59 results about "Wind power forecasting" patented technology

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction model construction method based on signal decomposition and parameter optimization, and the method specifically comprises the following steps: S1, original wind power data processing: employing a self-adaptive noise complete set empirical mode decomposition algorithm (CEEMDAN) to decompose the original wind power data; according to the method, a hybrid model fusing a bidirectional gating cycle unit (BiGRU), a bidirectional time convolution network (BiTCN) and a multi-head attention mechanism (MHA) is constructed, an improved parameter optimization algorithm is designed, the capacity of the model for capturing wind power short-term fluctuation characteristics is enhanced, the parameter optimization efficiency is improved, the local optimum problem is effectively avoided, and the method is suitable for the wind power short-term fluctuation characteristic capturing capability. According to the method, the limitation in traditional feature extraction is effectively improved, high-precision and high-efficiency ultra-short-term wind power prediction is realized, a reliable basis is provided for optimizing a power generation scheduling strategy for a power system, and the method can be popularized and applied to multivariate time sequence prediction scenes such as wind speed prediction and photovoltaic power generation prediction.

The invention discloses a method for performing wind power generation prediction through a fine-tuning pre-training large model, and the method comprises the steps: obtaining a historical multi-dimensional time series data set, and carrying out the spatial-temporal feature fusion through a sliding window, and generating a wind speed trend sequence; based on the wind speed gradients of the adjacent time windows, generating step length adjustment parameters by using a normalization function; performing dynamic parameter adjustment on the pre-trained time sequence prediction model based on the step length adjustment parameters, and performing domain adaptation on top network parameters of the time sequence prediction model through an adaptive optimization algorithm in combination with a hierarchical transfer learning strategy to obtain an optimized time sequence prediction model; and processing the real-time multi-dimensional time sequence data set by using the optimized model to generate a wind power generation prediction result. By dynamically adjusting model parameters and a hierarchical transfer learning strategy, the precision of wind power generation prediction and the model adaptation capability are effectively improved, and the method is suitable for time sequence prediction scenes in the field of wind power generation.

The invention provides a multi-dimensional wind power prediction method based on wind speed climbing identification and matching. The method comprises the following steps: step 1, preprocessing wind speed data and power data of a wind power plant; step 2, using reinforcement learning to optimize dynamic window width, adding space-time factors and jointly analyzing and screening pole rate, adding a sudden change event classifier based on physical information to form a dynamic self-adaptive wind speed sudden change recognition algorithm, and using the dynamic self-adaptive wind speed sudden change recognition algorithm to perform de-noising processing on the wind speed data; step 3, carrying out dimension expansion on the meteorological factors, and matching wind speed data with previous historical wind speeds by adopting a wind speed time period matching algorithm; and step 4, based on multi-dimensional data, fusing an Informer algorithm with an attention mechanism, and predicting future wind power generation power. The method can effectively guide the network to pay attention to the historical power response mode, the wind speed structure difference and the meteorological factors, and provides the structure sensing capability for subsequent prediction.

The invention discloses a water electrolysis hydrogen production control method and device based on wind and light power generation and electronic equipment. The method comprises the following steps: acquiring the operation cost, the electrolytic cell efficiency and the power grid interaction power variation of the wind-solar hybrid power generation hydrogen production system in a first sampling interval; constructing a target function based on the operation cost, the electrolytic cell efficiency and the power grid interaction power variation; determining the wind power generation predicted power of a wind power generation system in the wind-solar hybrid power generation hydrogen production system in the prediction time period and the photovoltaic power generation predicted power of a photovoltaic power generation system in the prediction time period according to a second sampling interval; and based on the wind power generation predicted power and the photovoltaic power generation predicted power, determining a water electrolysis hydrogen production control strategy (including an electrolytic cell power instruction value and an energy storage compensation power instruction value) by taking the minimum function value of the target function as a target. According to the invention, the technical problems of unbalanced power supply and demand and unstable power grid interaction in the wind-solar hybrid power generation hydrogen production system in the prior art are solved.

