plant according to the correlation.
Drawings
FIG. 1 is a schematic flow chart of a method for scheduling coal amount in a power plant according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a power plant coal quantity scheduling device according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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, a first embodiment of the present invention provides a power plant coal amount scheduling method, including the following steps:
and S11, acquiring historical sample data.
And S12, improving the ARIMA model to obtain a power consumption prediction model.
And S13, predicting according to the historical sample data and the power consumption prediction model to obtain the power consumption prediction value of each power transmission area.
And S14, analyzing the coal consumption and the coal scheduling condition according to the predicted value of the electricity consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area.
And S15, adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain the overall predicted coal consumption and the overall predicted scheduling value of the power plant.
And S16, controlling the coal amount scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
In step S11, the acquired historical sample data includes the power utilization time, the power utilization type, the power utilization address, the power utilization amount, the coal amount scheduling cost data, and the coal consumption amount of each power transmission area of the power plant. For example, historical sample data of an area A, an area B, an area C, an area D and an area E in Z city are counted respectively, the historical sample data of each area are stored in a single data table, and the data table comprises power utilization time, power utilization type, power utilization address and power utilization amount.
In the data table above, the first column is the time of use, which may be in the standard format of "2019-01-01". The electricity consumption types comprise commercial electricity consumption, residential electricity consumption, office electricity consumption and industrial electricity consumption, and the corresponding electricity consumption types are counted in the second column of the table. The third column counts electricity utilization addresses and represents electricity utilization sections in the corresponding electricity utilization time period A area. And the fourth column counts the electricity consumption of the corresponding electricity utilization section and the corresponding electricity utilization time period. Furthermore, the coal quantity scheduling cost data and the coal consumption are counted according to each region.
The above is a collection method of historical sample data provided in this embodiment, and in other embodiments, data statistics may be performed by using other forms, graphs, and the like, as long as historical sample data can be collected, which is not limited in this disclosure.
In one implementation, before processing the historical sample data, the method further includes:
preprocessing the historical sample data; wherein the preprocessing comprises one or more of generating columns, appending, aggregating, deleting, renaming, sorting, filtering, padding, format conversion, and type conversion.
Specifically, the historical sample data table in the area a does not include information of the power transmission area, and the table data needs to be subjected to column dimension processing, including adding, modifying, deleting, querying and the like of columns. Here, with the addition of columns, the method of withcolumn in spark is selected, and a column with a column name of the power transmission region is added, and the content of the column is the name of the power transmission region to which the column belongs. And respectively adding corresponding area names in the table files of the 5 different areas to obtain 5 tables with 5 columns of data.
Here, the historical sample data is further processed in an addition mode, and the addition is a method for connecting a plurality of data sets to form one data set, and is fusion in a column dimension. The union method is selected for addition, and the number of columns of the required data is the same, and the column names may be different. After different power transmission areas are added, a separate table file is obtained, and the table file has 5 columns, and the 5 columns contain all data of the 5 power transmission areas. In order to further unify the data types, the storage type of the electricity consumption time column data is modified into a Date type by using a formatting method, and the modified column data is stored and displayed in a standard format of '2019-01-01', so that the data can be conveniently processed at a later stage.
Optionally, after the historical sample data is preprocessed, the historical sample data is further processed, so that the historical sample data is displayed, and the data can be conveniently and intuitively observed.
In one implementation, two columns of the power utilization type and the power transmission area are selected from all data table files of 5 columns and 5 power transmission areas obtained in the preprocessing step, and aggregation operation is performed by using a countdisconnect method in spark to obtain a table of the power utilization type, the power transmission area and the count three columns. By this operation, it is convenient to know the number of times each power usage type occurs in the corresponding power transmission region. To facilitate use of the data and display, the column name of the counting column is changed to the number of electricity usage types. Furthermore, the relationship between the power transmission regions and the number of power consumption types can be represented by a two-dimensional stacked graph, so that the frequency of occurrence of different power consumption types in each power transmission region can be more intuitively understood. Some examples of contents in the aggregated table are as follows:
table 1 partial electricity consumption type number table
And adding two columns by using a Withocolumn method in spark to the tables of the power transmission area, the power utilization type and the power utilization type number obtained after the polymerization operation, wherein the names of the two columns are province and city respectively, and the column contents are the province and city to which the column belongs, and the examples are as follows:
| economic | City (R) | Power transmission region | Type of electricity consumption | Number of electricity consumption types |
| Y province | Z city | Zone A | Commercial power | 0 |
| Y province | Z city | Zone B | Commercial power | 1 |
| Y province | Z city | Region C | Commercial power | 0 |
| Y province | Z city | Region D | Commercial power | 1 |
| Y province | Z city | Region E | Commercial power | 1 |
TABLE 2 partial power consumption type number table after adding province and city
Furthermore, a map scatter diagram is drawn by using the place name mapping as a mapping mode, the district as a place name mapping level, the province as a province list name, the city as a city list name, the power transmission area as a district list name, the total statistics as a statistical method and the number of the power consumption types as a statistical column, so that a complete power consumption type number table after the province and the city are added is displayed. Through a map scatter diagram, the number of the power utilization types of each power transmission area can be seen more intuitively on the map, the more the number is, the larger the red point on the map represents, and the less the number is, the smaller the red point on the map represents.
