CN118033784A - Meteorological prediction method and system for micro-topography overhead transmission line - Google Patents
Meteorological prediction method and system for micro-topography overhead transmission line Download PDFInfo
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Abstract
The invention discloses a weather prediction method and a weather prediction system for a micro-terrain overhead transmission line, which relate to the technical field of power grid weather prediction and comprise the steps of collecting weather data, fusing the weather data of different data sources and extracting characteristics; establishing a weather prediction model and training and evaluating the model by using weather observation data; and the meteorological conditions obtained by the prediction model are used for energy management and scheduling decisions of the power grid, and the prediction model is optimized according to the running conditions. The meteorological prediction method for the micro-terrain overhead transmission line uses various data sources to perform data fusion, improves prediction accuracy, can be applied to energy management and scheduling decision for the micro-terrain overhead transmission line, improves system efficiency and economy, optimizes and adjusts according to actual running conditions, improves performance and stability of a prediction model, and achieves better effects in accuracy, system efficiency and stability.
Description
Technical Field
The invention relates to the technical field of power grid weather prediction, in particular to a weather prediction method for a micro-topography overhead power transmission line.
Background
In the field of weather prediction of micro-topography overhead transmission lines, the prior art is mainly based on traditional weather prediction methods, and the methods are mostly dependent on weather observation site data and a physical-based numerical prediction model. These traditional methods are relatively mature in weather prediction of common terrain and can provide a degree of accurate prediction. However, these conventional methods face many challenges in the application of micro-topography overhead transmission lines due to the specificity of the micro-topography, such as the topography complexity, climate variability, and locality of the geographical environment.
On the one hand, the prior art is limited in spatial resolution, making it difficult for them to accurately capture microscopic meteorological changes inside a micro-terrain area. For example, terrain fluctuations may result in rapid changes in wind speed and direction, which are critical to safe operation of the transmission line. Furthermore, conventional weather prediction models have limited accuracy in handling extreme weather events, which is particularly pronounced when climate change is increasingly exacerbated.
On the other hand, the existing weather prediction technology has the defects in data integration and processing. While the use of multi-source data has become a trend, it remains a challenge to effectively integrate data from different sources, having different formats and qualities. These techniques also exhibit limitations in converting data into information that is of practical significance to transmission line operation.
Furthermore, the prior art has limited capabilities in terms of real-time prediction and adaptability. Since the environmental conditions of a micro-terrain transmission line may change rapidly, it is desirable to have a predictive system that responds quickly and adapts to these changes. However, the flexibility and adaptability of current technology in this respect has not reached a desired level.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing weather prediction method has the problems of low data utilization rate, limited adaptability, low prediction accuracy and optimization of how to accurately capture microscopic weather changes in a micro-terrain area.
In order to solve the technical problems, the invention provides the following technical scheme: a weather prediction method for a micro-topography overhead transmission line comprises the steps of collecting weather data, fusing the weather data of different data sources, and extracting characteristics; establishing a weather prediction model and training and evaluating the model by using weather observation data; and the meteorological conditions obtained by the prediction model are used for energy management and scheduling decisions of the power grid, and the prediction model is optimized according to the running conditions.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: the method comprises the steps of acquiring meteorological data from different data sources, acquiring meteorological data from ground meteorological stations, satellites and sensor networks from different visual angles, reflecting meteorological changes in micro-terrain areas, enabling the data to enter a fusion stage, enabling information of each data source to be given a weight, providing input for feature extraction, and enabling integrated data to be expressed as follows:
Wherein S i (T) is the data of the ith data source at time T, w i is the weight of the ith data source, T 0 is the start time, and T is the time window.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: extracting features comprises extracting features from fused data, analyzing each data feature, identifying and highlighting weather features for a specific micro-topography area, extracting features and preserving trends and modes of the data, allowing a prediction system to adapt to different weather conditions and environmental changes, and the data fusion and feature extraction are expressed as:
where D is the dataset generated by F data, D j is the j-th feature, and a j,μj,σj is the scale factor, mean and standard deviation, respectively.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: the method comprises the steps of establishing a weather prediction model by using a random forest, selecting and configuring the random forest model according to model size and depth parameters, taking extracted characteristics and historical weather observation data as input, and expressing the weather prediction model as:
Where RF k is the kth random forest model, beta k is the coefficient of the kth model, and H is historical meteorological observation data.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: the training and evaluation includes dividing the collected historical meteorological observation data set into a training set and a verification set, wherein the training set is used for model training, the verification set is used for model evaluation, and the training evaluation process is expressed as:
wherein, P i is the predicted value, P actual,i is the actual value, and N is the number of data points.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: the energy management and scheduling decision of the power grid comprises the steps of scheduling energy supply and storage equipment of the power grid according to a prediction result, adjusting an operation strategy of a power grid system, and making a load management strategy, wherein the energy management and scheduling decision process is expressed as follows:
wherein, gamma i is the adjustment coefficient of the Cost function i, cost i is the Cost function, delta is the adjustment coefficient of the Risk function, and Risk (P) is the Risk function.
