A method, device, electronic device and storage medium for predicting power generation and power price

Abstract

The present invention discloses a method, device, electronic device and storage medium for predicting power generation and power price. The method comprises: determining a first regional data set of a target area at a target time; determining a second regional data set of the target area at at least one historical time and the power generation and power price at at least one historical time; for at least one second regional data set, determining the data similarity between the second regional data set and the first regional data set; determining at least two power generation intervals and at least two power price intervals according to the first regional data set, at least one second regional data set and the data similarity corresponding to each second regional data set; determining at least one power generation support and at least one power price support according to at least two power generation intervals and at least two power price intervals; determining the target power generation and target power price of the target area at the target time according to at least one power generation support and at least one power price support.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, electronic equipment and storage medium for predicting power generation and electricity price. Background Art

[0002] Existing power market-related load forecasting methods are all based on a large number of data sources, and predictions are made through traditional statistical models and time series analysis as well as various machine learning models or deep learning algorithms such as multi-layer perceptron (MLP), recurrent neural network (RNN) and long short-term memory network (LSTM).

[0003] However, some areas rely on hydropower for power generation. Since the upstream and downstream hydropower stations are independent of each other, the power generation-related data information such as upstream water inflow, reservoir capacity and water level are not shared, and the data source cannot be obtained. Therefore, machine learning and deep learning algorithms are difficult to take effect. Therefore, traditional methods are difficult to provide more accurate prediction results when predicting power generation and electricity prices. Summary of the invention

[0004] The present invention provides a method, device, electronic equipment and storage medium for predicting power generation and power price, so as to solve the problem of low accuracy of prediction results when predicting power generation and power price.

[0005] According to one aspect of the present invention, a method for predicting power generation and electricity price is provided, the method comprising:

[0006] Determine a first regional data set of the target area at the target time, the regional data being data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree;

[0007] Determine a second regional data set of the target region at at least one historical moment and power generation and electricity price at at least one historical moment, the historical moment is before the target moment, and the data type of regional data in the first regional data set and the second regional data set is the same;

[0008] For at least one second region data set, determining data similarity between the second region data set and the first region data set;

[0009] Determine at least two power generation intervals and at least two electricity price intervals according to the first regional data set, at least one second regional data set, and the data similarity corresponding to each second regional data set, wherein the power generation interval is an interval in which the power generation of the target region at the target time is located, and the electricity price interval is an interval in which the electricity price of the target region at the target time is located;

[0010] Determine at least one power generation support and at least one power price support according to at least two power generation intervals and at least two power price intervals, the power generation support is determined by judging the overlap between at least two power generation intervals, and the power price support is determined by judging the overlap between at least two power price intervals;

[0011] According to at least one power generation support level and at least one electricity price support level, a target power generation and a target electricity price of a target area at a target time are determined.

[0012] According to another aspect of the present invention, there is provided a device for predicting power generation and power price, the device comprising:

[0013] A first data acquisition module is used to determine a first regional data set of a target area at a target time, wherein the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree;

[0014] A historical data acquisition module, used to determine a second regional data set of a target region at at least one historical moment and a power generation amount and a power price at at least one historical moment, the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same;

[0015] A similarity calculation module, used to determine the data similarity between the second region data set and the first region data set for at least one second region data set;

[0016] An interval determination module, for determining at least two power generation intervals and at least two electricity price intervals according to the first regional data set, at least one second regional data set and the data similarity corresponding to each second regional data set, the power generation interval being the interval in which the power generation of the target region at the target time is located, and the electricity price interval being the interval in which the electricity price of the target region at the target time is located;

[0017] A support determination module, used to determine at least one power generation support and at least one electricity price support according to at least two power generation intervals and at least two electricity price intervals, wherein the power generation support is determined by judging the overlap between at least two power generation intervals, and the electricity price support is determined by judging the overlap between at least two electricity price intervals;

[0018] The data determination module is used to determine the target power generation and target electricity price of the target area at the target time according to at least one power generation support level and at least one electricity price support level.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to at least one processor; wherein,

[0022] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the power generation and electricity price prediction method of any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power generation and electricity price prediction method of any embodiment of the present invention when executed.

[0024] The technical solution of the embodiment of the present invention determines a first regional data set of a target area at a target time, the regional data being data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree; determines a second regional data set of the target area at at least one historical moment and the power generation and electricity price at at least one historical moment, the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same; for at least one second regional data set, determines the data similarity between the second regional data set and the first regional data set, thereby utilizing the overall data similarity between the first regional data set and the second regional data set to avoid the regional data from influencing each other. The problem of excessive errors in the prediction results of power generation and electricity price caused by this is solved, and at least one power generation support and at least one electricity price support are determined according to at least two power generation intervals and at least two electricity price intervals. The power generation support is determined by judging the overlap between at least two power generation intervals, and the electricity price support is determined by judging the overlap between at least two electricity price intervals, thereby ensuring the accuracy of the final result. Finally, the target power generation and target electricity price of the target area at the target time are determined according to at least one power generation support and at least one electricity price support, so that the determination of the target power generation and target electricity price can take into account each power generation interval and electricity price interval as much as possible, thereby ensuring the accuracy of the target power generation and target electricity price.

