A general verification method, system, and storage medium for wind speed forecasting in the context of power grid-related wind disasters.

Abstract

This invention discloses a general verification method, system, and storage medium for wind speed forecasts in power grid-related wind disasters. The method involves: calculating a general verification score for single-point wind speed forecasts within the wind speed prediction focus area using wind speed observation data and wind speed forecast data; calculating a set of general verification score values ​​for wind speed forecasts within a neighborhood range; determining the maximum value of the general verification score within the set; determining whether the disaster-causing wind speed is predicted correctly or falsely based on the general verification score within the neighborhood range over a period of time; and obtaining the spatial distribution characteristics of the location error based on the location coordinates of the maximum value of the general verification score for wind speed forecasts over a period of time. This invention constructs a new system for verifying the effectiveness of power grid-related wind speed disaster forecasts. It is computationally simple, with objective, transparent, and clearly defined rules, and features specificity, localization, universality, and continuous results. It is used to examine the accuracy of wind speed forecasts from numerical models and has significant practical value for disaster prevention decision-making.

Description

Technical Field

[0001] This invention relates to the field of power disaster prevention forecasting and evaluation technology, specifically to a general verification method, system and storage medium for wind speed forecasting of power-related wind disasters. Background Technology

[0002] Wind speed forecasting is of paramount importance in power sector disaster response. Power facilities, such as transmission lines and substations, are frequently affected by strong winds, especially during severe weather conditions like typhoons and tornadoes, where changes in wind speed can have serious consequences. When wind speed forecasts exceed set safety thresholds, timely measures such as power outages and facility reinforcement can be implemented to prevent damage or malfunctions. Furthermore, wind speed forecasts help power companies predict the likelihood and extent of wind disasters. Analysis can predict the timing, duration, and intensity of wind disasters, allowing for proactive countermeasures and reducing the possibility of power facility damage and casualties. Therefore, wind speed forecasting plays a crucial role in power sector disaster response, helping to ensure the stability and security of power supply.

[0003] Therefore, the power sector needs meteorological wind forecasts as data support. The power sector obtains this data through various means, including in-house research and external collaborations, and there are many sources. Therefore, the power sector must understand the quality of these data sources, prioritize them in its work, and ensure that disaster prevention decisions are based on scientific evidence. Only through wind speed verification can the current wind speed forecasting technology be mastered, thereby helping the power sector and related departments make reasonable decisions. Therefore, verifying these wind speed forecast data is indispensable. However, currently, there is a lack of specific verification standards tailored to wind disaster needs; generally, simple error calculations or some meteorological methods for verifying wind speed are used. These methods are not only lacking in specificity but also have many problems (distortion, discontinuity, double penalties, etc.).

[0004] Currently, there is considerable research on the evaluation and verification of wind speed forecasts in the meteorological field. Researchers have systematically defined and classified wind speed verification indicators to better assess the quality and accuracy of wind speed data. Common wind speed verification indicators include binary event testing methods (accuracy, hit rate, TS score, ETS score, etc.), mean wind speed error, standard deviation error, frequency distribution error, and correlation coefficient. In recent years, some new evaluation methods have begun to be applied, such as probabilistic forecast evaluation methods and spatial correlation analysis. These methods can more comprehensively evaluate the accuracy of wind speed forecasts. However, there is relatively little research specifically on power grid wind disasters and methods for evaluating the effectiveness of strong wind-induced disaster forecasts. Considering the important role of wind speed forecasts in disaster prevention and mitigation in the power industry, it is necessary to design corresponding verification indicators specifically for this purpose.