As a preferable scheme of the weather prediction method for the micro-terrain overhead transmission line, the invention comprises the following steps: the optimizing the prediction model comprises monitoring real-time operation data of the power grid power flow, feeding the real-time operation data back to the prediction model, and optimizing and adjusting the prediction model, wherein the optimizing and adjusting process is expressed as:
wherein Q is a prediction model, R is real-time operation data, lambda is a learning rate, Gradient of the loss function for Q.
Another object of the present invention is to provide a weather prediction system for a micro-topography overhead transmission line, which can perform feature extraction on weather data fused by different data sources through a feature extraction module, so as to solve the problem of lack of customized prediction models for micro-topography at present.
As a preferable scheme of the weather prediction system for the micro-terrain overhead transmission line, the weather prediction system comprises the following components: the system comprises a feature extraction module, a model establishment module and a model optimization module; the characteristic extraction module is used for extracting the operating characteristics of the micro-topography overhead transmission line from meteorological data fused from different data sources; the model building module uses a random forest algorithm, and combines the fused meteorological data and historical observation data to build a meteorological prediction model; and the model optimization module optimizes and adjusts the prediction model according to the accuracy of the prediction result and the actual running condition.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that execution of the computer program by the processor is a step of implementing a weather prediction method for a micro-terrain overhead transmission line.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a weather prediction method for a micro-terrain overhead transmission line.
The invention has the beneficial effects that: the weather prediction method for the micro-terrain overhead transmission line uses various data sources to perform data fusion, improves prediction accuracy, adopts a machine learning algorithm to establish a prediction model, automatically learns and adapts to the characteristics of a power grid system, and can be applied to energy management and scheduling decision for the micro-terrain overhead transmission line, improve the efficiency and economy of the system, optimize and adjust according to actual running conditions and improve the performance and stability of the prediction model.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without the need of creative efforts for a person of ordinary skill in the art. Wherein:
fig. 1 is an overall flowchart of a weather prediction method for a micro-terrain overhead transmission line according to a first embodiment of the present invention.
Fig. 2 is an overall flowchart of a weather prediction system for a micro-terrain overhead transmission line according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a weather prediction method for a micro-terrain overhead transmission line, including:
S1: meteorological data are collected, the meteorological data of different data sources are fused, and characteristics are extracted.
Still further, collecting the weather data includes obtaining the weather data from different data sources.
It should be noted that, meteorological data of different view angles are obtained from ground meteorological stations, satellites and sensor networks, the meteorological changes in the micro-topography area are reflected, the data enter a fusion stage, each data source information is given a weight, input is provided for feature extraction, and the integrated data are expressed as:
Wherein S i (T) is the data of the ith data source at time T, w i is the weight of the ith data source, T 0 is the start time, and T is the time window.
Still further, extracting features includes extracting features from the fused data.
It should be noted that analyzing each data feature, identifying and highlighting meteorological features for a particular micro-terrain area, extracting features and preserving trends and patterns of the data, allowing the prediction system to adapt to different meteorological conditions and environmental changes, data fusion and feature extraction are expressed as:
Wherein D j is the j-th feature, and α j,μj,σj is the scaling factor, mean and standard deviation, respectively.
It should also be noted that the characteristics include basic meteorological parameters such as temperature, humidity, wind speed, illumination intensity, etc., and more complex indicators such as sunshine hours, wind energy potential, etc.
S2: a weather prediction model is established and trained and evaluated using weather observation data.
Still further, building the weather prediction model includes building the weather prediction model using random forests.
It should be noted that, according to the model size and depth parameters, a random forest model is selected and configured, and the extracted characteristics and the historical meteorological observation data are taken as inputs, and the meteorological prediction model is expressed as:
Where RF k is the kth random forest model, beta k is the coefficient of the kth model, and H is historical meteorological observation data.
Still further, training and evaluation includes dividing the collected historical meteorological observation data set into a training set and a validation set.