In another implementation manner, in all data table files of 5 columns and 5 power transmission areas obtained in the preprocessing step, the selected power utilization time, the selected power utilization type, the selected power utilization amount and the selected power transmission area are aggregated by a countdisconnect method in spark, so that a table comprising five columns of power utilization time, the selected power utilization type, the selected power utilization amount and the selected power transmission area is obtained. The table is sorted according to the ascending order of the electricity utilization time, and when the electricity utilization time is the same, the table is sorted according to the electricity transmission area. After sorting, a column named as power transmission area _ power consumption is added by a wizardcolumn method, and the content of the column is the corresponding power transmission area _ power consumption filled by a function concat _ ws (the characters in series are _, "power transmission area" and "power consumption"). The storage type of the electricity consumption time column data is modified into a DateTime type by using a formatting method, and the modified column data is stored and displayed in a standard format of '2019-01-0100: 00: 00'.
In step S12, in order to improve the accuracy of the prediction result, the ARIMA model needs to be improved to obtain a power consumption prediction model. The traditional classical ARIMA model generally only predicts one time point in prediction, but sometimes the effect of one point cannot express all the influence on the future, so that the accuracy of the prediction result is low. In this embodiment, the data is predicted by building a power consumption prediction model using model improvements over a period of time accumulation.
Specifically, the conventional classical time series ARIMA prediction model is shown in the following formula (1):
yt＝φ1yt-1+φ2γt-2+…+φpyt-p+et-(θ1et-1+θ2et-2+…+θqet-q) (1)
where p is the number of autoregressive terms and q is the number of moving average terms.
Based on a Nowton-Cotes product-finding formula (also called an equidistant node formula) in numerical analysis, approximate calculation values of historical sample data over a period of time are calculated, and subsequent calculation is carried out by using the approximate calculation values so as to improve the calculation accuracy. The Nowton-Cotes integral formula is shown as formula (2):
wherein [ a, b]Is a finite interval, xi＝x0+ ih (i ═ 0, 1, …, n), h ═ b-a)/n, then
The Cotes coefficient is:
when the values of n are different from each other,
the value of (c) will vary accordingly.
When n is 1, then:
then, a variation of equation (1), i.e. a power prediction model, can be obtained, as shown in equation (3):
wherein, ytIs a predicted value of the electricity consumption at a certain moment,
is the autoregressive coefficient to be estimated, theta1,θ2,…θpIs the moving average coefficient to be estimated, etIs noise.
In step S13, the power consumption prediction is performed using the power consumption prediction model obtained in step S12 based on the sorted history sample data table, and a predicted power consumption value of each power transmission area is obtained. Specifically, the time series used in the power consumption prediction model is the power consumption time series data in table 6, the data series is the count series data, and the key series is the power transmission area _ power consumption series. In the embodiment, the model type is selected to be multificiative, and the model indicates that the prediction data is presented in a Multiplicative mode on the basis of the originally acquired data, and the mode is selected to improve the accuracy of prediction to a certain extent. For example, if the predicted quantity is set to 1, the periodic parameter is selected to 7, and the analysis frequency is selected to day, then the actual data is used to predict the electricity consumption of 7 days in the future. In this embodiment, the power consumption prediction model may be used to predict the maximum value and the minimum value of the power consumption in the future preset time period, in addition to the normal power consumption prediction value, and a prediction table may be established based on table 6 according to the power consumption prediction value, the predicted maximum value, and the predicted minimum value.
In this example, a significance level of 0.05 is used, which means that the error between the predicted data and the originally acquired actual data cannot exceed 5%, and the predicted data is acceptable only if the error is within 5%.
Optionally, after obtaining the prediction result, the correctness of the model may be verified. The data used for verification is data collected in the past time, the data are divided into two parts, one part is used for prediction, and the other part is used for comparing with a prediction result, so that the correctness of the model is verified.
In step S14, it is first necessary to analyze the coal consumption and the coal scheduling according to the predicted value of the electricity consumption. Specifically, in order to further screen the data, redundant columns in the prediction table obtained in step S13 are deleted, and a table including the power consumption time, the power transmission area _ power consumption amount, the power consumption amount prediction value, the prediction maximum value, and the prediction minimum value is obtained. And sorting the table according to the ascending sequence of the power transmission area _ electricity consumption column, and modifying the storage type of the electricity consumption predicted value into Integer by using a formatting method.