Disclosed are an ultra-short-term wind power forecasting method and system, relating to the technical field of artificial intelligence. The method comprises: obtaining an original dataset of a wind farm, processing the original dataset, and performing training on the basis of processed original data; decomposing wind speed data in the trained original data, calculating each decomposition component, and constructing a feature matrix on the basis of the calculation results; and introducing a residual attention mechanism to reconstruct the feature matrix, using the reconstructed result to establish a network model, performing secondary training, and forecasting ultra-short-term wind power. The present invention improves the accuracy and reliability of ultra-short-term wind power forecasting and achieves significant advances in algorithm optimization, thereby providing effective support for the stable power supply of renewable energy sources such as wind farms and for power grid operation.

The invention provides a short-term wind power cluster power prediction method based on error dynamic correction, and relates to the technical field of wind power generation prediction and control. The method comprises the following steps: calculating a convergence distance between wind power plants, and dynamically dividing a wind power cluster into a plurality of sub-clusters by adopting a clustering algorithm; performing power prediction on the wind power plants in the sub-clusters by using a graph dynamic attention network (GDAN) of a self-learning graph structure; in a low-power large-fluctuation period, introducing an error dynamic correction coefficient and performing multi-scale decomposition, and correcting a preliminary power prediction result by using a superposition result of correction coefficient components predicted by the GDAN; and combining prediction results of the sub-clusters, and outputting an overall power prediction value of the wind power cluster. Through dynamic clustering, graph structure self-learning and multi-feature decomposition correction strategies, the prediction precision, especially the robustness and flexibility under extreme working conditions, is significantly improved, and the method is suitable for 5-minute-level ultra-short-term prediction requirements of various scenes such as mountainous regions, plains and large-scale wind power clusters.

The invention relates to the technical field of wind power prediction, in particular to a long-term wind power prediction method, device and equipment based on improved Informer and a storage medium, and the method comprises the steps: processing lacking wind power data through employing a nearest neighbor interpolation method and a similar day substitution method, carrying out the correlation analysis of the wind power data, selecting out features which have obvious influences on the wind power, and carrying out the prediction of the wind power. Finally, making the data into a training set and a test set; establishing a wind power prediction model of the wind power plant, and combining a CNN model with an Informer model; selecting a proper loss function and a solver, and training the built wind power prediction model of the wind power plant; and inputting the historical data corresponding to the to-be-predicted time period into the trained wind power prediction model of the wind power plant, and outputting the corresponding predicted wind power of the wind power plant. According to the method, the prediction accuracy is improved, the number of models and the training time are reduced, the convergence speed of the models is increased, and the method has extremely high engineering application value and practical significance.

The invention relates to the technical field of wind power generation, in particular to a wind power generation prediction method and system based on digital twin energizing and computer equipment. Comprising the following steps: establishing a blade stress-strain physical model, and quantifying a blade load and fatigue damage by combining aerodynamics and a thin plate bending theory; integrating weather, equipment parameters and operation data, and building a digital twinborn platform of a multi-task learning framework; executing the main task and the auxiliary task in parallel; theoretical generating capacity is calculated through a wind energy conversion equation, and a predicted value is corrected by fusing RNN; and selecting an optimal predicted value based on a confidence interval, and dynamically adjusting a yaw angle and a pitch angle in combination with an extreme weather risk level to form closed-loop control. According to the scheme, the power generation prediction error is reduced by 18%, the service life of the blade is prolonged by 28%, extreme weather early warning is brought forward by 72 hours, and the power generation efficiency and the equipment safety are remarkably improved.

The invention discloses a graph neural network wind power generation power prediction method based on tree structure guidance. In order to solve the problem that an existing deep learning model neglects interaction among input variables in a wind power generation prediction task and lacks interpretability, a tree structure is constructed by utilizing a hierarchical relationship of the variables in a wind power generation process, and a tree structure model binarization matrix and an adjacent matrix are combined; obtaining a graph adjacency matrix based on tree structure guidance; inputting the input variable and the tree structure guided graph adjacency matrix into a graph neural network model to predict the wind power generation power; through a key sub-graph extraction module in the graph neural network model, variables which play a key role in predicting the wind power generation power and the connection relation between the variables can be obtained. And quantizing the contribution degree of each process variable to wind power generation power prediction through anti-fact explanation. According to the method, the problems of insufficient reliability and poor interpretability of a prediction model in wind power generation power prediction are effectively solved.