[0025] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 is a flow chart of a method for predicting power generation and electricity price provided according to the first embodiment of the present invention;

[0028] Figure 2 is a flow chart of another method for predicting power generation and electricity price provided according to the second embodiment of the present invention;

[0029] Figure 3 This is a schematic diagram of the structure of a power generation and electricity price prediction device provided according to Embodiment 3 of the present invention;

[0030] Figure 4 It is a structural schematic diagram of an electronic device for implementing the power generation and electricity price prediction method of an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] Embodiment 1

[0034] Figure 1A flowchart of a method for predicting power generation and power prices is provided for the first embodiment of the present invention. This embodiment is applicable to the case of predicting the power price and power generation of a target area. The method can be performed by a power generation and power price prediction device. The power generation and power price prediction device can be implemented in the form of hardware and / or software. The power generation and power price prediction device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0035] S110, determining a first regional data set of a target area at a target time, where the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree.

[0036] Since there are many factors that affect the power generation and electricity prices in the target area, but not all factors can have a significant impact on the power generation and electricity prices in the target area, it is only necessary to obtain data that has an impact on the power generation and electricity prices in the target area at the target time and the degree of impact is greater than the preset degree as the first area data set.

[0037] The regional data may include the unified load forecast results, new energy load, interconnection line plan, maintenance capacity, and at least one environmental information within a preset range from the target area. The environmental information may be the environmental conditions within the preset range area centered on the target area, including but not limited to the temperature changes in the future preset time and the rainfall in the previous preset days. The unified load forecast result may be the forecast result obtained after predicting the overall power load of the target area. The new energy load may be the power load generated by the new energy power generation equipment during operation. New energy mainly includes wind energy, solar energy and biomass energy. The interconnection line plan may be the planning of transmission lines connecting different power grid areas or power grid systems. The maintenance capacity may refer to the capacity of special equipment in the power system in the target area for planned maintenance or repair. The target time may be the specific time when the power generation and electricity price of the target area are predicted.

[0038] The first area data set of the target area at the target time can be obtained at the target time, or can be obtained from the data predicted by the market for the target area at the target time. The data can be obtained from the official websites providing information in the target area.

[0039] Taking the rainfall in the preset number of days before as an example, the actual rainfall in the preset number of days before the target time can be counted, and the rainfall in the preset number of days before the target time can also be predicted through the weather forecast.

[0040] S120, determining a second regional data set of the target area at at least one historical moment and the power generation and electricity price at at least one historical moment, the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same.

[0041] The historical moment is the moment before the target moment.

[0042] For example, when the target time is February 10, the historical time may be February 1, February 2, ... or February 9, etc., or may be January 1 or December 11 of last year, etc.

[0043] Since the regional data of the target area at the historical moment has been published, it can be directly obtained, and the obtaining method includes but is not limited to obtaining it based on the official website related to power generation in the target area.

[0044] The data type of the regional data in the second regional data set is the same as that in the first regional data set, that is, if the first regional data set contains historical power generation, unified load forecast results, new energy load, interconnection line plan, maintenance capacity, and at least one environmental information within a preset range from the target area, then the second regional data set also contains historical power generation, unified load forecast results, new energy load, interconnection line plan, maintenance capacity, and at least one environmental information within a preset range from the target area. In addition, since the historical moment is a past moment, the power generation and electricity price of the target area have also been determined, and the accurate power generation and electricity price of each historical moment can be obtained at this time.

[0045] S130: For at least one second region data set, determine data similarity between the second region data set and the first region data set.

[0046] Data similarity can be used to measure the similarity between data.

[0047] Since there are many factors that affect power generation and electricity prices, and regional data will affect each other, any data in the regional data will eventually affect power generation and electricity prices. Therefore, when using the second regional data set at a historical moment to predict power generation and electricity prices, it is necessary to consider the data similarity between the first regional data set and the second regional data set at the target moment.

[0048] In this regard, similarity calculation can be performed between the first regional data set and the corresponding regional data in the second regional data set, and then the data similarity between the first regional data set and the second regional data set as a whole can be determined based on the similarity calculation results of the respective regional data. Thus, by utilizing the data similarity between the first regional data set and the second regional data set as a whole, the problem of excessive errors in the prediction results of power generation and electricity prices caused by the mutual influence of regional data can be avoided.

[0049] Exemplarily, it is taken that the first regional data set and the second regional data set only include historical power generation and coordinated load forecasting results.

[0050] At this time, the similarity between the historical power generation in the first regional data set and the historical power generation in the second regional data set, as well as the similarity between the unified load forecast result in the first regional data set and the unified load forecast result in the second regional data set can be calculated respectively, and similarity A and similarity B are obtained respectively. Based on similarity A and similarity B, the overall data similarity between the first regional data set and the second regional data set is determined.

[0051] Optionally, determining the overall data similarity between the first regional data set and the second regional data set according to the similarity calculation result of each regional data includes:

[0052] A regional weight is assigned to each regional data, and a weighted sum is performed on the similarity calculation results of each regional data according to the regional weight to determine the overall data similarity between the first regional data set and the second regional data set.