After the analysis is completed, the predicted value of the power consumption is optimized according to the Cplex technology and the coal quantity scheduling cost data and the coal consumption of each power transmission area, and the predicted coal consumption and the predicted scheduling value of each power transmission area are obtained according to the optimization result.
Finally, the predicted coal consumption and the predicted scheduling value of each power transmission region are added to obtain the overall predicted coal consumption and the overall predicted scheduling value of the power plant, namely the content in step S15.
And in the step S16, controlling the coal quantity scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value. In practical application, according to the overall predicted coal consumption and the overall predicted scheduling value, the scheduling of the coal amount can be controlled by controlling equipment such as a conveying belt of a power plant for conveying coal.
In order to facilitate an understanding of the invention, some preferred embodiments of the invention will now be described.
In one implementation, the history sample data obtained in step S11 is classified to obtain discrete data and continuous data;
analyzing the deviation degree between the actual observed value and the theoretical inferred value of the discrete data according to a chi-square test algorithm to obtain the relevance between any two discrete data;
and analyzing the degree of dependence between the continuous data according to a spearman correlation coefficient algorithm to obtain the correlation between any two continuous data.
For example, in all data table files of 5 rows of 5 power transmission areas obtained in the preprocessing step, two rows of the power utilization type and the power transmission area are selected to be subjected to chi-square test, the deviation degree between the actual observed value and the theoretical inferred value of the two rows of samples is counted, the possibility of the relationship between the power utilization type and the power transmission area is obtained, the probability of the relationship between the power utilization type and the power transmission area is displayed by a table, whether the discrete data have the relationship or not is verified, and the power transmission amount and the coal storage amount of each power plant are further adjusted according to the relationship between the discrete data and the discrete data.
According to the method for scheduling the coal amount of the power plant, the traditional classical ARIMA model is improved, a power consumption prediction model capable of predicting the power consumption in a certain period of time in the future is obtained, then the power consumption is predicted, then the coal consumption and the coal amount scheduling of the power plant are predicted according to the power consumption prediction value, and accordingly, the accuracy of the coal consumption prediction of the power plant can be improved, the coal amount is reasonably scheduled, and the normal operation of a power grid is maintained. Meanwhile, the Cplex technology is adopted to optimize the predicted value of the power consumption, and then the coal consumption of the power plant and the coal scheduling prediction result are obtained, so that the prediction result is closer to the true value, and the accuracy of the coal consumption prediction of the power plant is further improved.
Referring to fig. 2, a second embodiment of the present invention provides a power plant coal amount scheduling device, including:
the sample acquisition module is used for acquiring historical sample data; the historical sample data comprises power utilization time, power utilization type, power utilization address, power utilization amount, coal amount scheduling cost data and coal consumption amount of each power transmission area of the power plant;
the model improvement module is used for improving the ARIMA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future;
the electric quantity prediction module is used for predicting according to the historical sample data and the power consumption prediction model to obtain a power consumption prediction value of each power transmission area;
the coal quantity prediction module is used for analyzing the coal consumption and the coal quantity scheduling condition according to the power consumption prediction value to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area;
the accumulation module is used for adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain the overall predicted coal consumption and the overall predicted scheduling value of the power plant;
and the control module is used for controlling the coal amount scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
Preferably, the model improvement module comprises:
the approximate value calculation unit is used for calculating an approximate calculation value of historical sample data of a certain period of time according to a Nowton-Cotes product calculation formula;
and the model improvement unit is used for improving the ARIMA model according to the approximate calculation value to obtain a power consumption prediction model.
The embodiment provides a power plant coal volume scheduling device, adopt the model to improve the module and improve traditional classic ARIMA model, and then obtain the power consumption prediction model that can carry out the prediction to the power consumption of a certain period of time in the future, it predicts the power consumption to reuse the power quantity prediction module, then use coal volume prediction module, according to the power consumption prediction value to the coal consumption and the coal volume scheduling of power plant predict, to this, can improve the degree of accuracy of power plant coal consumption prediction, rationally schedule the coal volume, maintain the normal operating of electric wire netting.
The embodiment of the invention also provides the terminal equipment. The terminal device includes: a processor, a memory, and a computer program stored in and executable on the memory, such as the above-described power plant coal quantity scheduling program. When the processor executes the computer program, the steps in the above-mentioned various embodiments of the power plant coal quantity scheduling method, such as step S11 shown in fig. 1, are implemented. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the above-mentioned device embodiments, such as the model improvement module.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the terminal device.