[0005] Currently, the most common wind speed testing indicators used in the power industry are still two types of testing. One type usually sets a threshold and then uses a binary event testing method (accuracy, hit rate, TS score, ETS score, etc.). This method has significant limitations. Wind speeds are classified by different thresholds, and it cannot be guaranteed that adjacent values ​​will be classified in the same category. Wind speed values ​​with small differences will not be in the same category, which will lead to the distortion and discontinuity of the score. For example, if 20 m / s is used as the threshold for a strong wind event, assuming that a strong wind occurs, the wind speed forecast data is 19.99 m / s, which scores 0, while the wind speed forecast data is 20.01 m / s, which scores 1, a perfect score. It can be said that the two forecast levels are almost the same, differing by only 0.02 m / s, but the scores are completely different. This is obviously unreasonable. Although 19.99 m / s is not a hit in the traditional sense, such a prediction still has some reference value, and this also leads to the dispersion and discontinuity of the scoring curve. The second type is non-discrete, non-quantitative testing methods (RMSE, MAE, correlation coefficient, etc.). These methods lack sensitivity to different wind speeds. Deviations occurring at low wind speeds and errors near high wind speed thresholds are scored the same, but the level of attention given to these two types of errors differs significantly for disaster prevention needs (power disaster prevention doesn't focus on low-wind-speed errors, but rather on errors near high wind speed thresholds). Furthermore, most of these two types of testing indicators do not consider the double penalty of location. For example, if a strong wind is forecast for a certain location, but the actual location is far away, not only is the forecast for that location invalid and therefore receives no points, but the actual location is also missed, resulting in a double "location penalty." Moreover, as forecast resolution becomes increasingly refined, this double penalty is becoming more severe. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the aforementioned background technology and provide a general verification method, system, and storage medium for wind speed forecasting in the context of power grid-related wind disasters.

[0007] The technical solution adopted in this invention is: a general verification method for wind speed forecasting in the context of power grid-related wind disasters, comprising:

[0008] Using wind speed observation data and wind speed forecast data, calculate the general verification score of wind speed forecast at a single point within the scope of wind speed prediction interest; calculate the set of general verification score values ​​for wind speed forecast within the neighborhood; determine the maximum value of the general verification score for wind speed forecast within the set; determine whether the disaster-causing wind speed is a hit or a false alarm based on the general verification score for wind speed forecast within the neighborhood over a period of time; and obtain the spatial distribution characteristics of the location error based on the location coordinates of the maximum value of the general verification score for wind speed forecast over a period of time.

[0009] Furthermore, the focus area for wind speed forecasting is set as WS.bom ~WS top , among which, WS top This represents the upper limit of the area of ​​interest for wind speed forecasting; WS bom This represents the lower limit of the area of ​​concern for wind speed forecasting.

[0010] Furthermore, the calculation of the general verification score for single-point wind speed forecast includes:

[0011] 1) When wind speed observation data u <WS bom hour:

[0012] a) Wind speed forecast data x <WS bom At that time, it is not included in the inspection;

[0013] b) Wind speed forecast data x ≥ WS bom hour;

[0014]

[0015] 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour:

[0016]

[0017] 3) When wind speed observation data u ≥ WS top hour:

[0018]

[0019] Where x represents wind speed forecast data; u represents wind speed observation data; Score represents the general verification score for wind speed forecasts; and w represents the ratio of the cost of false alarm defense to the value of accurate forecast disaster prevention.

[0020] Furthermore, let the wind speed forecast data at a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2. n}, the number of matching grid points or stations is n, based on the wind speed forecast data F x By matching the wind speed data with n wind speed observation data, a general verification score for wind speed forecast is calculated, resulting in a set of general verification score values ​​for wind speed forecast within the neighborhood.

[0021] Furthermore, the maximum value of the general verification score for wind speed forecasts, Scoreneighbor, within the set is determined using the following formula:

[0022] Scoreneighbor=max(dw i Scorei i = 1, 2, ..., n

[0023] Among them, dw i Score is the weighting coefficient for the i-th wind speed observation data. i The score for the i-th wind speed observation data.

[0024] Furthermore, the number of positive values ​​in the general verification score of wind speed forecast within a certain period of time, divided by the total number of samples, is taken as the hit rate of the disaster-causing wind speed.

[0025] Furthermore, the number of negative values ​​in the general verification score of wind speed forecast within a certain time range, divided by the total number of samples, is considered as a false alarm.

[0026] Furthermore, the spatial distribution characteristics of the position error were obtained through the following method:

[0027] When calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts are retained. By calculating the average latitude and longitude, the average latitude and longitude deviation is obtained as the spatial distribution characteristics of the location error.

[0028] A system for implementing the above-mentioned general verification method for wind speed forecasting in the context of power grid wind disasters includes a data acquisition module for acquiring wind speed observation data and wind speed forecast data.

[0029] The scoring calculation module is used to calculate the general verification score of wind speed forecast at a single point within the wind speed prediction focus area using wind speed observation data and wind speed forecast data; calculate the set of general verification score values ​​for wind speed forecast within the neighborhood; and determine the maximum value of the general verification score for wind speed forecast within the set.

