Small hydropower station generation power prediction method, system, and computer readable medium

Abstract

To provide a comprehensive and highly accurate small hydropower station generation power prediction method, system, and a computer readable medium.SOLUTION: A small hydropower station generation power prediction method comprises, if multiple pieces of data such as a small hydropower station ID code, a total rainfall amount, a rainfall position and the number of days of delay are received, executing a step A. The step A comprises the following steps of: a1, obtaining a distance from the rainfall position to a small hydropower station according to the rainfall position and pre-stored position information of the small hydropower station; and a2, predicting daily power generation of the small hydropower station in a period of the days of delay based on the total rainfall amount, the number of days of delay, the obtained distance and pre-stored historical monthly generated power data of the small hydropower station.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular, to a method, system, and computer-readable medium for predicting the power generation of a small hydropower plant.

Background Art

[0002] A small hydropower plant is a small hydropower plant with a small installed capacity that generates electricity depending on water resources, and its power generation is affected by rainfall. If there is rainfall in the upstream basin of the small hydropower plant, the rainwater converges into the river where the small hydropower plant is located and becomes the power generation source of the small hydropower plant. Since it takes time for the rainwater to converge, there is a lag in the impact of rainfall on the power generation of the small hydropower plant, that is, the rainfall lags behind the power generation of the small hydropower plant. There are several aspects to the lag effect of rainfall on the power generation of small hydropower plants, but one of the main aspects is the total rainfall. The greater the total rainfall, the higher the power generation of the small hydropower plant. Existing power generation prediction models for small hydropower plants predict the power generation of the small hydropower plant based on the total rainfall and the past power generation of the small hydropower plant. However, there are multiple aspects of rainfall that affect the lag of the generated power of small hydropower plants. In this prior art, only one of the main aspects of the above effects is considered, lacking comprehensiveness and having a problem of low prediction accuracy.

Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, system, and computer-readable medium for predicting the power generation of a small hydropower plant, and the prediction accuracy of this method for predicting the power generation of the small hydropower plant is higher than that of the prior art. To solve the above technical problem, the present invention provides a method for predicting the power generation of a small hydropower plant. ​​​​​​​​Receive multiple data such as small hydropower plant ID code, total rainfall, rainfall location, and number of delay days If so, execute step A, and step A includes the following steps: step a1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant, and step a2. Based on the total rainfall, number of delay days, the obtained distance, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower for each day during the number of delay days period. Furthermore, in step a2, based on the pre-stored topographic information of the upstream basin of the small hydropower plant, activate the topographic prediction module corresponding to the upstream basin topography of the small hydropower plant, and the topographic prediction module is based on the topographic information of the upstream basin of the small hydropower plant, the total rainfall provided by the present invention, the number of delay days provided by the present invention, the obtained distance, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower plant for each day during the number of delay days period. Furthermore, when receiving multiple data such as small hydropower plant ID code, total rainfall, rainfall location, number of delay days, and dam lake stored rainfall amount, execute step B, and step B includes the following steps : step b1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant, and step b2. Obtain the power generation rainfall based on the total rainfall and the dam lake stored rainfall amount, and step b3. Based on the number of delay days, the obtained distance, the obtained power generation rainfall amount, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower for each day during the number of delay days period. Furthermore, when receiving multiple data such as small hydropower plant ID code, total rainfall, rainfall location, number of delay days, and dam lake stored rainfall amount, execute step B, and step B includes the following steps : step b1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant, and step b2. Obtain the power generation rainfall based on the total rainfall and the dam lake stored rainfall amount, and step b3. Based on the number of delay days, the obtained distance, the obtained power generation rainfall amount, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower for each day during the number of delay days period. . ​​​​Furthermore, when receiving a plurality of data such as small hydropower plant ID codes, total rainfall, rainfall location, number of delay days, dam lake stored rainfall, and rainfall scheduling amount, step C is executed. Step C includes the following steps: Step c1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant. Step c2. Based on the total rainfall, dam lake stored rainfall, and rainfall scheduling amount, obtain the power generation rainfall. Step c3. Based on the number of delay days, the obtained distance, the obtained power generation rainfall, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower generation for each day during the number of delay days. Furthermore, when only receiving the data of the small hydropower plant ID code and not receiving data such as total rainfall, rainfall location, number of delay days, dam lake stored rainfall, and rainfall scheduling amount, the following step D is executed: Based on the pre-stored topographic information of the upstream basin of the small hydropower plant, activate the topographic prediction module corresponding to the upstream basin topography of the small hydropower plant. The topographic prediction module is based on the topographic information of the upstream basin of the small hydropower plant provided by the present invention and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, and predict the power generation amount of the small hydropower plant for each day within the preset prediction number of days threshold period. The present invention further provides a computer-readable medium, in which a computer program is pre-stored. When the computer program is executed by a processor, the power generation amount prediction method of the small hydropower plant provided by the present invention can be implemented. Furthermore, a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module are provided. The present invention further provides a power generation prediction system for a small hydropower plant, which includes a terminal device. The terminal device is provided with a processor and a computer-readable pre-storage medium connected to the processor. The computer-readable pre-storage medium is a computer-readable medium provided by the present invention. . In the present invention, since the distance from the rainfall position to the small hydropower plant is considered to affect the time until the rainfall converges to the position of the small hydropower plant, the power generation prediction method for the small hydropower plant provided by the present invention predicts the power generation amount of the small hydropower plant based on the total rainfall amount and the distance from the rainfall position to the small hydropower plant. By considering not only the total rainfall amount but also the distance from the rainfall position to the small hydropower plant, it is more comprehensive than the prior art and has a higher prediction accuracy than the prior art.