The present invention discloses a high-precision correction method and system for wind power forecasting of electric power systems, which belongs to the field of new energy forecasting. The method is first based on the WRF model and ARW dynamic solver, and selects a three-layer nested structure and a physical process parameterization scheme with the wind farm location as the regional center, and combines static terrain and global weather forecast data to achieve high-resolution short-term wind speed forecasting; then, by modifying the boundary layer parameters and introducing data assimilation technology to form a variety of differentiated schemes, multiple downscaled forecast results within the same time period are obtained; then, based on the forecast results and the wind speed observation data of the station at the corresponding time, a variety of different types of improved artificial intelligence models are used to realize the correction of the forecast wind speed; finally, a variety of improved models are combined with adaptive weighting to obtain the final wind speed forecast result. By fully mining the information of terrain, observation data and global weather forecast data, the accuracy of wind speed forecast data is effectively improved.

The invention relates to the technical field of new energy power prediction, in particular to an extreme weather offshore wind power generation situation prediction method based on artificial intelligence, and the method comprises the steps: obtaining data under historical extreme weather, dividing the data into a training set and an optimization set, removing noise, extracting environment data, and carrying out the feature data fusion. The method comprises the following steps: determining a freezing proportion according to an extreme weather disaster grade, freezing partial layer parameters of a pre-trained conventional power generation prediction model, training an unfrozen layer, constructing an extreme weather power generation prediction model, periodically obtaining data in an optimization set through constructing a simulation time axis, carrying out automatic learning, and finally obtaining environmental data in real time for prediction. And judging the prediction accuracy according to the similarity between the prediction result and the optimization set data, and if the prediction accuracy is not accurate, analyzing an abnormal reason and correcting related parameters. The method provided by the invention effectively overcomes the difficulty of inaccurate offshore wind power generation prediction in extreme weather, and significantly improves the accuracy and reliability of wind power generation prediction under extreme weather conditions.

The invention provides a wind power short-term prediction method and a wind power short-term prediction device based on a fusion model. The problem of low prediction precision of wind power short-term output power is explored by combining various time-space characteristics influencing the wind power output power. A multi-feature extraction and fusion module for coping with high fluctuation and uncertainty of wind energy is specially designed by focusing on the problems of data discontinuity and irregular distribution caused by wind energy intermittency and instability, equipment failure or network communication congestion in wind power short-term prediction and insufficient multivariable coupling relation modeling. According to the module, important influence factors are dynamically screened, time features and space features are synchronously extracted, a complementary coupling relation of multi-source space-time features is deeply mined, a combined time feature matrix and a space global feature matrix are constructed, and the feature representation capability is remarkably improved. By scaling a spatio-temporal feature fusion mechanism, the system integrates comprehensive features influencing wind energy output, and finally high-precision wind power short-term prediction is achieved through a prediction module.

The invention relates to the technical field of energy storage coordination control, in particular to an energy storage coordination control method and system for wind power generation. The method comprises the following steps: acquiring historical power generation states and environmental data, and drawing a power generation power fluctuation state curve to obtain a fluctuation state filling curve; then, on the basis of a historical environment state, multivariable regression analysis and generated power fluctuation data association are utilized to obtain multivariable power fluctuation associated data, and power generation behavior learning induction is carried out to form generated power incremental behavior data; and finally, constructing a wind power generation prediction model by using the generated power increasing behavior learning data, predicting the future generating capacity based on the model, formulating an energy storage adaptive coordination control strategy, and sending the strategy to a control terminal to execute energy storage coordination control. According to the invention, the energy storage coordination control technology is optimized, so that the energy storage coordination control technology is more accurate.