[0030] The wind speed verification module is used to determine whether the disaster-causing wind speed is a hit or a false alarm based on the general verification score of wind speed forecast within a neighborhood over a period of time; and to obtain the spatial distribution characteristics of the location error based on the location coordinates of the maximum value of the general verification score of wind speed forecast over a period of time.

[0031] Set the wind speed forecast focus area (WS) bom ~WS top , among which, WS top This represents the upper limit of the area of ​​interest for wind speed forecasting; WS bom This represents the lower limit of the area of ​​concern for wind speed forecasting.

[0032] The calculation of the general verification score for single-point wind speed forecast includes:

[0033] 1) When wind speed observation data u <WS bom hour:

[0034] a) Wind speed forecast data x <WSbom At that time, it is not included in the inspection;

[0035] b) Wind speed forecast data x ≥ WS bom hour;

[0036]

[0037]

[0038] 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour:

[0039]

[0040] 3) When wind speed observation data u ≥ WS top hour:

[0041]

[0042] Where x represents wind speed forecast data; u represents wind speed observation data; Score represents the general verification score for wind speed forecasts; and w represents the ratio of the cost of false alarm defense to the value of accurate forecast disaster prevention.

[0043] Let the wind speed forecast data at a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2. n}, the number of matching grid points or stations is n, based on the wind speed forecast data F x By matching the wind speed data with n wind speed observation data, a general verification score for wind speed forecast is calculated, resulting in a set of general verification score values ​​for wind speed forecast within the neighborhood.

[0044] The maximum general verification score for wind speed forecasts within the set, Scoreneighbor, is determined using the following formula:

[0045] Scoreneighbor=max(dw i Score i i = 1, 2, ..., n

[0046] Among them, dw i Score is the weighting coefficient for the i-th wind speed observation data. i The score for the i-th wind speed observation data.

[0047] The number of positive values ​​in the general verification score of wind speed forecast within a neighborhood over a certain period of time, divided by the total number of samples, is taken as the hit rate of the disaster-causing wind speed.

[0048] The number of negative values ​​in the general verification score of wind speed forecast within a certain time range, divided by the total number of samples, is considered as a false alarm.

[0049] The spatial distribution characteristics of the position error were obtained through the following method:

[0050] When calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts are retained. By calculating the average latitude and longitude, the average latitude and longitude deviation is obtained as the spatial distribution characteristics of the location error.

[0051] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in any of the preceding claims.

[0052] This invention aims to improve the application of wind speed forecasting in the power industry. It primarily addresses the sensitivity of power-related wind disasters to different wind speeds (sensitive only to the vicinity of the disaster-causing wind speed). By setting a self-defined threshold, a function is constructed to be more sensitive to errors within the wind speed range of interest. Based on actual wind speed observations, a hyperbolic tangent function is constructed using the e-exponential function to form a bounded, unclassified, sensitive, and smoothly varying evaluation score. Based on the neighborhood method, a statistical scheme is implemented to match forecast and observation data, and to assign a set of score values ​​to forecast values, tailored to specific industry needs. This avoids double penalties based on location within a reasonable range, while also reflecting the accuracy of the forecast location. Finally, a general comprehensive verification index is formed, along with a set of characteristic indicators for wind speed forecast deviation (hit and false alarm situations, location deviation indicators). The general comprehensive verification index can be used for comprehensive decision-making and to improve the objective function of forecast modeling (specifically for power disasters). The characteristic indicators of wind speed forecast deviation can also be used to assist decision-making and error analysis, contributing to the improvement of forecast error research.

[0053] This invention addresses the need for evaluating the quality of wind speed forecasts in response to power grid wind disasters. It solves the problems of current wind speed testing schemes (primarily derived from the meteorological industry) which lack specificity and suffer from issues such as discontinuous scoring and double penalties. The biggest difference between this invention and existing technologies is that it firstly provides a completely customized testing scheme specifically tailored to the testing needs of power grid wind disasters, while simultaneously resolving and rationalizing many problems of existing technologies, as detailed below:

[0054] 1) Compared with the prior art, the present invention is fully designed for the power industry’s disaster prevention needs for strong wind disasters, and has the characteristics of localized threshold setting and scheme selection, and has high pertinence, customization and universal applicability.

[0055] 2) This invention eliminates the need for a unified interpolation scheme between wind speed observation data and wind speed prediction data, thus avoiding errors introduced by the interpolation scheme. It also offers better adaptability to different data types.