Brief Description of the Drawings

[0004]

Figure 1

Embodiments for Carrying Out the Invention

[0005] Hereinafter, the present invention will be described in more detail with reference to specific embodiments. As aspects of the influence of rainfall on the delay of the generated power of a small hydropower plant, there are the total rainfall amount, the rainfall amount stored in the dam lake, the rainfall scheduling amount, the upstream basin topography of the small hydropower plant, and the distance from the rainfall position to the small hydropower plant. The total rainfall amount has a positive correlation with the power generation amount of the small hydropower plant, that is, the larger the total rainfall amount, the higher the power generation amount of the small hydropower plant. Since rainwater accumulates in the dam lake and there is a possibility that the rainwater is discharged into the reservoir where the small hydropower plant is located, by subtracting the amount of rainwater stored in the dam lake from the amount of rainwater discharged, the total power generation amount of the small hydropower plant can be calculated. ​​​​​​​It emits. The terrain of the upper reaches of a small hydropower plant affects the time required for rainfall to converge to the location of the small hydropower plant. When the terrain is relatively flat, the rainfall convergence time is slightly shorter. When the terrain has a steep gradient and a large elevation difference, the rainfall convergence time is slightly longer. The distance of rainfall to the location of the small hydropower plant also affects the rainfall convergence time, and the distance and the rainfall convergence time are proportional to the distance, that is, the shorter the distance, the shorter the convergence time. During the time, when the terrain is relatively flat, the rainfall convergence time is slightly shorter. When the terrain has a steep gradient and a large elevation difference, the rainfall convergence time is slightly longer. The distance of rainfall to the location of the small hydropower plant also affects the rainfall convergence time, and the distance and the rainfall convergence time are proportional to the distance, that is, the shorter the distance the shorter the convergence time. The power generation prediction system of a small hydropower plant includes a terminal device such as a computer. A processor and a computer-readable pre-storage medium are provided in the terminal device. The computer-readable pre-storage medium has a prediction model constructed based on the BP neural network algorithm. The prediction model includes an information module, an information processing module, and a prediction module of the small hydropower plant. The information module of the small hydropower plant pre-stores the power generation data, location information, and terrain information of the upper reaches of each small hydropower plant by month. The information processing module receives a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, rainfall scheduling amount, and dam lake storage rainfall amount, and is used to preprocess these data. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow of the small hydropower plant. The information module of the small hydropower plant pre-stores the monthly historical power generation data, location information, and terrain information of the upper reaches of each small hydropower plant. The information processing module receives a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, rainfall scheduling amount, and dam lake storage rainfall amount, and is used to preprocess these data. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow receives a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, rainfall scheduling amount, and dam lake storage rainfall amount, and is used to preprocess these data. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow receives a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, rainfall scheduling amount, and dam lake storage rainfall amount, and is used to preprocess these data. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow downstream of the small hydropower plant. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow downstream of the small hydropower plant. The lag days are the number of days from the first day to the nth day of the rainfall day until all the rainfall flows downstream of the hydropower plant, that is, the lag days are set as n. The prediction module includes a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow has a flat terrain prediction module, a steep terrain prediction module, and a flat-steep terrain prediction module. Here, the flat terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is flat. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow of the small hydropower plant has a flat terrain. The steep terrain prediction module is used to predict the power generation of a small hydropower plant where the terrain of the upper reaches is steep. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow of the small hydropower plant has a steep terrain. The flat-steep terrain prediction module is used to predict the power generation of a small hydropower plant where the upstream flow of the small hydropower plant has a combination of flat and steep terrains. It is used to predict the power generation of a small hydropower plant where the field terrain is flat and steep. These three terrain prediction modules each pre-store a prediction days threshold, and the first day of the prediction days threshold is counted from the prediction day. For example, if the prediction days threshold is set to 10, the terrain prediction module predicts the power generation of the small hydropower plant for each day from the first day to the tenth day of the prediction day. The method for predicting the power generation of a small hydropower plant includes an input information step and a prediction step, and the specific steps will be described in detail below. In the information input step, data is manually input into the terminal device. When there is rainfall in the upstream basin of the small hydropower plant and there is dam lake stored rainfall in the upstream basin of the small hydropower plant, the terminal device needs to manually input a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, and dam lake stored rainfall. Based on this dam lake stored rainfall, when rainfall scheduling for the reservoir where the small hydropower plant is located is performed within the lag days period, the terminal device needs to manually input a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, lag days, dam lake stored rainfall, and rainfall scheduling quantity. When there is rainfall in the upstream basin of the small hydropower plant but there is no dam lake stored rainfall and no water resource scheduling for the small hydropower plant, etc., the terminal device needs to manually input a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, and lag days. When there was no rainfall in the upstream basin of the small hydropower plant on the day before the prediction, only the data of the small hydropower plant ID code needs to be manually input into the terminal device as well. The prediction step is as shown in Figure 1. A computer program executable on the computer-readable pre-storage medium is pre-stored, and the processor runs the computer program as follows. When there is rainfall in the upstream basin of the small hydropower plant but there is no dam lake stored rainfall and no water resource scheduling for the small hydropower plant, etc., the terminal device needs to manually input a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall location, and lag days. When there was no rainfall in the upstream basin of the small hydropower plant on the day before the prediction, only the data of the small hydropower plant ID code needs to be manually input into the terminal device as well. When there is no rainfall in the upstream basin of the small hydropower plant on the day before the prediction, only the data of the small hydropower plant ID code needs to be manually input into the terminal device is sufficient. The prediction step is as shown in Figure 1. A computer program executable on the computer-readable pre-storage medium is pre-stored, and the processor runs the computer program stored therein. By executing this, the prediction step shown in FIG. 1 is realized. The power generation prediction system for the small hydropower plant executes this prediction step based on this, specifically as follows. When the information processing module receives a plurality of data such as the small hydropower plant ID code, total rainfall, rainfall position, and number of delay days, the power generation prediction system of the small hydropower plant executes step A, and step A includes the following steps: Step a1. The information processing module calculates the difference between the rainfall position and the small hydropower plant position based on the rainfall position and the small hydropower plant position information previously stored in the information module of the small hydropower plant, and obtains the distance from the rainfall position to the small hydropower plant. Step a2. The prediction module activates the terrain prediction module corresponding to the upstream basin terrain of the small hydropower plant based on the terrain information of the upstream basin of the small hydropower plant previously stored in the information module of the small hydropower plant. The terrain prediction module is based on the terrain information of the upstream basin of the small hydropower plant, total rainfall, number of delay days, obtained distance, and the power generation data of the monthly historical power generation of the small hydropower plant previously stored in the information module of the small hydropower plant, and predicts the power generation of the small hydropower plant on each day during the number of delay days. Step A is explained by way of example: The upstream basin terrain of the small hydropower plant N01 previously stored in the information module of the small hydropower plant is flat terrain, and the power generation data P of the monthly historical power generation of the small hydropower plant N01 =(100kW, 500kW, 1000kW, 600kW, 500kW 歴史1 =(100kW, 500kW, 1000kW, 600kW, 500kW , 400kW, 400kW…200kW, 700kW, 400kW), the position of the small hydropower plant N0 1 is S01, and the small hydropower plant N ​​​The upstream basin topography of No. 02 is steep terrain, and the monthly historical power generation data of the small hydropower plant N02 P 歴史2 =(400kW, 550kW, 600kW, 650kW, 850kW, 1000 kW, 1200kW…800kW, 1000kW, 1500kW), the location of the small hydropower plant N02 is S02, and the monthly historical power generation data P of the small hydropower plant N0 3 stored in advance in the information module of the small hydropower plant. The upstream basin topography of the small hydropower plant N03 is flat and steep terrain, and the monthly historical power generation data of the small hydropower plant N03 -ta P 歴史3 =(1000kW, 900kW, 800kW, 600kW, 600kW, 5 50kW, 400kW…650kW, 400kW, 400kW), and the location of the small hydropower plant N 03 is S03. Based on this assumption, the following example is shown.

