Mountain wind speed prediction system

Abstract

The invention discloses a mountain wind speed prediction system, and mainly relates to the field of wind speed prediction. Which comprises a data acquisition module, a data processing module, a prediction model module and a user interface module, and is characterized in that the data acquisition module acquires meteorological data of a mountain region in real time through various sensors and a meteorological station, and the meteorological data comprises meteorological parameters such as wind speed, wind direction, temperature, humidity, air pressure and the like, and terrain and landform information. The mountain wind speed prediction system has the beneficial effects that the mountain wind speed prediction system can remarkably improve the prediction precision of wind speed change under complex terrain conditions by collecting various meteorological data and terrain information and combining with an advanced machine learning algorithm. The high-precision wind speed prediction can effectively help the wind power plant to reasonably configure resources and optimize electric energy production.

Description

Technical Field

[0001] The invention relates to the field of wind speed prediction, in particular to a mountain wind speed prediction system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As global climate change intensifies, wind energy, as a renewable energy source, plays an increasingly important role in the modern energy structure. In mountainous areas, the characteristics of wind speed are often affected by a variety of factors such as topography, meteorology and environment. Therefore, accurate prediction of mountain wind speed is of great significance for wind energy utilization, climate monitoring and extreme weather warning.

[0004] The undulating terrain and complex meteorological conditions in mountainous areas make the changing characteristics of wind speed completely different from those in flat areas. The microclimate in the mountains often causes obvious heterogeneity in wind speed at different altitudes and directions, which brings great challenges to wind speed prediction. Traditional wind speed prediction models are mostly based on flat data, while ignoring the unique meteorological phenomena in mountains (such as valley wind, slope wind, etc.), resulting in large errors in mountain wind speed prediction.

[0005] In recent years, with the rapid development of sensor technology and big data technology, various data collection methods such as meteorological stations, drones, and satellites have been applied to wind speed monitoring. However, the prediction model of mountain wind speed still faces problems such as sparse data, poor timeliness, and insufficient spatial resolution, which makes it difficult to achieve the expected results in practical applications. At the same time, the existing meteorological prediction models and meteorological monitoring equipment are mainly concentrated in cities or flat areas, and lack pertinence and adaptability to mountain-specific meteorological phenomena.

[0006] With the development of machine learning, deep learning and data mining technology, data-driven methods have gradually become a hot topic in wind speed forecasting research. Through in-depth analysis of historical meteorological data and topographic data, combined with artificial intelligence algorithms, new ideas and methods can be provided for wind speed forecasting. This method can not only improve the accuracy of wind speed forecasting, but also realize real-time monitoring and dynamic updates, providing more accurate and timely wind speed forecasts for mountainous areas.

[0007] The development potential of mountain wind energy is huge. It can not only provide clean energy for local areas and promote economic development, but also mitigate global climate change to a certain extent. However, due to the uncertainty of wind speed in mountainous areas and the high predictability requirements for wind energy, the lack of an accurate prediction system makes the construction and scheduling of wind farms face greater risks. Therefore, it is very necessary to build an efficient mountain wind speed prediction system.

[0008] In summary, establishing a system for predicting wind speed in mountainous areas has important theoretical value and practical significance. The system can comprehensively consider terrain and meteorological factors, use advanced machine learning technology, and continuously optimize the prediction model, aiming to improve the prediction accuracy of mountain wind speed, promote the effective use of mountain wind energy, and achieve sustainable development. Summary of the invention

[0009] The purpose of the present invention is to provide a mountain wind speed prediction system, which can significantly improve the prediction accuracy of wind speed changes under complex terrain conditions by collecting a variety of meteorological data and terrain information and combining advanced machine learning algorithms. High-precision wind speed prediction can effectively help wind farms to rationally allocate resources and optimize electricity production.

[0010] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0011] A mountain wind speed prediction system includes a data acquisition module, a data processing module, a prediction model module and a user interface module. The data acquisition module obtains meteorological data of mountainous areas in real time through a variety of sensors and meteorological stations. The meteorological data includes meteorological parameters such as wind speed, wind direction, temperature, humidity, air pressure, as well as terrain and landform information.

[0012] The data acquisition module also includes mobile devices and drones, etc., which are used to perform three-dimensional monitoring of wind speed at different heights and in different areas.

[0013] The data processing module uses data cleaning and data fusion technology to pre-process the collected data to remove noise and outliers to ensure the accuracy and reliability of the data.

[0014] The prediction model module is based on a machine learning algorithm, combines historical data and real-time data, obtains a model for mountain wind speed prediction through training, and dynamically updates it according to real-time meteorological data to achieve real-time prediction of wind speed.

