10 results about "Wind forecast" patented technology

Computer implemented systems and methods for providing an uncertainty platform for an energy grid controller that (1) receives risk inputs including (a) one or more load forecasts, (b) one or more wind forecasts, (c) one or more solar forecasts, (d) one or more generator availability risk forecasts, (e) one or more generator fail-to-start or fail-to-run predictions, (f) one or more net scheduled interchange forecasts, and / or (g) one or more transmission congestion forecasts; (2) determines net uncertainty for a predetermined time period based on the risk inputs; and (3) provides dynamic risk outputs including forecast scenarios, reserve requirements and / or reserve margin thresholds based on the determination of net uncertainty.

The invention relates to the technical field of weather forecast, and particularly discloses a method for forecasting the strong wind intensity grade of an intelligent grid by using a convection potential index, which comprises the following steps of: firstly, acquiring a numerical mode product with a specified range and resolution, extracting five elements, namely a lifting index, 1000m vertical wind shear, a temperature difference between 500hPa and 850hPa, a multilayer vertical speed and convection effective potential energy, and calculating the wind intensity grade of the intelligent grid by using the convection potential index; the quartile value, the abnormal value and the like of each element are respectively calculated, and a standardized value is obtained by combining the correlation with the convection strong wind. The standardized values of all the elements are summed to obtain a convection gale potential index, then the gale intensity is calculated by fusing the maximum wind speed and the maximum wind speed, and after potential index correction is conducted, the grade of the gale intensity is determined by adopting a dual-threshold grading method in combination with rainfall elimination and control. According to the method, the instantaneous and continuous strength of the strong wind is considered, the convection potential effect is fused, strong wind forecasting skills of different time periods and the accurate forecasting advance of the local strong wind are improved, and the TS score of the forecasting result is obviously improved compared with an EC numerical mode on the whole, especially for high-grade strong wind forecasting.

The invention discloses a thunderstorm gale short-time nowcasting method based on an ENTwo evolution network and Uform network hybrid architecture, and the method comprises the steps: obtaining historical live data, which comprises a radar echo sample data set, a maximum wind speed data set and a lightning data set; generating a grid thunderstorm gale label data set based on the gale wind speed and the lightning data; an ENTwo evolution network and a Uform hybrid model are constructed, the evolution network performs space-time extrapolation on the input data to generate a forecast field, and the Uform fuses an extrapolation result and historical data to output a thunderstorm and gale forecast field; training the model through a loss function; and inputting real-time data into the training model, and outputting a lattice forecasting field. According to the method, the accuracy and timeliness of 0-2-hour thunderstorm gale forecasting are effectively improved, the spatial resolution reaches 1 kilometer, the strong echo and extreme wind speed forecasting performance is superior to that of a traditional method, and high-precision technical support is provided for disaster prevention and reduction.

The invention provides a deep learning-based severe convection strong wind short-term and imminent forecasting method and device, and the method comprises the steps: inputting historical data into an encoder, obtaining an encoding result of the historical data outputted by the encoder, and enabling the historical data to comprise historical radar echo data and historical automatic station data; inputting the coding result into a MaxViT module to obtain an intermediate feature of the historical data output by the MaxViT module; inputting the coding result and the intermediate feature into a feature fusion module to obtain a fusion feature of the historical data output by the feature fusion module; the fusion feature is input into a decoder, a decoding result of the fusion feature output by the decoder is obtained, and the encoder and the decoder comprise a Swin Transformer module; and according to the decoding result, obtaining a short temporary gale forecast result. According to the invention, the precision and efficiency of short-term and temporary strong wind forecasting are improved.

This application provides a radar echo extrapolation and thunderstorm / strong wind data processing method, apparatus, and electronic device based on optical flow, relating to the field of meteorological forecasting. The method includes: acquiring radar base data of a target area over a continuous historical period to generate a corresponding radar reflectivity factor image sequence; inputting the preprocessed radar reflectivity factor image into an optical flow calculation model to obtain a dense optical flow field; calculating the vorticity field and divergence field based on the dense optical flow field; stitching and fusing the radar reflectivity factor image, optical flow field, vorticity field, and divergence field at the same historical moment to form a multi-feature fused spatiotemporal sequence data; inputting the spatiotemporal sequence data into a pre-trained spatiotemporal sequence prediction model to output prediction results for a future preset time period; and generating and outputting a short-term thunderstorm / strong wind forecast product. This method can more accurately capture the nonlinear evolution of the micro-scale structure of thunderstorm / strong wind systems, significantly improving the accuracy of extrapolation forecasts.