[0006] (1) Assume that the small hydropower plant ID code received by the information processing module is N01, the total rainfall is 10000 m3, the rainfall location is S1, and the delay days are 10. In this case , based on the rainfall location S1 and the information of the location S01 of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant, the information processing module calculates the difference between the rainfall location S1 and the small hydropower plant location S01. Assuming that the difference is 100 and the unit is km, the distance from the rainfall location to the small hydropower plant is 100 km. The prediction module starts the flat terrain prediction module based on the information that the upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain. The flat terrain prediction module uses the flat terrain information, total rainfall of 10000 m3, delay days of 10, the obtained distance of 100 km, and the monthly historical power generation data P of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant to predict. Based on the information that the upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain, the flat terrain prediction module is activated. The flat terrain prediction module uses the flat terrain information, total rainfall of 10000 m3, delay days of 10, the obtained distance of 100 km, and the monthly historical power generation data P of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant to predict. 歴史1 =(100kW, 500kW, 1000kW, 600kW, 500kW, 400k W, 400kW…200kW, 700kW, 400kW) based on this, small hydropower plant N0 1's daily power generation P for each day from the 1st day to the 10th day of the prediction date 予測A1 is 300 kW, 1000kW, 1600kW, 1900kW, 1700kW, 1500kW, 10 00kW, 800kW, 600kW, 500kW and it is predicted that there is a possibility that it is so. (2) When the small hydropower plant ID code received by the information processing module is N02, the total rainfall is 10000 m3, the rainfall location is S2, and the number of delay days is 5. The information processing mod ule calculates the difference between the rainfall location S2 and the small hydropower plant location S02 based on the information of the rainfall location S2 and the small hydropower plant stored in advance in the information module of the small hydropower plant N02 location S02 information. Assuming that the difference is 100 and the unit is km, the distance from the rainfall location to the small hydropower plant is 100 km. The prediction module starts the steep terrain prediction module based on the information that the upstream basin terrain of the small hydropower plant N02 stored in advance in the information module of the small hydropower plant is steep terrain. The steep terrain prediction module uses the steep terrain information, total rainfall 1 0000 m3, number of delay days 5, the obtained distance 100 km and the power generation data P of the monthly historical power generation of the small hydropower plant N02 stored in advance in the information module of the small hydropower plant Based on P=(400 kW, 550kW, 600kW, 650kW, 850kW, 1000kW, 1200kW 歴史2 =(400 kW, 550kW, 600kW, 650kW, 850kW, 1000kW, 1200kW …800kW, 1000kW, 1500kW), for the small hydropower plant N02, the daily power generation P for each day from the 1st day to the 5th day 予測A2 is 1600kW, 2100kW, 20 Predict that it may be 00 kW, 1900 kW, or 1900 kW. (3) When the small hydropower plant ID code received by the information processing module is N03, the total rainfall is 10000 m3, the rainfall location is S3, and the number of delay days is 3, the information processing mo dule calculates the difference between the rainfall location S3 and the small hydropower plant location S03 based on the information of the small hydropower plant N02 location S03 stored in advance in the information module of the small hydropower plant. If it is assumed that the difference is 50 and the unit is km, the distance from the rainfall location to the small hydropower plant is 50 km. The prediction module starts the flat-steep terrain prediction module based on the information that the upstream basin terrain of the small hydropower plant N03 stored in advance in the information module of the small hydropower plant is flat-steep terrain. The flat-steep terrain prediction module uses the flat-steep terrain information, total rainfall of 10000 m3, number of delay days of 3, the obtained distance of 100 km, and the monthly historical power generation data P =(1000 kW, 900 kW, 800 kW, 600 kW, 600 kW, 550 kW 歴 史3 =(1000 kW, 900 kW, 800 kW, 600 kW, 600 kW, 550 kW 予測A3 Based on this, when the information processing module further receives the dam lake storage rainfall data, and in particular when the information processing module receives multiple data such as the small hydropower plant ID code, total rainfall, rainfall location, number of delay days, and dam lake storage rainfall, the power generation prediction system of the small hydropower plant executes step B, and step B includes the following steps.​​ Step b1. Based on the rainfall position and the position information of the small hydropower plant pre-stored in the information processing module, the information processing module calculates the difference between the rainfall position and the small hydropower plant position based on the pre-stored position information of the small hydropower plant, to obtain the distance from the rainfall position to the small hydropower plant. Step b2. Based on the total rainfall and the dam lake storage rainfall, the information processing module calculates the difference between the total rainfall and the dam lake storage rainfall to obtain the actual power generation rainfall used by the small hydropower plant. Step b3. The prediction module activates the terrain prediction module corresponding to the upstream basin terrain of the small hydropower plant based on the terrain information of the upstream basin of the small hydropower plant pre-stored in the information module of the small hydropower plant. The terrain prediction module predicts the power generation amount of the small hydropower generation for each day during the lag days based on the terrain information of the upstream basin of the small hydropower plant, the lag days, the obtained distance, the obtained power generation rainfall, and the power generation data of the monthly historical power generation of the small hydropower plant pre-stored in the information module of the small hydropower plant. An example is given to illustrate Step B: The upstream basin terrain of the small hydropower plant N01 pre-stored in the information module of the small hydropower plant is flat terrain, and the power generation data P of the monthly historical power generation of the small hydropower plant N01 = (100kW, 500kW, 1000kW, 600kW, 500kW 歴史1 =(100kW, 500kW, 1000kW, 600kW, 500kW , 400kW, 400kW…200kW, 700kW, 400kW), the position of the small hydropower plant N0 1 is S01, the upstream basin terrain of the small hydropower plant N 02 pre-stored in the information module of the small hydropower plant is steep terrain, and the power generation data of the monthly historical power generation of the small hydropower plant N02 P 歴史2 =(400kW, 550kW, 600kW, 650kW, 850kW, 1000 ​​​​​​​kW, 1200kW…800kW, 1000kW, 1500kW), Small Hydropower Plant N02 The location is S02, and Small Hydropower Plant N0 The upstream basin terrain of No. 3 is flat and steep terrain, and the monthly historical power generation data of Small Hydropower Plant N03 Table P 歴史3 =(1000kW, 900kW, 800kW, 600kW, 600kW, 55 0kW, 400kW…650kW, 400kW, 400kW), the location of Small Hydropower Plant N03 Assuming that the location is S03, based on this assumption, the following example is shown.