[0015] The machine learning algorithm may be a random forest, a support vector machine, a neural network, etc., and the optimal prediction model is selected according to different meteorological conditions and requirements.

[0016] The user interface module provides the user with visualized prediction results, including wind speed prediction graphs, trend graphs and warning information, so that the user can understand the changes in mountain wind speed in a timely manner.

[0017] The system also includes a data storage module for storing historical meteorological data and forecast results for subsequent analysis and improvement of the forecast model.

[0018] The output results of the prediction model module can be used in multiple application scenarios such as energy scheduling of wind farms, meteorological services, extreme weather warnings, etc., to improve the utilization efficiency and safety of wind energy.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] 1. Improve wind speed prediction accuracy: The mountain wind speed prediction system can significantly improve the prediction accuracy of wind speed changes under complex terrain conditions by collecting a variety of meteorological data and terrain information and combining advanced machine learning algorithms. High-precision wind speed prediction can effectively help wind farms rationally allocate resources and optimize electricity production.

[0021] 2. Real-time monitoring and dynamic update: The system can monitor the meteorological conditions in mountainous areas in real time and dynamically update the prediction model. This real-time feature makes the wind speed forecast not only accurate, but also able to reflect weather changes in a timely manner, helping users to respond quickly and reduce the risks brought by weather changes.

[0022] 3. Multi-level monitoring capability: Through technologies such as mobile devices and drones, the system achieves three-dimensional wind speed monitoring, covering different altitudes and regions, and can more comprehensively and deeply analyze the complex wind speed change characteristics in the mountains. This multi-level monitoring method ensures the authenticity and reliability of the data.

[0023] 4. Support the development of renewable energy: Accurate wind speed forecasting can promote the effective development and utilization of mountain wind energy resources, reduce the uncertainty of wind power production, thereby improving the overall utilization rate of renewable energy and helping to achieve sustainable development goals.

[0024] 5. Optimize wind power dispatch: Through accurate prediction of wind speed, wind farms can conduct energy dispatch and production planning more scientifically, reduce wind energy waste, and reduce economic losses caused by wind speed fluctuations.

[0025] 6. Enhanced early warning capabilities: The system can monitor extreme weather conditions, such as strong winds or storms, and provide early warnings for possible damage. This will help improve safety in mountainous areas and reduce the impact of natural disasters on human activities.

[0026] 7. Visual data presentation: The system provides a user-friendly interface and displays the forecast results through visualization, helping users to understand the wind speed trend more intuitively. This ease of use enables non-professional users to use relevant data to make decisions, expanding the scope of application of the system.

[0027] 8. Data support and historical analysis: The system's data storage function can not only save historical meteorological data, but also be used for optimization and improvement of subsequent models, promote continuous research and technological progress, and provide rich data support for wind energy utilization and meteorological research. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Attached Figure 1 It is a structural schematic diagram of a mountain wind speed prediction system of the present invention.

[0029] Attached Figure 2 It is a flow chart of a mountain wind speed prediction system in the present invention. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the application equally.

[0031] The present invention is a mountain wind speed prediction system, the main structure of which includes the following modules:

[0032] Data collection module: Use environmental sensors (such as weather stations, drones, etc.) to collect real-time meteorological data such as temperature, humidity, air pressure, wind speed, wind direction, etc. in mountainous areas. Combined with historical meteorological data, data from relevant meteorological databases (such as the Meteorological Bureau, Weather Star, etc.) are obtained through crawler technology or API interfaces.

[0033] Data processing module: Clean and preprocess the collected raw data, including removing outliers, filling missing values, etc. Through data standardization and normalization, data from different sources are converted into a unified format for subsequent analysis.

[0034] Feature extraction module: Use statistical and machine learning methods to extract the characteristic variables required for wind speed prediction from preprocessed data. For example: Time series features: such as weekly, monthly, and seasonal changes. Environmental features: the impact of temperature, humidity, air pressure, etc. on wind speed. Terrain features: mountain slope, wind outlet location, etc.

[0035] Model building module: Select a suitable wind speed prediction model (such as ARIMA, LSTM, random forest, etc.) and perform model training. During the training process, cross-validate historical meteorological data and real-time data and adjust model parameters to improve prediction accuracy. Use the multi-model integration method, that is, weighted average the prediction results of multiple models to improve the robustness and accuracy of the model.

[0036] Prediction and feedback module: Based on the trained model, the wind speed in the future is predicted. A feedback mechanism is established to compare the actual observed wind speed data with the predicted data, and the model parameters are adjusted in time to improve the adaptability of the model.

[0037] User interface module: Provides a user-friendly interface to display wind speed forecast results and related meteorological information. Supports user personalized settings, such as forecast time period, alarm settings, etc. Provides an API interface to facilitate other systems or devices to call forecast data.