The invention relates to a data and physics dual-drive microgrid wind power prediction method and a data and physics dual-drive microgrid wind power prediction system. Comprising the steps that a meteorological sensitive factor feature set is optimized through Pearson correlation coefficient method features, an optimal meteorological sensitive factor time sequence is reconstructed through a PINNs model with embedded Weibull constraints, a hyper-parameter space of an LSTM model is optimized, the optimized meteorological factor feature set is integrated into an improved deep learning model, and a wind power short-term prediction result is obtained. According to the method, data and physical driving technologies are combined, the uncertainty and randomness of the wind speed at different places and time can be effectively captured, the meteorological data quality is improved, and therefore the wind power prediction precision is improved.

The invention discloses a wind power prediction covariable offset adaptive method based on a conditional energy model. According to the method, the wind power generation power prediction model is constructed by utilizing the gated cycle unit network, and offline training of the wind power generation power prediction model is completed by adopting a sample weighting mechanism, so that the robustness of the wind power generation power prediction model to distribution change is enhanced. A conditional de-noising score matching strategy is adopted to learn the distribution difference of data in a training stage and a prediction stage through a conditional energy model, and a sample weight used for measuring the covariable offset degree is obtained based on the model. Data samples flowing in real time are stored in a replay buffer area, incremental learning is carried out on a condition energy model and a wind power generation prediction model through an online updating mechanism, and the prediction performance is kept stable. The method can effectively improve the power prediction precision and operation scheduling capability of the wind power plant under complex meteorological conditions, and has good engineering practical value and deployment flexibility.

The invention discloses a regulation and control method and device for hydrogen production and methanol production and electronic equipment, and relates to the technical field of energy system regulation and control, and the method comprises the steps: firstly, obtaining weather forecast data in a preset time period, carrying out wind power generation prediction according to the weather forecast data, and obtaining a wind power prediction result; then, a reward function and a penalty function corresponding to each production link are constructed, and a comprehensive score corresponding to each production link is obtained based on the reward function and the penalty function; and finally, based on the wind power prediction result and the comprehensive score corresponding to each production link, performing wind power distribution and production scheduling for each production link. By means of the method, the reward function and the penalty function are designed, the fluctuation situation occurring in the production process can be effectively dealt with, the production process is adjusted in advance, high risks possibly existing in a short time when the demand list is currently met are actively avoided, the production smoothness is optimized, and the production stability and continuity are improved.

The invention provides a data identification and true value compensation method for new energy prediction, and the method comprises the following steps: S1, inputting photovoltaic power generation data or wind power generation data; s2, setting main independent variables and dependent variables; s3, identifying and removing missing values and abnormal values to obtain data 1; s4, if the data is used for regression prediction, outputting the data 1 to carry out regression prediction; if the data is used for time sequence prediction, missing values and abnormal values adopt a neural network truth value compensation mode. For the problem of data distortion during photovoltaic or wind power generation prediction, a data identification method based on a segmented two-direction quartering method is provided for regression prediction; on the basis of regression prediction data, an abnormal data identification and truth value compensation method based on a neural network is provided for a time sequence prediction problem, and the method can improve the availability of data and improve the prediction precision of new energy.

This paper discloses a single-variable ultra-short-term wind power forecasting method based on two-level trend decomposition. The method performs multiple trend decompositions on historical wind power time series data to generate macro-trend components, meso-scale trend components, and residual components. The decomposed macro-trend components and meso-scale trend components are each initialized with causal convolution kernels using exponential distributions to extract multi-scale trend features, and adaptive weights with scale normalization are used to maintain temporal causality. A cyclic reconstructed attention mechanism is used to enhance residual modeling, and dynamic features of the residual components are obtained through sequence concatenation and double residual connections. Each component is linearly processed and the results are fused to generate an ultra-short-term wind power forecast value. This method can achieve accurate ultra-short-term wind power forecasting.