[0007] (1) The small hydropower plant ID code received by the information processing module is N01, the total rainfall is 10000 m3, the rainfall location is S1, the number of delay days is 10, and the dam lake storage rainfall is 1000 m3. In this case, the information processing module, based on the rainfall location S1 and the location S01 information of Small Hydropower Plant N01 pre-stored in the information module of the small hydropower plant, calculates the difference between the rainfall location S1 and the location S01 of the small hydropower plant. Assuming that the difference is 100 and the unit is km the distance from the rainfall location to the small hydropower plant is 100 km. Based on the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3, the difference between the total rainfall of 10000 m 3 and the dam lake storage rainfall of 1000 m3 is calculated, and the actual power generation rainfall used for the small hydropower plant is 9000 m3. The prediction module, based on the information that the upstream basin terrain of Small Hydropower Plant N01 pre-stored in the information module of the small hydropower plant is flat terrain, activates the flat terrain prediction module. The flat terrain prediction module uses the flat terrain information, the number of delay days of 10, the obtained distance of 100 km, the obtained power generation rainfall of 9000 m3, and the information module of the small hydropower plant to calculate the difference between the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3, and the actual power generation rainfall used for the small hydropower plant is 9000 m3. The prediction module, based on the information that the upstream basin terrain of Small Hydropower Plant N01 pre-stored in the information module of the small hydropower plant is flat terrain, activates the flat terrain prediction module. The flat terrain prediction module uses the flat terrain information, the number of delay days of 10, the obtained distance of 100 km, the obtained power generation rainfall of 9000 m3, and the information module of the small hydropower plant to calculate the difference between the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3, and the actual power generation rainfall used for the small hydropower plant is 9000 m3. The prediction module, based on the information that the upstream basin terrain of Small Hydropower Plant N01 pre-stored in the information module of the small hydropower plant is flat terrain, activates the flat terrain prediction module. The flat terrain prediction module uses the flat terrain information, the number of delay days of 10, the obtained distance of 100 km, the obtained power generation rainfall of 9000 m3, and the information module of the small hydropower plant to calculate the difference between the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3, and the actual power generation rainfall used for the small hydropower plant is 9000 m3. The prediction module, based on the information that the upstream basin terrain of Small Hydropower Plant N01 pre-stored in the information module of the small hydropower plant is flat terrain, The power generation data P of the monthly historical power generation of the small hydropower plant N01 pre-stored in the yule 歴史1 =(1 00kW, 500kW, 1000kW, 600kW, 500kW, 400kW, 400k W…200kW, 700kW, 400kW), based on this, it is predicted that the power generation P of the small hydropower plant N01 on each day from the 1st day to the 10th day 予測B1 may be 200kW, 900kW, 1500 kW, 1800kW, 1600kW, 1400kW, 900kW, 700kW, 500k W, 400kW. (2) The small hydropower plant ID code received by the information processing module is N02, the total rainfall is 10000 m3, the rainfall location is S2, the number of delay days is 5 days, and the dam lake storage rainfall is 1000 m3. In this case, based on the rainfall location S2 and the small hydropower plant location S02 information pre-stored in the information module of the small hydropower plant, the information processing module calculates the difference between the rainfall location S2 and the small hydropower plant location S02. Assuming that the difference is 100 and the unit is km , the distance from the rainfall location to the small hydropower plant is 100 km. Based on the total rainfall of 10000 m 3 and the dam lake storage rainfall of 1000 m3, the difference between the total rainfall of 10000 m 3 and the dam lake storage rainfall of 1000 m3 is calculated, and the actual power generation rainfall used in the small hydropower plant is 9000 m3. The prediction module starts the steep terrain prediction module based on the information that the upstream basin terrain of the small hydropower plant N02 pre-stored in the information module of the small hydropower plant is steep terrain. The steep terrain prediction module uses the steep terrain information, the number of delay days of 5, the obtained distance of 100 km, the obtained power generation rainfall of 9000 m3, and the power generation data P of the monthly historical power generation of the small hydropower plant N02 pre-stored in the information module of the small hydropower plant ​歴史2 =(400 kW, 550kW, 600kW, 650kW, 850kW, 1000kW, 1200kW …800kW, 1000kW, 1500kW), based on this, it is predicted that the power generation amount P of the small hydropower plant N02 on each day from the 1st day to the 5th day 予測B2 may be 1500kW, 2000kW, 19 00kW, 1800kW, 1800kW. (3) When the small hydropower plant ID code received by the information processing module is N03, the total rainfall is 10000 m3, the rainfall location is S3, the number of delay days is 3, and the dam lake storage rainfall amount is 1000 m3, the information processing module calculates the difference between the rainfall location S3 and the small hydropower