[0038] Data storage module: saves historical data and model parameters for subsequent analysis and improvement.

[0039] Data collection: Multiple wind speed sensors, temperature sensors, humidity sensors and air pressure sensors are deployed at different heights and locations in the mountains. These sensors will collect meteorological data in real time.

[0040] Mobile device and drone monitoring: Deploy drones for high-altitude data collection, especially in areas that are difficult to access. Drones will be equipped with meteorological sensors that can acquire data at different altitudes and transmit it to the system server in real time.

[0041] Real-time data transmission: Use wireless communication networks (such as 4G / 5G, LoRaWAN, etc.) to transmit the collected data to the data processing module in real time to ensure the timeliness and accuracy of the data.

[0042] Data processing: Data cleaning: In the data processing module, the collected raw data is first cleaned to remove noise and outliers, and the data is screened using simple statistical methods (such as mean and standard deviation).

[0043] Data fusion: Fusing data from different sensors and devices to form a unified meteorological data set. This can be achieved through data fusion techniques such as Kalman filtering to improve data quality.

[0044] Integration of terrain data: combining location-related geographic information (such as digital elevation models) with meteorological data to form a comprehensive terrain and meteorological database, providing basic data for subsequent prediction models.

[0045] Prediction model: Model selection: According to the characteristics of the data, select a machine learning algorithm suitable for mountain wind speed prediction, such as random forest, support vector machine (SVM) or deep learning model (such as LSTM). The model selection will be based on the test and evaluation results of historical data, and the algorithm with the best prediction effect will be selected.

[0046] Model training: Use a training set containing historical meteorological data and corresponding wind speeds to train the selected model. Optimize model parameters through cross-validation and other means, and evaluate its prediction performance.

[0047] Dynamic update mechanism: The system will continuously receive real-time meteorological data and regularly update model parameters to ensure that the model always reflects current meteorological changes. This can be achieved through incremental learning methods, allowing the model to quickly adapt to environmental changes.

[0048] Visualization: The system provides visualization tools through the user interface module to display wind speed forecast results, including real-time wind speed graphs, forecast trend graphs, and extreme weather warning information. Users can easily view wind speed changes at different locations and times.

[0049] User feedback mechanism: Provides a user feedback interface where users can give their opinions on the prediction results to help improve the actual application effect of the system.

[0050] Data storage: Historical data preservation: All collected meteorological data, wind speed forecast results and model parameters will be stored in the database, which provides important support for subsequent data analysis and model optimization.

[0051] Data analysis function: The system should also provide historical data analysis function to help users understand the long-term trends of mountain wind speed and meteorological changes, and provide reference for wind energy development decisions.

Claims

1. A mountain wind speed prediction system, comprising a data acquisition module, a data processing module, a prediction model module and a user interface module, characterized in that: The data acquisition module acquires meteorological data in mountainous areas in real time through a variety of sensors and weather stations. The meteorological data includes meteorological parameters such as wind speed, wind direction, temperature, humidity, air pressure, and topographic and landform information.

2. A mountain wind speed prediction system according to claim 1, characterized in that: The data acquisition module also includes mobile devices and drones, etc., which are used to perform three-dimensional monitoring of wind speed at different heights and in different areas.

3. A mountain wind speed prediction system according to claim 1 or 2, characterized in that: The data processing module uses data cleaning and data fusion technology to pre-process the collected data to remove noise and outliers to ensure the accuracy and reliability of the data.

4. A mountain wind speed prediction system according to claim 3, characterized in that: The prediction model module is based on a machine learning algorithm, combines historical data and real-time data, obtains a model for mountain wind speed prediction through training, and dynamically updates it according to real-time meteorological data to achieve real-time prediction of wind speed.

5. A mountain wind speed prediction system according to claim 4, characterized in that: The machine learning algorithm may be a random forest, a support vector machine, a neural network, etc., and the optimal prediction model is selected according to different meteorological conditions and requirements.

6. A mountain wind speed prediction system according to claim 5, characterized in that: The user interface module provides the user with visualized prediction results, including wind speed prediction graphs, trend graphs and warning information, so that the user can understand the changes in mountain wind speed in a timely manner.

7. A mountain wind speed prediction system according to claim 6, characterized in that: The system also includes a data storage module for storing historical meteorological data and forecast results for subsequent analysis and improvement of the forecast model.

8. A mountain wind speed prediction system according to claim 7, characterized in that: The output results of the prediction model module can be used in multiple application scenarios such as energy scheduling of wind farms, meteorological services, extreme weather warnings, etc., to improve the utilization efficiency and safety of wind energy.

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