[0056] 3) This invention avoids the problems of distortion, discontinuity, lack of sensitivity, and double penalty for location in traditional methods. The method of this invention is continuous and distortion-free, more sensitive to high wind speed errors, avoids double penalty for location, and reasonably reflects the accuracy of the predicted location.

[0057] 4) This invention takes into account the trade-off between the cost of disaster prevention and defense and the value of accurate disaster forecasting, and has strong practical decision-making value.

[0058] 5) This invention is simple to calculate and has clear physical significance. The general comprehensive test index can be used for comprehensive decision-making and can also be used to improve the objective function of forecast modeling (specifically for power disasters).

[0059] 6) This product ultimately forms a general comprehensive verification index, and also provides a set of characteristic indicators for wind speed forecast deviations (hit and false alarm situations, direction of positional deviation, etc.). These characteristic indicators can also be used to assist in decision-making and error analysis, contributing to improvements in forecast error research. Attached Figure Description

[0060] Figure 1 A flowchart of the method provided in an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram showing the distribution of general verification scores for wind speed forecasts when the observed wind speed data u < 10 m / s and the predicted wind speed data is 10-30 m / s.

[0062] Figure 3 This is a schematic diagram showing the distribution of the general verification score for wind speed prediction data when 10m / s ≤ wind speed observation data u < 20m / s;

[0063] Figure 4 This is a schematic diagram showing the distribution of the general verification score for wind speed prediction data when the observed wind speed data u≥20m / s.

[0064] Figure 5 A schematic diagram illustrating the matching of forecast points and observation points;

[0065] Figure 6 This is a system diagram showing the module connections of the present invention. Detailed Implementation

[0066] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments to facilitate a clear understanding of the present invention, but these descriptions do not constitute a limitation on the present invention.

[0067] Example 1

[0068] like Figure 1 As shown, the present invention provides a general verification method for wind speed forecasting in the context of power grid-related wind disasters, comprising the following steps:

[0069] 1. Acquire wind speed observation data and wind speed forecast data;

[0070] Based on actual data conditions, wind speed forecast data is generally gridded data, while wind speed observation data may be either gridded or station-specific; this method is universally applicable. The method does not require uniform interpolation between wind speed observation data and wind speed forecast data, thus avoiding errors introduced by interpolation. This method calculates wind speed forecast data grid-by-grid (or station-by-station) and forecast lead time, finally averaging to obtain the overall conclusion.

[0071] 2. Setting the disaster threshold for wind speed and the scope of attention for wind speed prediction;

[0072] The wind speed at which a power outage will occur depends on the design and wind resistance of the power system. Generally, power systems are designed to withstand local maximum wind speeds based on geographical location and climate conditions. Wind speed limits may vary from region to region. Generally, the risk of a power outage increases when wind speeds exceed 20 meters per second (72 kilometers per hour). However, this is only a general reference value. In reality, different power systems may have different wind speed limits, and other factors such as equipment quality and maintenance conditions will also be considered.

[0073] This method requires determining the wind speed-induced disaster threshold and the scope of concern for wind speed prediction. The upper limit of the scope of concern for wind speed prediction (i.e., the wind speed-induced disaster threshold) is defined using WS. top This indicates that the lower limit is represented by WS. bom This embodiment assumes 20 m / s as the disaster-causing wind speed threshold, and the lower limit of the predicted concern range is 10 m / s. That is, in addition to testing disaster-causing strong winds exceeding 20 m / s, this embodiment also defines the range of 10-20 m / s, which is close to strong winds, as the predicted concern range. The specific wind speed threshold should be set according to the design and operation requirements of the local power system.

[0074] 3. Calculate the general verification score for single-point wind speed forecast;

[0075] For wind speed testing in typhoon disaster assessment, the most important principle is to examine the accuracy of forecasts while minimizing the rate of false alarms. Therefore, the main focus is on the accuracy and false alarm aspects of the forecasts that could cause damage. If the actual wind speed does not occur, the impact of false alarms is primarily considered; if the actual wind speed does occur, the impact of accurate forecasts is considered. This method is a comprehensive trade-off between the two, using the e-exponential function to construct a hyperbolic tangent function (also commonly expressed as tanh) to avoid abrupt changes in the score, ensuring a smooth, continuous, and non-discrete score.