plant location S03 information pre-stored in the information module of the small hydropower plant based on the small hydropower plant N03 location S03 information. Based on this, when it is assumed that the difference between the rainfall location S3 and the small hydropower plant location S03 is 50 and the unit is km, the distance from the rainfall location to the small hydropower plant is 50 km. Based on the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3, the difference between the total rainfall of 1000 0 m3 and the dam lake storage rainfall of 1000 m3 is calculated, and the actual power generation rainfall used for the small hydropower plant is 9000 m3. The prediction module is based on the information pre-stored in the information module of the small hydropower plant that the upstream basin topography of the small hydropower plant N03 is flat and steep terrain information, activates the flat and steep terrain prediction module, and the flat and steep terrain prediction module is based on the flat and steep terrain information , the number of delay days of 3, the obtained distance of 100 km, the obtained power generation rainfall of 9000 m3, and the monthly historical power generation amount data P of the small hydropower plant N03 pre-stored in the information module of the small hydropower plant =(1000kW, 900kW, 800kW, 600kW, 600kW, 5 … … 歴史3 =(1000kW, 900kW, 800kW, 600kW, 600kW, 5 Based on 50kW, 400kW…650kW, 400kW, 400kW), small hydropower generation The daily power generation amount P of small hydropower station N03 from the 1st day to the 3rd day 予測B3 is predicted to be possibly 2800kW, 2500 kW, 2300kW. Based on this, the information processing module further receives rainfall scheduling amount data, that is, when the information processing module receives a plurality of data such as the small hydropower station ID code, total rainfall amount, rainfall location, number of delay days , dam lake stored rainfall, and rainfall scheduling amount, the power generation amount prediction system of the small hydropower station executes step C, and step C includes the following steps . Step c1. The information processing module calculates the difference between the rainfall location and the small hydropower station location based on the rainfall location and the location information of the small hydropower station pre-stored in the information module of the small hydropower station to obtain the distance from the rainfall location to the small hydropower station. Step c2. The information processing module calculates the difference between the total rainfall amount, the dam lake stored rainfall amount, and the rainfall scheduling amount based on the total rainfall amount and the dam lake stored rainfall amount, and obtains the actual power generation rainfall amount used for the small hydropower station . Step c3. The prediction module activates the terrain prediction module corresponding to the upstream basin terrain of the small hydropower station based on the terrain information of the upstream basin of the small hydropower station pre-stored in the information module of the small hydropower station . The terrain prediction module predicts the power generation amount of the small hydropower generation for each day during the number of delay days based on the terrain information of the upstream basin of the small hydropower station , the number of delay days, the distance obtained above, the power generation rainfall amount obtained above, and the power generation amount data of the monthly historical power generation of the small hydropower station pre-stored in the information module of the small hydropower station . Step c3. The prediction module activates the terrain prediction module corresponding to the upstream basin terrain of the small hydropower station based on the terrain information of the upstream basin of the small hydropower station pre-stored in the information module of the small hydropower station . The terrain prediction module predicts the power generation amount of the small hydropower generation for each day during the number of delay days based on the terrain information of the upstream basin of the small hydropower station , the number of delay days, the distance obtained above, the power generation rainfall amount obtained above, and the power generation amount data of the monthly historical power generation of the small hydropower station pre-stored in the information module of the small hydropower station . Describe Step C with examples: The upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain, and the monthly historical power generation amount data P of the small hydropower plant N01 =(100kW, 500kW, 1000kW, 600kW, 500kW 歴史1 =(100kW, 500kW, 1000kW, 600kW, 500kW , 400kW, 400kW…200kW, 700kW, 400kW), and the location of the small hydropower plant N01 is S01. The upstream basin topography of the small hydropower plant N02 stored in advance in the information module of the small hydropower plant is steep terrain, and the monthly historical power generation amount data of the small hydropower plant N02 P =(400kW, 550kW, 600kW, 650kW, 850kW, 10 歴史2 =(400kW, 550kW, 600kW, 650kW, 850kW, 10 00kW, 1200kW…800kW, 1000kW, 1500kW), and the location of the small hydropower plant N02 is S02. Assuming that the upstream basin topography of the small hydropower plant N03 stored in advance in the information module of the small hydropower plant is flat and steep terrain, and the monthly historical power generation amount data P of the small hydropower plant N03 =(1000kW, 900kW, 800kW, 600kW, 600 歴史3 =(1000kW, 900kW, 800kW, 600kW, 600 kW, 550kW, 400kW…650kW, 400kW, 400kW), and the location of the small hydropower plant N03 is S03. Based on this assumption, the following example is shown.