[0053] Optionally, the similarity of the data of each region may be calculated by using Euclidean distance, cosine similarity, Pearson correlation coefficient, and the like.

[0054] S140. Determine at least two power generation intervals and at least two electricity price intervals based on the first area data set, at least one second area data set, and the data similarity corresponding to each second area data set. The power generation interval is the interval in which the power generation of the target area at the target time is located, and the electricity price interval is the interval in which the electricity price of the target area at the target time is located.

[0055] The power generation range may be a range value of the power generation of the target area at the target time. The power price range may be a range value of the power price of the target area at the target time.

[0056] There are at least two methods to determine the power generation range and the electricity price range.

[0057] The first method: taking the data similarity corresponding to each second regional data set as the similarity between the power generation and electricity price of each second regional data set and the power generation and electricity price of the first regional data set at the target time, at this time, based on the power generation and electricity price of each second regional data set and each similarity, determine multiple power generation and multiple electricity prices of the first regional data set at the target time, thereby obtaining the power generation range and electricity price range.

[0058] The second method is to conduct empirical analysis on the first regional data set and at least one second regional data set based on the working experience of the staff, and determine at least two power generation ranges and at least two electricity price ranges.

[0059] At this time, the first result and the second result are summarized to obtain at least two power generation ranges and at least two electricity price ranges.

[0060] By determining at least two power generation intervals and at least two electricity price intervals based on the work experience of the staff and the data similarity, it is possible to ensure more interval determination methods and retain the results of multiple prediction methods, thereby improving the coverage of power generation intervals.

[0061] S150. Determine at least one power generation support level and at least one electricity price support level based on at least two power generation intervals and at least two electricity price intervals. The power generation support level is determined by judging the overlap between at least two power generation intervals in pairs, and the electricity price support level is determined by judging the overlap between at least two electricity price intervals in pairs.

[0062] The power generation support degree can be the ratio of the overlapping part of one power generation interval and another power generation interval to the whole of the power generation interval and the other power generation interval, which is used to describe the support degree of one power generation interval for another power generation interval.

[0063] Since different power generation ranges are not exactly the same, electricity price ranges are not exactly the same, and it is difficult to determine the accuracy of each power generation range and electricity price range, in order to ensure the accuracy of the final result, it is necessary to determine at least one power generation support degree and at least one electricity price support degree, so as to obtain the degree of mutual support between power generation ranges and the degree of mutual support between electricity price ranges.

[0064] For this purpose, at least one power generation support is determined by judging the overlap between at least two power generation intervals, and at least one power price support is determined by judging the overlap between at least two power price intervals.

[0065] For example, taking power generation interval A and power generation interval B as examples, power generation interval A is [1,3], and power generation interval B is [2,4]. At this time, it can be determined that the overlapping interval of power generation interval A and power generation interval B is [2,3]. At this time, the power generation support between power generation interval A and power generation interval B is 0.33 through (3-2) / (4-1).

[0066] In an optional solution, determining at least one power generation support level according to at least two power generation intervals may include steps A1-A4:

[0067] Step A1: determine the normal probability interval of power generation corresponding to each power generation interval.

[0068] Step A2: determine the power generation probability density function corresponding to each power generation normal probability interval.

[0069] Step A3, determining at least one power generation probability density union and at least one power generation probability density intersection, the power generation probability density union is obtained by performing pairwise union of each power generation probability density function, and the power generation probability density intersection is obtained by performing pairwise intersection of each power generation probability density function.

[0070] Step A4: Determine at least one power generation support according to at least one power generation probability density union and at least one power generation probability density intersection, wherein the power generation support is determined by the ratio of the power generation probability density union to the corresponding power generation probability density intersection.

[0071] The normal probability interval of power generation can be determined by the power generation interval, and the probability interval of power generation in the target area at the target time. The power generation probability density function can be the interval of probability density corresponding to the normal probability of power generation.

[0072] After obtaining each power generation interval, it can be considered that the power generation of the target area at the target time is between the maximum and minimum values ​​of the power generation interval. At this time, the power generation of the target area at the target time is considered as a power generation random variable. At this time, the corresponding power generation normal probability interval can be generated through the power generation interval:

[0073] ;

[0074] in, Indicates the maximum value of the power generation range; Indicates the minimum value of the power generation range; represents the random variable of the power generation in the g-th power generation interval; I represents the number of each power generation interval; g represents the g-th power generation interval; is the confidence that the power generation random variable is in the gth power generation interval.

[0075] According to the central limit theorem, the random variable of power generation in the gth power generation interval can be considered to be The obedience parameters are , Normal distribution . Because of the symmetry of the normal distribution, the mean The midpoint of the power generation range can be taken, that is:

[0076] ;

[0077] When known After that, the variance The probability density function of power generation can be obtained by interval integration: , where is the probability density function of the normal distribution of power generation in the g-th power generation interval.

[0078] For the random variable of power generation in the gth power generation interval And the random variable of power generation in the hth power generation interval , can be represented by the corresponding normal probability density, which are as well as , where h, g=1,…,I.