[0076] Define x as wind speed forecast data, u as wind speed observation data, Score as the general verification score for wind speed forecasts, and w as the ratio of the cost of false alarm prevention to the disaster prevention value of accurate forecasts. w is generally between 0 and 1 and is set by power disaster prevention technicians. The Score ranges from -w to 1, where 1 represents a completely accurate prediction, 0 to 1 indicates no accurate prediction but some reference value, and 0 indicates no impact. -w represents a completely false alarm, and -w to 0 represents no false alarms but some misleading over-forecasting. A positive Score has positive reference value, and a negative Score has negative reference value. The overall forecast value is obtained by averaging the scores. The specific scoring calculation scheme is as follows:

[0077] 1) When wind speed observation data u <WS bom hour:

[0078] a) Wind speed forecast data x <WS bom When Score = Nan;

[0079] When both wind speed observation data and wind speed forecast data are much lower than the disaster-causing wind speed, although this is a correct negation of the wind disaster and is a high-probability time in the sample, such a correct forecast is almost meaningless from an application perspective. We do not include such samples in our test, thus highlighting the information within the scope of wind speed prediction that we are concerned with.

[0080] b) Wind speed forecast data x ≥ WS bom hour;

[0081]

[0082] For the sake of visual demonstration, assume WS top 20m / s, WS bom The wind speed is 10 m / s, and w = 0.5. When the observed wind speed data u < 10 m / s, the correspondence between the wind speed forecast data and the general verification score for wind speed forecast is as follows: Figure 2 As shown, the rating transitions smoothly, from having no value to having somewhat misleading over-forecasts, and finally to having a completely false negative value.

[0083] 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour:

[0084]

[0085] For the sake of visual demonstration, assume WS top 20m / s, WS bomThe wind speed is 10 m / s, and w = 0.5. When 10 m / s ≤ wind speed observation data u < 20 m / s, the correspondence between the wind speed forecast data and the general verification score for wind speed forecast is as follows: Figure 3 As shown, the rating transitions smoothly, from having no value to having somewhat misleading over-forecasts, and finally to having a completely false negative value.

[0086] 3) When wind speed observation data u ≥ WS top hour:

[0087]

[0088] For a more intuitive demonstration, assuming the observed wind speed u ≥ 20 m / s, the correspondence between the wind speed forecast data and the general verification score for wind speed forecast is as follows: Figure 4 As shown, the rating transitions smoothly, from having no value to approaching the threshold with some reference value, and finally to a completely accurate positive value.

[0089] 4. Calculate the set of general verification scores for wind speed forecasts within the neighborhood;

[0090] Since most previous general verification scores based on wind speed forecasts did not consider the neighborhood, they still suffered from double penalties due to location bias, necessitating the neighborhood method for improvement. Matching wind speed forecast data with observed wind speed data is a crucial step in the neighborhood method's forecast verification process. Because current numerical models still have limited capabilities for precise, quantitative forecasting of strong winds, some locational and quantitative errors in forecasts are tolerable to the public. For example, if a strong wind is forecast for a certain location, but the actual location is far away, not only is the forecast for that location invalid and receives no points, but the actual location, if not forecasted, is also considered a missed report and receives no points—a "double penalty" that clearly does not reflect reality. In power industry disaster prevention, precision down to every verification grid point is often unnecessary, and a certain tolerance range is usually maintained. Therefore, the neighborhood matching method is more suitable. The matching method between forecast verification points and observation points is described below.

[0091] like Figure 5 As shown, the method for matching forecast observation data is as follows:

[0092] Based on the neighborhood matching between the forecast verification point and the observation point, let the wind speed forecast data for a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2. n}, where the number of matching grid points or stations is n, i.e., the wind speed forecast data F x The wind speed forecast general verification score is calculated by matching it with n wind speed observation data. Figure 5This is a schematic diagram. The observations and forecasts shown are both gridded files. If the forecast is a gridded file, it doesn't matter if the observation is a station file; the neighborhood radius R can still be selected. The neighborhood radius R can be automatically set according to the actual disaster prevention needs of the power industry. Ultimately, a set of general verification scores for wind speed forecasts within the neighborhood is obtained.