[0008] (1) When the small hydropower plant ID code received by the information processing module is N01, the total rainfall is 10000 m3, the rainfall location is S1, the number of delay days is 10, the rainfall storage volume in the dam lake is 1000 m3, and the rainfall scheduling amount is 4500 m3, the information processing module shall calculate the difference between the rainfall location S1 and the location S01 of the small hydropower plant N01 based on the rainfall location S1 and the location S01 information of the small hydropower plant stored in advance in the information module of the small hydropower plant and the location S01 information of the small hydropower plant N01 Assume that the difference is 100 and the unit is km. When the distance from the rainfall location to the small hydropower plant is 100 km, the total rainfall is 10,000 m3, the dam lake storage rainfall is 1,000 m3 Based on the rainfall scheduling volume of 4,500 m3, the difference between the total rainfall of 10,000 m3, the dam lake storage rainfall of 1,000 m3, and the rainfall scheduling volume of 4,500 m3 is calculated, and the actual power generation rainfall used in the small hydropower plant is 4,500 m3. The prediction module is based on the information of the upstream basin terrain of the small hydropower plant N01 pre-stored in the information module of the small hydropower plant. Based on the information that the terrain is flat terrain, the flat terrain prediction module is activated. The flat terrain prediction module uses the flat terrain information, a lag of 10 days, the obtained distance of 100 km, the obtained power generation rainfall of 4,500 m 3, and the monthly historical power generation data P 歴史1 =(100 kW, 500 kW, 1000 kW, 600 kW, 5 00 kW, 400 kW, 400 kW…200 kW, 700 kW, 400 kW) to predict that the power generation P of the small hydropower plant N01 on each day from the 1st day to the 10th day 予測C1 may be 150 kW, 700 kW, 1250 kW, 1500 kW, 1400 kW, 1300 kW, 7 00 kW, 550 kW, 350 kW, 300 kW. (2) When the small hydropower plant ID code received by the information processing module is N02, the total rainfall is 10,000 m3, the rainfall location is S2, the lag is 5 days, the dam lake storage rainfall is 1,000 m3, and the rainfall scheduling volume is 4,500 m3, the information processing module uses the rainfall location S2 and the information of the small hydropower plant pre-stored in the information module Based on the position S02 information of NO2, calculate the difference between the rainfall position S2 and the small hydropower plant position S02 Assume that the difference is 100 and the unit is km. In this case, the distance from the rainfall position to the small hydropower plant is 100 km, the total rainfall is 10000 m3, and the dam lake storage rainfall is 1000 m3 Based on the total rainfall of 10000 m3, the dam lake storage rainfall of 1000 m3, and the rainfall scheduling volume of 4500 m3, calculate the difference between the total rainfall of 10000 m3 and the dam lake storage rainfall of 1000 m3 and the rainfall scheduling volume of 4500 m3, and obtain that the actual power generation rainfall used by the small hydropower plant is 4500 m3. The prediction module is based on the information of the small hydropower plant stored in advance in the information module of the small hydropower plant, where the terrain of the upper reaches of the small hydropower plant N02 is steep terrain information, activate the steep terrain prediction module. The steep terrain prediction module is based on the steep terrain information, the lag days of 5, the obtained distance of 100 km, the obtained power generation rainfall of 4500 m 3, and the monthly historical power generation data P of the small hydropower plant N02 stored in advance in the information module of the small hydropower plant =(400kW, 550kW, 600kW, 650kW, 85 歴史2 0kW, 1000kW, 1200kW…800kW, 1000kW, 1500kW) based on this, predict that the power generation P of the small hydropower plant N02 on each day from the 1st day to the 5th day may be 予測C2 950kW, 1150kW, 1250kW, 1100kW, 1050kW is predicted. (3) When the small hydropower plant ID code received by the information processing module is N03, the total rainfall is 10000 m3, the rainfall position is S3, the lag days are 3, the dam lake storage rainfall is 1000 m3, and the rainfall scheduling volume is 4500 m3, the information processing module is based on the rainfall position S3 and the small hydropower plant stored in advance in the information module of the small hydropower plant ​Based on the N03 position and S03 information, the difference between the rainfall position S3 and the small hydropower plant position S03 is calculated. Assuming that the difference is 100 and the unit is km, the distance from the rainfall position to the small hydropower plant is 100 km. Based on the total rainfall of 10,000 m3, the dam lake storage rainfall of 1 000 m3, and the rainfall scheduling amount of 4,500 m3, the difference between the total rainfall of 10,000 m 3, the dam lake storage rainfall of 1,000 m3, and the rainfall scheduling amount of 4,500 m3 is calculated to obtain an actual power generation rainfall of 4,500 m3 for use in the small hydropower plant. The prediction module activates the flat-steep terrain prediction module based on the information stored in advance in the information module of the small hydropower plant that the upstream basin terrain of the small hydropower plant N03 is flat-steep terrain. The flat-steep terrain prediction module uses the flat-steep terrain information, the lag days of 3, the obtained distance of 100 km, the obtained power generation rainfall of 4,500 m3, and the power generation data P of the monthly historical power generation of the small hydropower plant N03 stored in advance in the information module of the small hydropower plant P 歴史3 =(1000 kW, 900 kW, 8 00 kW, 600 kW, 600 kW, 550 kW, 400 kW…650 kW, 400 kW to predict that the power generation of the small hydropower plant N03 on each day from the 1st day to the 3rd day P 予測C3 may be 1550 kW, 1800 kW, 1600 kW. If the information processing module only receives the data of the small hydropower plant ID code and does not receive data such as the total rainfall, rainfall position and lag days, dam lake storage rainfall, and rainfall scheduling amount, the power generation prediction system of the small hydropower plant executes the following steps. Step D. The prediction module retrieves the small hydropower plant stored in advance in the information module of the small hydropower plant. ​Based on the topographic information of the upstream basin of the small hydropower plant, a topographic prediction module corresponding to the upstream basin topography of the small hydropower plant is activated. The topographic prediction module predicts the power generation amount of the small hydropower plant on each day within a preset prediction days threshold period based on the topographic information of the upstream basin of the small hydropower plant and the monthly historical power generation amount data of the small hydropower plant stored in advance in the information module of the small hydropower plant. The topographic prediction module corresponding to the upstream basin topography of the small hydropower plant is activated based on the topographic information of the upstream basin of the small hydropower plant. The topographic prediction module predicts the power generation amount of the small hydropower plant on each day within a preset prediction days threshold period based on the topographic information of the upstream basin of the small hydropower plant and the monthly historical power generation amount data of the small hydropower plant stored in advance in the information module of the small hydropower plant. Based on the topographic information of the upstream basin of the small hydropower plant and the monthly historical power generation amount data of the small hydropower plant stored in advance in the information module of the small hydropower plant, the topographic prediction module predicts the power generation amount of the small hydropower plant on each day within a preset prediction days threshold period. Based on the topographic information of the upstream basin of the small hydropower plant and the monthly historical power generation amount data of the small hydropower plant stored in advance in the information module of the small hydropower plant, the topographic prediction module predicts the power generation amount of the small hydropower plant on each day within a preset prediction days threshold period. The topographic prediction module predicts the power generation amount of the small hydropower plant on each day within a preset prediction days threshold period based on the topographic information of the upstream basin of the small hydropower plant and the monthly historical power generation amount data of the small hydropower plant stored in advance in the information module of the small hydropower plant. Step D is explained by way of example: The prediction days thresholds stored in the three topographic prediction modules are all 7 days. The upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain, and the power generation amount data P of the monthly historical power generation of the small hydropower plant N01 =(100kW, 500kW, 1000kW, 600kW, 500kW, 400kW W, 400kW…200kW). The upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain, and the power generation amount data P of the monthly historical power generation of the small hydropower plant N02 歴史1 =(100kW, 500kW, 1000kW, 600kW, 500kW, 400kW W, 400kW…200kW). The upstream basin topography of the small hydropower plant N01 stored in advance in the information module of the small hydropower plant is flat terrain, and the power generation amount data P of the monthly historical power generation of the small hydropower plant N02 =(400kW, 550kW, 600kW, 650kW, 850kW W, 1000kW, 1200kW…800kW, 1000kW, 1500kW). The upstream basin topography of the small hydropower plant N03 stored in advance in the information module of the small hydropower plant is flat and steep terrain, and the power generation amount data P of the monthly historical power generation of the small hydropower plant N03 歴史2 =(400kW, 550kW, 600kW, 650kW, 850kW W, 1000kW, 1200kW…800kW, 1000kW, 1500kW). Assuming that the upstream basin topography of the small hydropower plant N03 stored in advance in the information module of the small hydropower plant is flat and steep terrain, and the power generation amount data P of the monthly historical power generation of the small hydropower plant N03 =(100 0kW, 900kW, 800kW, 600kW, 600kW, 550kW, 400kW… 歴史3 =(100 0kW, 900kW, 800kW, 600kW, 600kW, 550kW, 400kW… 650kW, 400kW, 400kW). If this assumption is made, the following example is shown based on this assumption. The following example is shown based on this assumption.