It should be noted that the training set is used for model training, the verification set is used for model evaluation, and the training evaluation process is expressed as:
wherein, P i is the predicted value, P actual,i is the actual value, and N is the number of data points.
It should also be noted that the collected historical meteorological observation data set is divided into a training set and a verification set, wherein the training set accounts for 80% of the total data, the verification set accounts for 20% of the total data, the training set is used for training the model, the verification set is used for evaluating the model, common evaluation indexes such as Root Mean Square Error (RMSE), mean Absolute Error (MAE), mean Absolute Percent Error (MAPE) and the like are used for evaluating the prediction accuracy and stability of the model, and the model is optimized and adjusted according to the verification result.
S3: and the meteorological conditions obtained by the prediction model are used for energy management and scheduling decisions of the power grid, and the prediction model is optimized according to the running conditions.
Further, the energy management and scheduling decisions of the power grid include based on the prediction results.
It should be noted that, the energy supply and storage equipment of the power grid is scheduled, the operation strategy of the power grid system is adjusted, the load management strategy is formulated, and the energy management and scheduling decision process is expressed as follows:
wherein, gamma i is the adjustment coefficient of the Cost function i, cost i is the Cost function, delta is the adjustment coefficient of the Risk function, and Risk (P) is the Risk function.
It should also be noted that, according to the prediction result, the energy supply and storage equipment in the power grid system is reasonably scheduled, the operation strategy of the power grid system is adjusted to improve the energy utilization efficiency and economy, and a reasonable load management strategy is formulated.
Further, optimizing the predictive model includes monitoring real-time operational data of the power grid flow.
It should be noted that, real-time operation data is fed back to the prediction model, the prediction model is optimized and adjusted, and the optimization and adjustment process is expressed as:
wherein Q is a prediction model, R is real-time operation data, lambda is a learning rate, Gradient of the loss function for Q.
It should also be noted that, monitoring the real-time operation data of the power grid system, including the energy generation and consumption data, the actual meteorological observation data, etc., feeds back these data to the prediction model for updating and optimizing the model to adapt to the changing environment and requirements.
Example 2
In order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.
Determining sources and types of meteorological data, wherein the data comprise temperature, humidity, wind speed and illumination intensity, carrying out data fusion and feature extraction from a meteorological sensor, a satellite image and a meteorological forecast model, establishing a meteorological prediction model, selecting a Random Forest (RF) model, inputting the meteorological data and historical meteorological observation data after fusion, outputting the meteorological data and the historical meteorological observation data for a period of time in the future, carrying out training and verification of the model, dividing a collected historical meteorological observation data set into a 80% training set and a 20% verification set, evaluating the accuracy and stability of the model by using evaluation indexes such as Root Mean Square Error (RMSE), average absolute error (MAE), average absolute percentage error (MAPE) and the like, carrying out energy management and scheduling decision of a power grid system according to the prediction result, reasonably scheduling energy supply and storage equipment in the power grid according to the meteorological condition prediction result, and adjusting the operation strategy of the power grid system so as to improve the energy utilization efficiency and economy.
As shown in Table 1, under sunny conditions, the RMSE, MAE and MAPE values of the model are lower, which indicates that the model can accurately predict the weather conditions under the condition of sufficient illumination and stable weather, and in rainy days and cloudy weather, although errors of the model slightly rise, the model still has good adaptability to the complex weather conditions, the influence of the change of wind speed on the model prediction accuracy is large, under the condition of large wind, although the RMSE of the model is slightly higher than that of sunny days, the MAE and MAPE values are relatively lower, which indicates that the model can adapt to the wind speed change, the prediction accuracy is maintained, and the prediction model not only improves the adaptability to the complex weather conditions, but also enhances the prediction accuracy through the multi-source data fusion and machine learning technology, and has obvious advantages compared with the traditional prediction method particularly when the special weather change of micro topography is processed.
TABLE 1 Meteorological prediction model accuracy data sheet
| Sunny day | Rain day | Clouds of people | Strong wind | |
| Temperature (temperature) | 25℃ | 18℃ | 20℃ | 22℃ |
| Humidity of the water | 30％ | 80％ | 60％ | 45％ |
| Wind speed | 10km/h | 5km/h | 7km/h | 20km/h |
| Intensity of illumination | 10000lux | 1000lux | 5000lux | 8000lux |
| Root mean square error | 2.5RMSE | 3.2RMSE | 2.8RMSE | 2.1RMSE |
| Average absolute error | 1.8MAE | 2.4MAE | 2MAE | 1.7MAE |
| Average absolute percentage error | 5MAPE | 6.5MAPE | 5.8MAPE | 4.7MAPE |
Example 3
Referring to fig. 2, for one embodiment of the present invention, a weather prediction system for a micro-terrain overhead power transmission line is provided, including a feature extraction module, a model building module, and a model optimization module.