[0093] 5. Determine the maximum value of the general verification score for wind speed forecasts within the ensemble;

[0094] Since each wind speed forecast corresponds to a set of general verification scores for wind speed forecasts, and power industry disaster prevention technicians actually want each wind speed forecast to have exactly one score, it is necessary to use statistical methods to analyze the scoring results. Common statistical indicators include the maximum value, ensemble mean, or median of the score set. To enhance the accuracy of forecast location, this method uses a distance-weighted maximum score statistical method to determine the maximum general verification score for wind speed forecasts at the forecast verification point, Scoreneighbor. Here, Score... i This is a general verification score for wind speed forecasts calculated from wind speed forecast data and observation grid points or stations within each matching range. The distance weighting coefficient dw ranges from 0 to 1. When the location is at the maximum distance, dw is 0; when the location is at the location specified in the wind speed forecast data, dw is 1; and for other locations, dw values ​​fall between 0 and 1, linearly distributed according to the inverse distance weighting. This design avoids severe double penalties while reasonably reflecting the value of forecast location accuracy. In practical use, the value of dw can be set according to the actual requirements for location accuracy.

[0095] Scoreneighbor=max(dw i Score i i = 1, 2, ..., n;

[0096] Of course, if the actual disaster prevention needs of the power industry may not have such strict requirements regarding location, then the location weighting coefficient can be omitted here. Only the scope of the investigation can be considered.

[0097] Scoreneighbor = max(Score i i = 1, 2, ..., nScore.

[0098] 6. Set characteristic indicators;

[0099] This method calculates the general verification score of wind speed forecast data for each grid point (site) within a certain time period, and finally averages the scores to obtain the overall value of the forecast for a certain period of time to be examined, thus forming a general comprehensive verification index.

[0100] Meanwhile, a set of characteristic indicators of wind speed forecast deviation can also be given during the calculation process. Unlike the overall test score, which is a comprehensive indicator, the characteristic indicators are various aspects that reflect the characteristics of the forecast error.

[0101] Specifically as follows:

[0102] ①. The number of positive values ​​in the general verification score of wind speed forecast within a certain time range / the total number of samples is taken as the hit of the disaster-causing wind speed, and the number of negative values ​​in the general verification score of wind speed forecast within a certain time range / the total number of samples is taken as the false alarm.

[0103] ②. Based on the location coordinates of the maximum value of the general verification score for wind speed forecasts over a period of time, obtain the spatial distribution characteristics of the location error. Specifically, this includes: when calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, retaining multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts, and obtaining the average latitude and longitude deviation as the spatial distribution characteristics of the location error by calculating the average latitude and longitude. This can also be displayed in a two-dimensional plane using a heat map scatter plot.

[0104] These characteristic indicators can be used for comprehensive decision-making and to improve the objective function of forecast modeling (specifically for power disasters). Characteristic indicators such as wind speed forecast deviation can also be used for auxiliary decision-making and error analysis, which helps to improve the research on forecast errors.

[0105] This method ultimately forms a general comprehensive verification index. Simultaneously, during the calculation process, it also provides a set of characteristic indices for wind speed forecast deviation. Unlike the overall verification score, which is a comprehensive index, these characteristic indices reflect the features of the forecast error from various aspects. The positive and negative values ​​of the score can be averaged separately to determine the occurrence and false alarms of the disaster-causing wind speed. Alternatively, a set of score values ​​can be obtained, and the coordinates of the selected maximum value can be recorded, thus allowing for the statistical analysis of the spatial distribution characteristics of location errors. The general comprehensive verification index can be used for integrated decision-making and to improve the objective function of forecast modeling (specifically for power disasters), while characteristic indices such as wind speed forecast deviation can be used for auxiliary decision-making and error analysis, contributing to the improvement of forecast error research.

[0106] Example 2

[0107] like Figure 6 As shown, a system for implementing the general verification method for wind speed forecasting in the context of power grid-related wind disasters includes...

[0108] The data acquisition module is used to collect wind speed observation data and wind speed forecast data;

[0109] The scoring calculation module is used to calculate the general verification score of wind speed forecast at a single point within the wind speed prediction focus area using wind speed observation data and wind speed forecast data; calculate the set of general verification score values ​​for wind speed forecast within the neighborhood; and determine the maximum value of the general verification score for wind speed forecast within the set.

[0110] The wind speed verification module is used to determine whether the disaster-causing wind speed is a hit or a false alarm based on the general verification score of wind speed forecast within a neighborhood over a period of time; and to obtain the spatial distribution characteristics of the location error based on the location coordinates of the maximum value of the general verification score of wind speed forecast over a period of time.