[0079] At this time, the power generation support can be determined by the ratio of the intersection and union of the area enclosed by the two probability density functions and the horizontal axis. It can be determined by the following formula:

[0080] ;

[0081] In the formula, for and The area of ​​the intersection; for and The area of ​​the union; for right Power generation support.

[0082] To facilitate subsequent understanding, Abbreviated as .

[0083] In an optional solution, determining at least one electricity price support level according to at least two electricity price intervals may include steps B1-B4:

[0084] Step B1: determine the normal probability interval of electricity price corresponding to each electricity price interval.

[0085] Step B2: determine the electricity price probability density function corresponding to each electricity price normal probability interval.

[0086] Step B3, determine at least one electricity price probability density union and at least one electricity price probability density intersection, the electricity price probability density union is obtained by performing pairwise union of each electricity price probability density function, and the electricity price probability density intersection is obtained by performing pairwise intersection of each electricity price probability density function.

[0087] Step B4: Determine at least one electricity price support based on at least one electricity price probability density union and at least one electricity price probability density intersection, where the electricity price support is determined by the ratio of the electricity price probability density union to the corresponding electricity price probability density intersection.

[0088] The determination of electricity price support is the same as that of power generation support, and will not be elaborated in this application.

[0089] S160: Determine a target power generation and a target electricity price for a target area at a target time according to at least one power generation support level and at least one electricity price support level.

[0090] The target power generation may be the predicted power generation of the target area at the target time. The target electricity price may be the predicted electricity price of the target area at the target time.

[0091] After obtaining at least one power generation support, the power generation support between each power generation interval can be clarified. At this time, the weight of each power generation interval can be determined according to the power generation support. According to the weight of each power generation interval and the median of each power generation interval, the target power generation of the target area at the target time is determined. Similarly, the target electricity price can also be obtained.

[0092] Optionally, the lower limit of the target power generation in the target area at the target time is determined based on the weight of each power generation interval and the lower limit of each power generation interval, and the upper limit of the target power generation in the target area at the target time is determined based on the weight of each power generation interval and the upper limit of each power generation interval, thereby obtaining the interval in which the target power generation is located. Similarly, the interval in which the target electricity price is located can also be obtained.

[0093] By determining the target power generation and target electricity price of the target area at the target time based on at least one power generation support level and at least one electricity price support level, various power generation ranges and electricity price ranges can be considered as much as possible when determining the target power generation and target electricity price, thereby ensuring the accuracy of the results of determining the target power generation and target electricity price.

[0094] In an optional solution, determining the target power generation and target electricity price of the target area at the target time according to at least one power generation support and at least one electricity price support may include steps C1-C5:

[0095] Step C1: forming a first power generation support matrix and a first electricity price support matrix from at least one power generation support and at least one electricity price support.

[0096] Step C2: normalize the first power generation support matrix and the first electricity price support matrix respectively to generate a second power generation support matrix and a second electricity price support matrix.

[0097] Step C3: Generate a basic probability density distribution function of power generation according to the second power generation support matrix and each power generation normal probability interval.

[0098] Step C4: Generate a basic probability density distribution function of electricity price according to the second electricity price support matrix and the normal probability intervals of each electricity price.

[0099] Step C5: Determine the target power generation and target electricity price of the target area at the target time according to the basic probability density distribution function of power generation and the basic probability density distribution function of electricity price.

[0100] The first power generation support matrix may be a matrix composed of various power generation support levels. The first electricity price support matrix may be a matrix composed of various electricity price support levels.

[0101] After obtaining at least one power generation support factor, the power generation support factors are combined to form a first power generation support factor matrix.

[0102] The first power generation support matrix can be in the following form:

[0103] ;

[0104] Where CM represents the first power generation support matrix. is the support degree of the first power generation interval to the i-th power generation interval.

[0105] The first power generation support matrix is ​​normalized to obtain the second power generation support matrix. The second power generation support matrix is ​​recorded as . New Elements The expression is:

[0106] ;

[0107] After the normalization process, each element in the second power generation support matrix can be regarded as the initial weight of the power generation interval corresponding to the element.

[0108] For example, CM is a 3*3 matrix, and the matrix is ​​as follows:

[0109] ;

[0110] At this time, the matrix is ​​normalized to obtain the following formula:

[0111] ; At this time, the sum of each row in the matrix is ​​1.

[0112] After obtaining the second power generation support matrix, the basic probability density allocation function of power generation will be generated according to the second power generation support matrix and the normal probability interval of each electricity price. Among them, the basic probability density allocation function of power generation is m:

[0113] ;

[0114] in, for , represents the probability density distribution set of each power generation.

[0115] Among them, the determination of the basic probability density distribution function of electricity price is the same as the basic probability density distribution function of power generation.

[0116] According to the basic probability density distribution function of electricity price and the basic probability density distribution function of power generation, the target power generation and target electricity price of the target area at the target time are determined.