[0009] (1) The small hydropower plant ID code received by the information processing module is N01, and the small hydropower The upstream basin terrain of the small hydropower plant N01 pre-stored in the power plant information module is flat terrain and the power generation data P of the monthly historical power generation of the small hydropower plant N01 歴史1 =(100kW, 500 kW, 1000kW, 600kW, 500kW, 400kW, 400kW…200kW, 700kW, 400kW). In this case, the prediction module is based on the information that the upstream basin terrain of the small hydropower plant N01 pre-stored in the small hydropower plant information module is flat terrain and activates the flat terrain prediction module. The flat terrain prediction module is based on the flat terrain information and the power generation data P of the monthly historical power generation of the small hydropower plant N01 pre-stored in the small hydropower plant information module to predict that the power generation P 歴史1 of the small hydropower plant N01 on each day from the 1st day to the 7th day may be 120kW, 600kW, 1000kW, 700kW, 500kW P 予測D1 , 400kW, 350kW. (2) The small hydropower plant ID code received by the information processing module is N02, and the upstream basin terrain of the small hydropower plant N02 pre-stored in the small hydropower plant information module is steep terrain and the power generation data P of the monthly historical power generation of the small hydropower plant N02 =(400kW, 550 kW, 600kW, 650kW, 850kW, 1000kW, 1200kW…800kW 歴史2 , 1000kW, 1500kW). In this case, the prediction module is based on the information that the upstream basin terrain of the small hydropower plant N02 pre-stored in the small hydropower plant information module is steep terrain and activates the steep terrain prediction module. The steep terrain prediction module is based on the steep terrain information and the power generation data P of the monthly historical power generation of the small hydropower plant N01 pre-stored in the small hydropower plant information module to predict that the power generation of the small hydropower plant N02 on each day from the 1st day to the 7th day may be 120kW, 600kW, 1000kW, 700kW, 500kW and the power generation data P of the monthly historical power generation of the small hydropower plant N01 pre-stored in the small hydropower plant information module P 歴史2Power generation amounts of the small hydropower plant N02 for each day from the 1st day to the 7th day P 予測D2 is predicted to possibly be 350 kW, 500 kW, 600 kW, 700 kW, 900 kW, 100 0 kW, 1200 kW (3) The small hydropower plant ID code received by the information processing module is N03, and the upstream basin topography of the small hydropower plant N03 pre-stored in the information module of the small hydropower plant is flat and steep terrain and the power generation amount data P of the monthly historical power generation of the small hydropower plant N03 =(1000 kW, 歴史3 900 kW, 800 kW, 600 kW, 600 kW, 550 kW, 400 kW…650 k W, 400 kW, 400 kW), when this is the case, the prediction module, based on the information that the upstream basin topography of the small hydropower plant N03 pre-stored in the information module of the small hydropower plant is flat and steep terrain activates the flat and steep terrain prediction module, and the flat and steep terrain prediction module, based on the flat and steep terrain information and the power generation amount data P of the monthly historical power generation of the small hydropower plant N03 pre-stored in the information module of the small hydropower plant predicts that the power generation amount P of the small hydropower plant N03 for each day from the 1st day to the 7th day may be 1000 kW, 850 kW, 800 kW, 6 00 kW, 550 kW, 500 kW, 400 kW This invention is developed from the fact that rainfall has various effects on the power generation hysteresis of small hydropower plants 歴史3 and considering that the total rainfall amount, the rainfall amount stored in the dam lake, and the rainfall scheduling amount affect the power generation amount of the small hydropower plant, and further considering that the topography of the upstream basin of the small hydropower plant and the distance of the rainfall position to the small hydropower plant affect the rainfall convergence time 予測D3 ​​​​​​Therefore, the power generation amount prediction method for the small hydropower plant provided by the present invention is based on the total rainfall, the reservoir lake stored rainfall, the rainfall scheduling amount, the upstream basin topography of the small hydropower plant, and the distance from the rainfall position to the small hydropower plant, predicts the power generation amount of the small hydropower plant, and is more comprehensive than the prior art and has higher prediction accuracy than the prior art. The above is only one embodiment of the present invention and does not limit the scope of patent protection. Those skilled in the art shall make all substantial changes or substitutions made based on the present invention be included in the scope of patent protection. It is assumed.