[0111] Set the wind speed forecast focus area (WS) bom ~WS top , among which, WS top This represents the upper limit of the area of ​​interest for wind speed forecasting; WS bom This represents the lower limit of the area of ​​concern for wind speed forecasting.

[0112] The calculation of the general verification score for single-point wind speed forecast includes:

[0113] 1) When wind speed observation data u <WS bom hour:

[0114] a) Wind speed forecast data x <WS bom At that time, it is not included in the inspection;

[0115] b) Wind speed forecast data x ≥ WS bom hour;

[0116]

[0117] 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour:

[0118]

[0119] 3) When wind speed observation data u ≥ WS top hour:

[0120]

[0121] Where x represents wind speed forecast data; u represents wind speed observation data; Score represents the general verification score for wind speed forecasts; and w represents the ratio of the cost of false alarm defense to the value of accurate forecast disaster prevention.

[0122] Let the wind speed forecast data at a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2.n}, the number of matching grid points or stations is n, based on the wind speed forecast data F x By matching the wind speed data with n wind speed observation data, a general verification score for wind speed forecast is calculated, resulting in a set of general verification score values ​​for wind speed forecast within the neighborhood.

[0123] The maximum general verification score for wind speed forecasts within the set, Scoreneighbor, is determined using the following formula:

[0124] Scoreneighbor=max(dw i Score i i = 1, 2, ..., n:

[0125] Among them, dw i Score is the weighting coefficient for the i-th wind speed observation data. i The score for the i-th wind speed observation data.

[0126] The number of positive values ​​in the general verification score of wind speed forecast within a neighborhood over a certain period of time, divided by the total number of samples, is taken as the hit rate of the disaster-causing wind speed.

[0127] The number of negative values ​​in the general verification score of wind speed forecast within a certain time range, divided by the total number of samples, is considered as a false alarm.

[0128] The spatial distribution characteristics of the position error were obtained through the following method:

[0129] When calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts are retained. By calculating the average latitude and longitude, the average latitude and longitude deviation is obtained as the spatial distribution characteristics of the location error.

[0130] Example 3

[0131] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described energy efficiency improvement method and embodiments. Alternatively, when executed by a processor, the computer program implements the functions of each module / unit in the above-described energy efficiency improvement system.

[0132] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0134] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its scope of protection. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the scope of protection of the pending claims of the invention.

[0137] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A general verification method for wind speed forecasting in the context of power grid-related wind disasters, characterized in that: include Calculate the general verification score for wind speed forecast at a single point within the scope of wind speed prediction interest using wind speed observation data and wind speed forecast data; calculate the set of general verification score values ​​for wind speed forecast within the neighborhood range. Determine the maximum value of the general verification score for wind speed forecasts within the set; The accuracy of the disaster-causing wind speed prediction is determined by the general verification score of wind speed forecast within a certain period of time; the spatial distribution characteristics of the location error are obtained by the location coordinates of the maximum value of the general verification score of wind speed forecast within a certain period of time. Set the wind speed forecast focus area (WS) bom ~WS top , among which, WS top This represents the upper limit of the area of ​​interest for wind speed forecasting; WS bom This represents the lower limit of the area of ​​concern for wind speed forecasting. The calculation of the general verification score for single-point wind speed forecast includes: 1) When wind speed observation data u <WS bom hour: a) Wind speed forecast data x <WS bom At that time, it is not included in the inspection; b) Wind speed forecast data x ≥ WS bom hour; 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour: 3) When wind speed observation data u ≥ WS top hour: Where x represents wind speed forecast data; u represents wind speed observation data; Score represents the general verification score for wind speed forecasts; and w represents the ratio of the cost of false alarm defense to the value of accurate forecast disaster prevention.

2. The general verification method for wind speed forecasting in the context of power grid-related wind disasters according to claim 1, characterized in that: Let the wind speed forecast data at a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2. n }, the number of matching grid points or stations is n, based on the wind speed forecast data F x By matching the wind speed data with n wind speed observation data, a general verification score for wind speed forecast is calculated, resulting in a set of general verification score values ​​for wind speed forecast within the neighborhood.

3. The general verification method for wind speed forecasting in the context of power grid-related wind disasters according to claim 2, characterized in that: The maximum general verification score for wind speed forecasts within the set, Scoreneighbor, is determined using the following formula: Scoreneighbor=max(dw i .Score i ) i=1,2,...,n Among them, dw i Score is the weighting coefficient for the i-th wind speed observation data. i The score for the i-th wind speed observation data.