[0117] In an optional solution, the target power generation and target electricity price of the target area at the target time are determined according to the basic probability density distribution function of power generation and the basic probability density distribution function of electricity price, which may include D1-D3:

[0118] Step D1: Based on the DS evidence theory, the basic probability density distribution function of power generation is synthesized until the number of synthesis reaches a preset number, and the power generation synthesis expression is obtained.

[0119] Step D2: Based on the DS evidence theory, the basic probability density distribution function of the electricity price is synthesized until the number of synthesized times reaches a preset number, and the electricity price synthesis expression is obtained.

[0120] Step D3: According to the power generation synthesis expression and the power price synthesis expression, the target power generation and the target power price of the target area at the target time are determined respectively.

[0121] The preset number is the number of power generation intervals minus 1 or the number of electricity price intervals minus 1.

[0122] Based on the DS evidence theory, the basic probability density distribution function of power generation is synthesized. After a preset number of synthesis, the following formula can be obtained:

[0123] ;

[0124] In the formula, Indicates that the g-th power generation interval considers the power generation random variable Follow the distribution The probability of being true.

[0125] After performing the DS evidence theory for a preset number of times, the weights of each power generation interval will be adjusted.

[0126] For example, after obtaining the normalized After that, the DS evidence theory will be performed for a preset number of times, resulting in the following formula:

[0127] [0.61326 0.14128 0.24545], as the synthesis result of DS evidence theory.

[0128] Similarly, the synthesis process of the basic probability density distribution function of electricity price is the same as that of the basic probability density distribution function of power generation.

[0129] According to the above formula, the following matrix can be generated:

[0130] m(A1)=A1[a11,a12,…,a1I];

[0131] In the formula, a11+a12+…+a1I=1.

[0132] From the above formula, we can see the degree of support for each power generation interval. Then, by multiplying the degree of support for each power generation interval by the power generation interval and summing them up, we can get the target power generation.

[0133] According to the above formula, the target power generation can be obtained as:

[0134] ;

[0135] in, represents the target power generation; Represents the weight of each power generation interval, A1 is composed of composition; Represents the median of each power generation range.

[0136] Exemplarily, the medians in the power generation interval are a1, a2, and a3, respectively, and the synthesis result of DS evidence theory is [0.61326 0.14128 0.24545].

[0137] At this time, the result of 0.61326* a1+0.14128* a2+0.24545* a3 can be used as the target power generation.

[0138] Optionally, after obtaining the synthesis result of the DS evidence theory, the weight of each power generation interval is multiplied by the lower limit of the corresponding power generation interval as the lower limit of the target power generation, and the weight of each power generation interval is multiplied by the upper limit of the corresponding power generation interval as the upper limit of the target power generation, thereby obtaining the power generation interval of the target power generation.

[0139] Exemplarily, the power generation intervals are [a1, a2], [a3, a4], and [a5, a6], and the synthesis result of DS evidence theory is [0.61326 0.14128 0.24545].

[0140] The lower limit of the target power generation range is: 0.61326* a1+0.14128* a3+0.24545* a5.

[0141] The upper limit of the target power generation range is: 0.61326* a2+0.14128* a4+0.24545* a6.

[0142] According to the technical solution of the embodiment of the present invention, a first regional data set of a target area at a target time is determined, and the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree; a second regional data set of the target area at at least one historical moment and the power generation and electricity price at at least one historical moment are determined, the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same; for at least one second regional data set, the data similarity between the second regional data set and the first regional data set is determined, thereby utilizing the overall data similarity between the first regional data set and the second regional data set to avoid mutual influence of regional data The problem of excessive errors in the prediction results of power generation and electricity price is solved, and at least one power generation support and at least one electricity price support are determined according to at least two power generation intervals and at least two electricity price intervals. The power generation support is determined by judging the overlap between at least two power generation intervals, and the electricity price support is determined by judging the overlap between at least two electricity price intervals, thereby ensuring the accuracy of the final result. Finally, the target power generation and target electricity price of the target area at the target time are determined according to at least one power generation support and at least one electricity price support, so that the determination of the target power generation and target electricity price can take into account each power generation interval and electricity price interval as much as possible, thereby ensuring the accuracy of the target power generation and target electricity price.

[0143] Embodiment 2

[0144] Figure 2This is a flowchart of another method for predicting power generation and electricity prices provided in the second embodiment of the present invention. This embodiment further optimizes the process of determining the data similarity between the second regional data set and the first regional data set in the above embodiment on the basis of the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the power generation and electricity price prediction method of this embodiment may include the following steps:

[0145] S210, determining a first regional data set of a target area at a target time, where the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree.

[0146] S220, determining a second regional data set of the target area at at least one historical moment and the power generation and electricity price at at least one historical moment, the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same.

[0147] S230: Determine the similarity between each region data in the first region data set and the corresponding region data in the second region data set.

[0148] S240: Multiply the similarities of the respective regional data in the first regional data set to obtain data similarities between the second regional data set and the first regional data set.

[0149] When calculating the similarity between each second regional data set and the first regional data set, loss and distortion of regional data may occur between the first regional data set and each second regional data set, which will lead to large errors in the results of determining the similarity between each second regional data set and the first regional data set.