The invention discloses a method and device for determining a drag coefficient of terrain turbulence drag force, equipment and a medium, and relates to the field of weather forecast. Comprising the following steps: determining the surface roughness as a first dependence parameter of wind shear, and determining the Monin-Obchhoff length as a second dependence parameter of wind shear; determining the current value of the first proportion according to the current parameter value of the first dependency parameter and the current parameter value of the second dependency parameter; determining a current drag coefficient based on the drag coefficient constant, the current value of the first proportion and an exponential function associated with the first proportion; the current drag coefficient is used for determining the current terrain turbulence drag force, and low-altitude wind forecasting is carried out based on the current terrain turbulence drag force, so that the low-altitude wind forecasting accuracy of the numerical weather forecasting mode is improved by considering the effect of the boundary layer stability on the terrain turbulence drag force.

The application discloses a terrain turbulent drag force drag coefficient determination method and device, equipment and medium, and relates to the meteorological forecast field. Including: the ground roughness is determined as the first dependent parameter of the wind shear, and the Monin-Obukhov length is determined as the second dependent parameter of the wind shear; according to the current parameter value of the first dependent parameter and the current parameter value of the second dependent parameter, the current value of the first proportion is determined; based on the drag coefficient constant, the current value of the first proportion and the exponential function associated with the first proportion, the current drag coefficient is determined; the current drag coefficient is used to determine the current terrain turbulent drag force, so as to carry out low-altitude wind forecast based on the current terrain turbulent drag force, thereby improving the accuracy of the numerical weather prediction model for low-altitude wind forecast by considering the effect of the boundary layer stability on the terrain turbulent drag force.

The invention provides a plateau canyon gale forecasting method based on a WSTD-KOA-BPNN technology. The method comprises the following steps: acquiring historical wind speed data and other meteorological element data such as temperature of a plateau canyon target area by using a distributed wind speed sensor; missing data points are filled through a cubic spline interpolation method, noise filtering is carried out through a wavelet soft threshold denoising algorithm (WSTD), and normalization processing is carried out on the denoised wind speed data through a Min-Max method; parameters of a back propagation neural network (BPNN) are optimized based on a Kepler optimization algorithm (KOA), and a KOA-BPNN prediction model is constructed; and real-time monitoring and forecast result output. According to the method, WSTD noise suppression, KOA global parameter optimization and BPNN nonlinear modeling capability are fused, so that the capture precision and generalization performance of the plateau canyon complex gale features are remarkably improved, and reliable technical support is provided for disaster prevention and reduction.

The application discloses a thunderstorm gale prediction method and system based on centralized sampling and multi-branch fusion, and equipment, relates to the meteorological prediction technical field, and includes: adopting centralized sampling to intercept the radar echo sequence of a prediction area; performing multi-branch feature extraction on the radar echo sequence to obtain double-dimension splicing features, and performing branch feature interaction on the double-dimension splicing features to obtain global fusion features; extracting physical constraint features of the prediction area, and constructing physical-global fusion features based on the physical constraint features and the global fusion features; performing coordinate regression and logistic regression calculation based on the physical-global fusion features to obtain the thunderstorm gale occurrence probability of the thunderstorm gale drop point in a short term. The application is used to solve the problem that the thunderstorm gale drop area and the thunderstorm gale occurrence probability in a short term cannot be accurately predicted.

The application relates to the technical field of power system safety protection, and discloses a strong wind prediction method, system, device and medium for a power grid transmission channel, global meteorological features and local topographic features are respectively constructed based on meteorological prediction data and geographic information data of the power grid transmission channel, the obtained two features are input into a composite neural network model constructed based on a convolutional neural network and FiLM modulation for processing, and a wind field change prediction value of the power grid transmission channel under a first spatial resolution is output; based on real-time wind speed data of the power grid transmission channel, an initial moment wind field of the power grid transmission channel under a second spatial resolution is determined by using a double-source distance weighted interpolation method based on terrain correction; a change amount at a prediction moment and a wind value at an initial moment are subjected to vector composition to form a strong wind prediction result; and a km-level spatial resolution, 1-24 hour short-time refined strong wind prediction for a transmission channel in a complex terrain area is realized.