The feature extraction module is used for extracting the operating features of the micro-topography overhead transmission line from meteorological data fused from different data sources; the model building module uses a random forest algorithm, and combines the fused meteorological data and historical observation data to build a meteorological prediction model; the model optimization module optimizes and adjusts the prediction model according to the accuracy of the prediction result and the actual running condition
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A weather prediction method for a micro-terrain overhead transmission line, comprising:
Collecting meteorological data, fusing the meteorological data of different data sources, and extracting characteristics;
Establishing a weather prediction model and training and evaluating the model by using weather observation data;
And the meteorological conditions obtained by the prediction model are used for energy management and scheduling decisions of the power grid, and the prediction model is optimized according to the running conditions.
2. A weather prediction method for a micro-terrain overhead transmission line as claimed in claim 1, wherein: the method comprises the steps of acquiring meteorological data from different data sources, acquiring meteorological data from ground meteorological stations, satellites and sensor networks from different visual angles, reflecting meteorological changes in micro-terrain areas, enabling the data to enter a fusion stage, enabling information of each data source to be given a weight, providing input for feature extraction, and enabling integrated data to be expressed as follows:
Wherein S i (T) is the data of the ith data source at time T, w i is the weight of the ith data source, T 0 is the start time, and T is the time window.
3. A weather prediction method for a micro-terrain overhead transmission line as claimed in claim 2, wherein: extracting features comprises extracting features from fused data, analyzing each data feature, identifying and highlighting weather features for a specific micro-topography area, extracting features and preserving trends and modes of the data, allowing a prediction system to adapt to different weather conditions and environmental changes, and the data fusion and feature extraction are expressed as:
where D is the dataset generated by F data, D j is the j-th feature, and a j,μj,σj is the scale factor, mean and standard deviation, respectively.
4. A weather prediction method for a micro-terrain overhead transmission line as claimed in claim 3, wherein: the method comprises the steps of establishing a weather prediction model by using a random forest, selecting and configuring the random forest model according to model size and depth parameters, taking extracted characteristics and historical weather observation data as input, and expressing the weather prediction model as:
Where RF k is the kth random forest model, beta k is the coefficient of the kth model, and H is historical meteorological observation data.
5. The weather prediction method for a micro-terrain overhead transmission line according to claim 4, wherein: the training and evaluation includes dividing the collected historical meteorological observation data set into a training set and a verification set, wherein the training set is used for model training, the verification set is used for model evaluation, and the training evaluation process is expressed as:
wherein, P i is the predicted value, P actual,i is the actual value, and N is the number of data points.
6. A weather prediction method for a micro-terrain overhead transmission line as defined in claim 5, wherein: the energy management and scheduling decision of the power grid comprises the steps of scheduling energy supply and storage equipment of the power grid according to a prediction result, adjusting an operation strategy of a power grid system, and making a load management strategy, wherein the energy management and scheduling decision process is expressed as follows:
wherein, gamma i is the adjustment coefficient of the Cost function i, cost i is the Cost function, delta is the adjustment coefficient of the Risk function, and Risk (P) is the Risk function.
7. The weather prediction method for a micro-terrain overhead transmission line according to claim 6, wherein: the optimizing the prediction model comprises monitoring real-time operation data of the power grid power flow, feeding the real-time operation data back to the prediction model, and optimizing and adjusting the prediction model, wherein the optimizing and adjusting process is expressed as:
wherein Q is a prediction model, R is real-time operation data, lambda is a learning rate, Gradient of the loss function for Q.
8. A system employing the weather prediction method for a micro-terrain overhead transmission line as claimed in any one of claims 1 to 7, characterized in that: the system comprises a feature extraction module, a model establishment module and a model optimization module;
the characteristic extraction module is used for extracting the operating characteristics of the micro-topography overhead transmission line from meteorological data fused from different data sources;
The model building module uses a random forest algorithm, and combines the fused meteorological data and historical observation data to build a meteorological prediction model;
and the model optimization module optimizes and adjusts the prediction model according to the accuracy of the prediction result and the actual running condition.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the weather prediction method for a micro-terrain overhead transmission line as claimed in any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the steps of the weather prediction method for a micro-terrain overhead transmission line according to any of claims 1 to 7.
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