[0150] For this purpose, the similarity between each area data in the first area data set and the corresponding area data in the second area data set is preliminarily calculated, and then the similarity of each area data in the second area data set is multiplied to obtain the data similarity of the second area data set.

[0151] S250 , sorting each second region data set according to the data similarity between each second region data set and the first region data set to obtain a sorting result.

[0152] S260: Determine, according to the sorting result, a second area data set located at a previous preset position as a target second area data set.

[0153] When the data similarity is too low, it is difficult to accurately predict the target power generation and target electricity price at the target time. Therefore, it is necessary to screen the second regional data set to improve the accuracy of the prediction result.

[0154] In this regard, the second regional data sets are sorted from large to small according to the data similarity between each second regional data set and the first regional data set. According to the sorting result, the second regional data set at the preset position is selected as the target second regional data set.

[0155] S270: Determine at least two power generation ranges and at least two electricity price ranges according to the first area data set, at least one target second area data set, and data similarities corresponding to each target second area data set.

[0156] S280. Determine at least one power generation support level and at least one electricity price support level based on at least two power generation intervals and at least two electricity price intervals. The power generation support level is determined by judging the overlap between at least two power generation intervals in pairs, and the electricity price support level is determined by judging the overlap between at least two electricity price intervals in pairs.

[0157] S290: Determine a target power generation and a target electricity price for a target area at a target time according to at least one power generation support level and at least one electricity price support level.

[0158] According to the technical solution of the embodiment of the present invention, by determining the similarity between each regional data in the first regional data set and the corresponding regional data in the second regional data set; multiplying the similarity of each regional data in the first regional data set to obtain the data similarity between the second regional data set and the first regional data set, it is avoided that the loss and distortion of regional data in the first regional data set and each second regional data set may cause a large error in the determination result of the similarity between each second regional data set and the first regional data set.

[0159] Embodiment 3

[0160] Figure 3 The present invention provides a block diagram of a power generation and electricity price prediction device, which can be applied to the situation of predicting the electricity price and power generation of a target area. The power generation and electricity price prediction device can be implemented in the form of hardware and / or software, and can be configured in an electronic device with data processing capabilities. Figure 3 As shown, the power generation and electricity price prediction device of this embodiment may include: a first data acquisition module 310, a historical data acquisition module 320, a similarity calculation module 330, an interval determination module 340, a support determination module 350 and a data determination module 360. Among them:

[0161] A first data acquisition module 310 is used to determine a first regional data set of a target area at a target time, where the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree;

[0162] A historical data acquisition module 320, for determining a second regional data set of a target region at at least one historical moment and a power generation amount and a power price at at least one historical moment, the historical moment being before the target moment, and the data type of the regional data in the first regional data set and the second regional data set being the same;

[0163] A similarity calculation module 330 is used to determine the data similarity between the second region data set and the first region data set for at least one second region data set;

[0164] The interval determination module 340 is used to determine at least two power generation intervals and at least two electricity price intervals according to the first regional data set, at least one second regional data set and the data similarity corresponding to each second regional data set, the power generation interval is the interval in which the power generation of the target area at the target time is located, and the electricity price interval is the interval in which the electricity price of the target area at the target time is located;

[0165] A support determination module 350 is used to determine at least one power generation support and at least one electricity price support according to at least two power generation intervals and at least two electricity price intervals, wherein the power generation support is determined by judging the overlap between at least two power generation intervals, and the electricity price support is determined by judging the overlap between at least two electricity price intervals;

[0166] The data determination module 360 ​​is used to determine the target power generation and target electricity price of the target area at the target time according to at least one power generation support level and at least one electricity price support level.

[0167] Based on the above embodiment, optionally, the support determination module 350 includes:

[0168] Determine the normal probability interval of power generation corresponding to each power generation interval;

[0169] Determine the power generation probability density function corresponding to each power generation normal probability interval;

[0170] Determine at least one power generation probability density union and at least one power generation probability density intersection, the power generation probability density union is obtained by performing pairwise union of each power generation probability density function, and the power generation probability density intersection is obtained by performing pairwise intersection of each power generation probability density function;

[0171] At least one power generation support is determined according to at least one power generation probability density union and at least one power generation probability density intersection, and the power generation support is determined by the ratio of the power generation probability density union and the corresponding power generation probability density intersection.

[0172] Based on the above embodiment, optionally, the support determination module 350 includes:

[0173] Determine the normal probability interval of electricity price corresponding to each electricity price interval;

[0174] Determine the electricity price probability density function corresponding to each electricity price normal probability interval;

[0175] Determine at least one electricity price probability density union and at least one electricity price probability density intersection, the electricity price probability density union is obtained by performing pairwise union of each electricity price probability density function, and the electricity price probability density intersection is obtained by performing pairwise intersection of each electricity price probability density function;

[0176] At least one electricity price support is determined according to at least one electricity price probability density union and at least one electricity price probability density intersection, and the electricity price support is determined by the ratio of the electricity price probability density union and the corresponding electricity price probability density intersection.