4. The general verification method for wind speed forecasting in the context of power grid-related wind disasters according to claim 1, characterized in that: The number of positive values ​​in the general verification score of wind speed forecast within a neighborhood over a certain period of time, divided by the total number of samples, is taken as the hit rate of the disaster-causing wind speed.

5. The general verification method for wind speed forecasting in the context of power grid-related wind disasters according to claim 1, characterized in that: The number of negative values ​​in the general verification score of wind speed forecast within a certain time range, divided by the total number of samples, is considered as a false alarm.

6. The general verification method for wind speed forecasting in the context of power grid-related wind disasters according to claim 1, characterized in that: The spatial distribution characteristics of the position error were obtained through the following method: When calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts are retained. By calculating the average latitude and longitude, the average latitude and longitude deviation is obtained as the spatial distribution characteristics of the location error.

7. A system for implementing the general verification method for wind speed forecasting in power-related wind disasters as described in any one of claims 1-6, characterized in that: include The data acquisition module is used to collect wind speed observation data and wind speed forecast data; The scoring calculation module is used to calculate the general verification score of wind speed forecast at a single point within the scope of wind speed prediction interest using wind speed observation data and wind speed forecast data; and to calculate the set of general verification score values ​​of wind speed forecast within the neighborhood. Determine the maximum value of the general verification score for wind speed forecasts within the set; The wind speed verification module is used to determine whether the disaster-causing wind speed is a hit or a false alarm based on the general verification score of wind speed forecast within a neighborhood over a period of time; and to obtain the spatial distribution characteristics of the location error based on the location coordinates of the maximum value of the general verification score of wind speed forecast over a period of time.

8. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 7, characterized in that: Set the wind speed forecast focus area (WS) bom ~WS top , among which, WS top This represents the upper limit of the area of ​​interest for wind speed forecasting; WS bom This represents the lower limit of the area of ​​concern for wind speed forecasting.

9. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 8, characterized in that: The calculation of the general verification score for single-point wind speed forecast includes: 1) When wind speed observation data u <WS bom hour: a) Wind speed forecast data x <WS bom At that time, it is not included in the inspection; b) Wind speed forecast data x ≥ WS bom hour; 2) When the wind speed observation data u is located in WS bom ≤u <WS top hour: 3) When wind speed observation data u ≥ WS top hour: Where x represents wind speed forecast data; u represents wind speed observation data; Score represents the general verification score for wind speed forecasts; and w represents the ratio of the cost of false alarm defense to the value of accurate forecast disaster prevention.

10. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 9, characterized in that: Let the wind speed forecast data at a certain point be F. x Define O x It is a group of F x The set of wind speed observation data {O1, O2, ..., O2} is the set of all grid points or stations within a circle radially expanding with radius R centered at point O2. n }, the number of matching grid points or stations is n, based on the wind speed forecast data F x By matching the wind speed data with n wind speed observation data, a general verification score for wind speed forecast is calculated, resulting in a set of general verification score values ​​for wind speed forecast within the neighborhood.

11. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 10, characterized in that: The maximum general verification score for wind speed forecasts within the set, Scoreneighbor, is determined using the following formula: Scoreneighbor=max(dw i .Score i ) i=1,2,...,n Among them, dw i Score is the weighting coefficient for the i-th wind speed observation data. i The score for the i-th wind speed observation data.

12. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 7, characterized in that: The number of positive values ​​in the general verification score of wind speed forecast within a neighborhood over a certain period of time, divided by the total number of samples, is taken as the hit rate of the disaster-causing wind speed.

13. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 7, characterized in that: The number of negative values ​​in the general verification score of wind speed forecast within a certain time range, divided by the total number of samples, is considered as a false alarm.

14. The system for a general verification method of wind speed forecast for power grid-related wind disasters according to claim 7, characterized in that: The spatial distribution characteristics of the position error were obtained through the following method: When calculating the maximum value of the general verification score for multiple wind speed forecasts over a period of time, multiple latitude and longitude coordinates corresponding to the maximum value of the general verification score for wind speed forecasts are retained. By calculating the average latitude and longitude, the average latitude and longitude deviation is obtained as the spatial distribution characteristics of the location error.

15. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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