The terminal device can be a desktop computer, a notebook, a palm computer, an intelligent tablet and other computing devices. The terminal device may include, but is not limited to, a processor, a memory. It will be appreciated by those skilled in the art that the above components are merely examples of a terminal device and do not constitute a limitation of a terminal device, and that more or fewer components than those described above may be included, or certain components may be combined, or different components may be included, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal device and connects the various parts of the whole terminal device using various interfaces and lines.
The memory may be used for storing the computer programs and/or modules, and the processor may implement various functions of the terminal device by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the terminal device integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The above-mentioned embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, and it should be understood that the above-mentioned embodiments are only examples of the present invention and are not intended to limit the scope of the present invention. It should be understood that any modifications, equivalents, improvements and the like, which come within the spirit and principle of the invention, may occur to those skilled in the art and are intended to be included within the scope of the invention.
Claims (10)
1. A power plant coal amount scheduling method is characterized by comprising the following steps:
acquiring historical sample data; the historical sample data comprises power utilization time, power utilization type, power utilization address, power utilization amount, coal amount scheduling cost data and coal consumption amount of each power transmission area of the power plant;
improving the ARIMA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future;
predicting according to the historical sample data and a power consumption prediction model to obtain a power consumption prediction value of each power transmission area;
analyzing the coal consumption and the coal amount scheduling condition according to the predicted value of the power consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area;
adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain an overall predicted coal consumption and an overall predicted scheduling value of the power plant;
and controlling the coal quantity scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
2. The power plant coal quantity scheduling method according to claim 1, further comprising, after obtaining historical sample data:
preprocessing the historical sample data; wherein the preprocessing comprises one or more of generating columns, appending, aggregating, deleting, renaming, sorting, filtering, padding, format conversion, and type conversion.
3. The power plant coal quantity scheduling method according to claim 1, further comprising:
classifying the historical sample data to obtain discrete data and continuous data;
analyzing the deviation degree between the actual observed value and the theoretical inferred value of the discrete data according to a chi-square test algorithm to obtain the relevance between any two discrete data;
and analyzing the degree of dependence between the continuous data according to a spearman correlation coefficient algorithm to obtain the correlation between any two continuous data.
4. The power plant coal quantity scheduling method according to claim 1, wherein the analyzing of the coal consumption and the coal quantity scheduling according to the predicted value of the power consumption to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area specifically comprises:
optimizing the predicted value of the power consumption according to the Cplex technology, the coal quantity scheduling cost data and the coal consumption of each power transmission area;
and obtaining the predicted coal consumption and the predicted scheduling value of each power transmission area according to the optimization result.
5. The power plant coal quantity scheduling method according to claim 1, wherein the improvement of the ARIMA model to obtain a power consumption prediction model specifically comprises:
calculating an approximate calculation value of historical sample data at a certain time according to a Nowton-Cotes product-solving formula;
and improving an ARIMA model according to the approximate calculation value to obtain a power consumption prediction model.
6. The power plant coal quantity scheduling method according to claim 5, wherein the power consumption prediction model is specifically:
wherein, ytIs a predicted value of the electricity consumption at a certain moment,
is the autoregressive coefficient to be estimated, theta1,θ2,…θpIs the moving average coefficient to be estimated, etIs noise.
7. The utility model provides a coal amount scheduling device of power plant which characterized in that includes:
the sample acquisition module is used for acquiring historical sample data; the historical sample data comprises power utilization time, power utilization type, power utilization address, power utilization amount, coal amount scheduling cost data and coal consumption amount of each power transmission area of the power plant;
the model improvement module is used for improving the ARIMA model to obtain a power consumption prediction model; the power consumption prediction model is used for predicting the power consumption of a certain period of time in the future;
the electric quantity prediction module is used for predicting according to the historical sample data and the power consumption prediction model to obtain a power consumption prediction value of each power transmission area;
the coal quantity prediction module is used for analyzing the coal consumption and the coal quantity scheduling condition according to the power consumption prediction value to obtain the predicted coal consumption and the predicted scheduling value of each power transmission area;
the accumulation module is used for adding the predicted coal consumption and the predicted scheduling value of each power transmission area to obtain the overall predicted coal consumption and the overall predicted scheduling value of the power plant;
and the control module is used for controlling the coal amount scheduling of the power plant according to the overall predicted coal consumption and the overall predicted scheduling value.
8. The power plant coal quantity scheduling device of claim 7, wherein the model refinement module comprises:
the approximate value calculation unit is used for calculating an approximate calculation value of historical sample data of a certain period of time according to a Nowton-Cotes product calculation formula;
and the model improvement unit is used for improving the ARIMA model according to the approximate calculation value to obtain a power consumption prediction model.
9. A terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing a power plant coal quantity scheduling method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls a device on which the computer-readable storage medium is located to perform the power plant coal amount scheduling method according to any one of claims 1 to 6.
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