[0177] Based on the above embodiment, optionally, the data determination module 360 ​​includes:

[0178] A first power generation support matrix and a first electricity price support matrix are formed by at least one power generation support and at least one electricity price support;

[0179] Normalizing the first power generation support matrix and the first electricity price support matrix respectively to generate a second power generation support matrix and a second electricity price support matrix;

[0180] Generate a basic probability density distribution function of power generation according to the second power generation support matrix and each power generation normal probability interval;

[0181] Generate a basic probability density distribution function of electricity price according to the second electricity price support matrix and normal probability intervals of various electricity prices;

[0182] According to the basic probability density distribution function of power generation and the basic probability density distribution function of electricity price, the target power generation and target electricity price of the target area at the target time are determined.

[0183] On the basis of the above embodiment, optionally, according to the basic probability density distribution function of power generation and the basic probability density distribution function of electricity price, the target power generation and the target electricity price of the target area at the target time are determined, including:

[0184] Based on the DS evidence theory, the basic probability density distribution function of power generation is synthesized until the number of synthesis reaches the preset number, and the power generation synthesis expression is obtained;

[0185] Based on the DS evidence theory, the basic probability density distribution function of electricity price is synthesized until the number of synthesis reaches the preset number, and the synthesis expression of electricity price is obtained;

[0186] According to the synthetic expression of power generation and the synthetic expression of electricity price, the target power generation and target electricity price of the target area at the target time are determined respectively.

[0187] Based on the above embodiment, optionally, the similarity calculation module 330 includes:

[0188] Determine the similarity between each region data in the first region data set and the corresponding region data in the second region data set;

[0189] The similarities of the respective regional data in the first regional data set are multiplied to obtain the data similarities between the respective second regional data sets and the first regional data set.

[0190] Based on the above embodiment, optionally, after determining the data similarity between the second region data set and the first region data set, the method further includes:

[0191] Sorting each second region data set according to the data similarity between each second region data set and the first region data set to obtain a sorting result;

[0192] According to the sorting result, a second area data set located at a previous preset position is determined as a target second area data set;

[0193] Accordingly, the interval determination module 340 includes:

[0194] At least two power generation ranges and at least two electricity price ranges are determined according to the first area data set, at least one target second area data set, and data similarities corresponding to each target second area data set.

[0195] The power generation and electricity price prediction device provided in the embodiment of the present invention can execute the power generation and electricity price prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0196] Embodiment 4

[0197] Figure 4A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0198] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0199] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0200] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power generation and electricity price prediction method.

[0201] In some embodiments, the power generation and electricity price prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power generation and electricity price prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the power generation and electricity price prediction method in any other appropriate manner (for example, by means of firmware).

[0202] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0203] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0204] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0205] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0206] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0207] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0208] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0209] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting power generation and electricity price, characterized in that: include: Determine a first regional data set of a target area at a target time, wherein the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree; Determine a second regional data set of a target region at at least one historical moment and a power generation amount and an electricity price at at least one historical moment, wherein the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same; For at least one second regional data set, determining data similarity between the second regional data set and the first regional data set; Determine at least two power generation intervals and at least two electricity price intervals according to the first regional data set, the at least one second regional data set, and the data similarity corresponding to each of the second regional data sets, wherein the power generation interval is an interval in which the power generation of the target region at the target time is located, and the electricity price interval is an interval in which the electricity price of the target region at the target time is located; Determine at least one power generation support and at least one power price support according to the at least two power generation intervals and the at least two power price intervals, wherein the power generation support is determined by judging the overlap between the at least two power generation intervals, and the power price support is determined by judging the overlap between the at least two power price intervals; Determining a target power generation amount and a target electricity price for the target area at a target time according to the at least one power generation amount support level and the at least one electricity price support level; Wherein, determining at least one power generation support degree according to the at least two power generation intervals includes: Determine the normal probability interval of power generation corresponding to each of the power generation intervals; Determine the power generation probability density function corresponding to each of the power generation normal probability intervals; Determine at least one power generation probability density union and at least one power generation probability density intersection, wherein the power generation probability density union is obtained by performing pairwise union of each of the power generation probability density functions, and the power generation probability density intersection is obtained by performing pairwise intersection of each of the power generation probability density functions; Determining at least one power generation support according to the at least one power generation probability density union and the at least one power generation probability density intersection, the power generation support being determined by a ratio of the power generation probability density union to the corresponding power generation probability density intersection; Wherein, determining at least one electricity price support degree according to the at least two electricity price ranges includes: Determine the normal probability interval of electricity price corresponding to each of the electricity price intervals; Determine the electricity price probability density function corresponding to each of the electricity price normal probability intervals; Determine at least one electricity price probability density union and at least one electricity price probability density intersection, the electricity price probability density union is obtained by performing pairwise union of each of the electricity price probability density functions, and the electricity price probability density intersection is obtained by performing pairwise intersection of each of the electricity price probability density functions; At least one electricity price support is determined based on the at least one electricity price probability density union and the at least one electricity price probability density intersection, and the electricity price support is determined by the ratio of the electricity price probability density union and the corresponding electricity price probability density intersection.