Claims

1. When receiving a plurality of data such as a small hydropower plant ID code, total rainfall, rainfall location, and number of delay days, execute Step A, and Step A includes the following steps: Step a1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant. Step a2. Based on the total rainfall, number of delay days, the obtained distance, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower plant for each day during the number of delay days. A method for predicting the power generation amount of a small hydropower plant, characterized by the above.

2. In Step a2, based on the pre-stored topographic information of the upstream basin of the small hydropower plant, activate a topographic prediction module corresponding to the upstream basin topography of the small hydropower plant, and based on the topographic information of the upstream basin of the small hydropower plant, the total rainfall, the number of delay days, the obtained distance, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower plant for each day during the number of delay days. The method for predicting the power generation amount of a small hydropower plant according to Claim 1, characterized by the above.

3. When receiving a plurality of data such as a small hydropower plant ID code, total rainfall, rainfall location, number of delay days, and dam lake storage rainfall, execute Step B, and Step B includes the following steps: Step b1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant. Step b2. Based on the total rainfall and the dam lake storage rainfall, obtain the power generation rainfall. Step b3. Based on the number of delay days, the obtained distance, the obtained power generation rainfall, and the pre-stored power generation amount data of the monthly historical power generation of the small hydropower plant, predict the power generation amount of the small hydropower plant for each day during the number of delay days. The method for predicting the power generation amount of a small hydropower plant according to Claim 1, characterized by the above.

4. When receiving a plurality of data such as a small hydropower plant ID code, total rainfall, rainfall location, number of delay days, dam lake storage rainfall, and rainfall scheduling amount, execute Step C, and Step C includes the following steps: Step c1. Based on the rainfall location and the pre-stored location information of the small hydropower plant, obtain the distance from the rainfall location to the small hydropower plant. Step c2 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ . Based on the total rainfall, dam lake storage rainfall, and rainfall scheduling amount, obtain the power generation rainfall , step c3. Based on the number of delay days, the obtained distance, the obtained power generation rainfall, and the power generation data of the monthly historical power generation of the small hydropower plant memorized in advance , predict the power generation amount of the small hydropower generation for each day during the period of the number of delay days, the method for predicting the power generation amount of the small hydropower plant according to claim 3, characterized in that .

5. When only receiving the data of the small hydropower plant ID code and not receiving data such as the total rainfall, rainfall position, number of delay days, dam lake storage rainfall, and rainfall scheduling amount, execute the following step D: Based on the terrain information of the upstream basin of the small hydropower plant memorized in advance, start the terrain prediction module corresponding to the upstream basin terrain of the small hydropower plant, and based on the terrain information of the upstream basin of the small hydropower plant and the power generation data of the monthly historical power generation of the small hydropower plant memorized in advance , predict the power generation amount of the small hydropower plant for each day within the preset prediction days threshold period, the method for predicting the power generation amount of the small hydropower plant according to claim 4, characterized in that .

6. A computer-readable medium in which a computer program is pre-stored, and when the computer program is executed by a processor, it can implement the method for predicting the power generation amount of the small hydropower plant according to any one of claims 1 to 5, a computer-readable medium characterized in that

7. The computer-readable medium according to claim 6, characterized in that it comprises a flat terrain prediction module, a steep terrain prediction module, and a flat and steep terrain prediction module.

8. A power generation amount prediction system for a small hydropower plant, comprising a terminal device, and a computer-readable pre-storage medium connected to the processor and the processor provided in the terminal device, the computer-readable pre-storage medium is the computer-readable medium according to claim 6 or 7, a power generation amount prediction system for a small hydropower plant characterized in that . ​ ​ 。 ​ ​ ​ ​ ​ ​ ​ ​

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