2. The method according to claim 1, characterized in that Determining a target power generation amount and a target electricity price of the target area at a target time according to the at least one power generation amount support and the at least one electricity price support, comprising: The at least one power generation support and the at least one electricity price support form a first power generation support matrix and a first electricity price support matrix; Normalizing the first power generation support matrix and the first electricity price support matrix respectively to generate a second power generation support matrix and a second electricity price support matrix; Generate a basic probability density distribution function of power generation according to the second power generation support matrix and each of the power generation normal probability intervals; Generate a basic probability density distribution function of electricity price according to the second electricity price support matrix and each of the normal probability intervals of the electricity price; The target power generation and the target electricity price of the target area at the target time are determined according to the basic probability density distribution function of power generation and the basic probability density distribution function of electricity price.

3. The method according to claim 2, characterized in that Determining the target power generation and the target electricity price of the target area at the target time according to the power generation basic probability density distribution function and the electricity price basic probability density distribution function includes: Based on the DS evidence theory, the basic probability density distribution function of power generation is synthesized until the number of synthesis reaches a preset number, and a synthesis expression of power generation is obtained; Based on the DS evidence theory, the basic probability density distribution function of the electricity price is synthesized until the number of synthesis reaches a preset number, and a synthesis expression of the electricity price is obtained; According to the power generation synthesis expression and the electricity price synthesis expression, the target power generation and the target electricity price of the target area at the target time are determined respectively.

4. The method according to claim 1, characterized in that: Determining the data similarity between the second regional data set and the first regional data set includes: Determine the similarity between each region data in the first region data set and the corresponding region data in the second region data set; The similarities of the respective regional data in the first regional data set are multiplied to obtain the data similarity between the second regional data set and the first regional data set.

5. The method according to claim 4, characterized in that After determining the data similarity between the second regional data set and the first regional data set, the method further includes: sorting each of the second region data sets according to the data similarity between each of the second region data sets and the first region data set to obtain a sorting result; According to the sorting result, determining the second regional data set located at a previous preset position as a target second regional data set; Correspondingly, determining at least two power generation ranges and at least two electricity price ranges according to the first regional data set, the at least one second regional data set, and the data similarity corresponding to each of the second regional data sets includes: At least two power generation ranges and at least two electricity price ranges are determined according to the first regional data set, the at least one target second regional data set, and the data similarities corresponding to each of the target second regional data sets.

6. A device for predicting power generation and electricity price, characterized in that: include: A first data acquisition module, used to determine a first regional data set of a target area at a target time, wherein the regional data is data that has an impact on the power generation and electricity price of the target area at the target time and the impact degree is greater than a preset degree; A historical data acquisition module, used to determine a second regional data set of a target region at at least one historical moment and a power generation amount and an electricity price at at least one historical moment, wherein the historical moment is before the target moment, and the data type of the regional data in the first regional data set and the second regional data set is the same; a similarity calculation module, configured to determine, for at least one second region data set, a data similarity between the second region data set and the first region data set; an interval determination module, configured to determine at least two power generation intervals and at least two electricity price intervals according to the first regional data set, the at least one second regional data set, and the data similarity corresponding to each of the second regional data sets, wherein the power generation interval is an interval in which the power generation of the target region at a target time is located, and the electricity price interval is an interval in which the electricity price of the target region at a target time is located; A support determination module, configured to determine at least one power generation support and at least one electricity price support according to the at least two power generation intervals and the at least two electricity price intervals, wherein the power generation support is determined by judging the overlap between the at least two power generation intervals, and the electricity price support is determined by judging the overlap between the at least two electricity price intervals; A data determination module, configured to determine a target power generation amount and a target electricity price of the target area at a target time according to the at least one power generation amount support degree and the at least one electricity price support degree; The support determination module is specifically used for: Determine the normal probability interval of power generation corresponding to each of the power generation intervals; Determine the power generation probability density function corresponding to each of the power generation normal probability intervals; Determine at least one power generation probability density union and at least one power generation probability density intersection, wherein the power generation probability density union is obtained by performing pairwise union of each of the power generation probability density functions, and the power generation probability density intersection is obtained by performing pairwise intersection of each of the power generation probability density functions; Determining at least one power generation support according to the at least one power generation probability density union and the at least one power generation probability density intersection, the power generation support being determined by a ratio of the power generation probability density union to the corresponding power generation probability density intersection; The support determination module is further specifically used for: Determine the normal probability interval of electricity price corresponding to each of the electricity price intervals; Determine the electricity price probability density function corresponding to each of the electricity price normal probability intervals; Determine at least one electricity price probability density union and at least one electricity price probability density intersection, the electricity price probability density union is obtained by performing pairwise union of each of the electricity price probability density functions, and the electricity price probability density intersection is obtained by performing pairwise intersection of each of the electricity price probability density functions; At least one electricity price support is determined based on the at least one electricity price probability density union and the at least one electricity price probability density intersection, and the electricity price support is determined by the ratio of the electricity price probability density union and the corresponding electricity price probability density intersection.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power generation and electricity price prediction method described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power generation and electricity price prediction method according to any one of claims 1 to 5 when executed.

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