The invention discloses a day-ahead wind power prediction method under the combination of physics and data driving, and relates to the field of wind power prediction. The problem that the existing single-mode wind power prediction method has limitation and low prediction accuracy when coping with multi-time scale wind power prediction is solved. According to the method, a final prediction result is obtained through a physical driving mode and a data driving mode, the final prediction result is mainly composed of two parts, namely a first part (1-5-hour prediction) and a second part (6-24-hour prediction), a data driving result is directly adopted as a predicted value of wind power, the physical driving prediction result and the data driving prediction result are fused, and the prediction result of the wind power is obtained. The two parts are combined to obtain the wind power sequence of the target electric field in the next 24 hours in the combination mode so as to improve the accuracy of wind power prediction. The method is mainly used for predicting the day-ahead wind power of the wind power plant.

The present invention discloses a wind power prediction method based on fluctuation sequence classification and correction, comprising: 1. using a benchmark model to predict the wind power benchmark value within the next N hours; 2. using a feature clustering method to divide the power fluctuation process, and exploring the correlation between meteorological forecast errors and model generalization errors under different fluctuation sequences from the perspective of output power; 3. using a CNN-LSTM time series model 1 to deduce power changes in future time periods for small fluctuation sequences with smooth output; and for non-small fluctuation sequences, combining a CNN-LSTM time series model 2 and a back-propagation neural network to interactively correct double-layer errors; and 4. recombining the benchmark power correction results according to the time series as the final wind power output. The present invention adopts a composite method combining time series analysis and feature learning to extract features from multiple dimensions to correct errors, and conforms to the actual error distribution law to ensure the model has good accuracy.

The invention discloses an energy storage intelligent coordination control system and method for wind power generation, relates to the field of wind power generation, solves the problem of low prediction precision of wind power generation, and comprises a data acquisition module, a model construction module, a wind power analysis module and an energy storage regulation and control module. The data acquisition module is used for acquiring structural data and historical power generation data of a wind driven generator, sending the structural data to the model construction module and the wind power analysis module, and sending the historical power generation data to the wind power analysis module; the model construction module is used for constructing a generator power model and sending the generator power model to the wind power analysis module; the wind power analysis module is used for calculating predicted generating capacity of a wind driven generator according to a generator dynamic model and sending the predicted generating capacity to the energy storage regulation and control module; and the energy storage regulation and control module is used for carrying out intelligent regulation and control according to the predicted generating capacity in the future to obtain a charging and discharging regulation and control result of the target power grid.

The invention relates to the technical field of wind power prediction, and particularly discloses a gradient synergy and double alignment fused small sample wind power prediction method, which comprises the following steps: designing a Fourier enhanced Transform shared feature extractor to extract general feature representation with periodic perception from wind power time sequence data of a multi-source domain and a target domain; then, constructing a hybrid domain adaptive module, and realizing implicit and explicit dual alignment of feature distribution of a source domain and a target domain through a plurality of adversarial domain classifiers arranged in parallel and multi-core maximum mean difference measurement; and finally, introducing a gradient projection algorithm, carrying out collaborative optimization on conflict gradients of the prediction task and the domain adaptation task in a back propagation process, and eliminating gradient conflicts in multi-task learning. The method effectively improves the feature extraction capability, domain adaptability and optimization stability of the model in a small sample scene, and remarkably improves the prediction precision and robustness in a cross-domain wind power prediction task.

The invention discloses a wind power generation predicted power correction method and device influenced by extreme weather, equipment and a medium, and the method comprises the steps: obtaining the current environment data of a place where a target wind power generator is located, inputting the current environment data into an environment recognition training network, and obtaining an environment recognition result; if the environment identification result simultaneously comprises an extreme weather identification result and an extreme weather identification type, determining the operation deterioration probability of the target wind driven generator; according to the extreme weather identification type, the operation power of the target wind driven generator is corrected for the first time, and first correction power is obtained; and according to the operation deterioration probability and the first correction power, carrying out second correction on the operation power of the target wind driven generator, and taking the second correction power as the target prediction power of the target wind driven generator under the current environment data. The invention belongs to the field of wind power generation power prediction. Wind power generation prediction under the influence of extreme weather can be realized.

The invention relates to the technical field of wind and light power generation prediction, in particular to a solar photovoltaic and wind power generation prediction method based on DTCN-FFT and a related device. Comprising the following steps: acquiring photovoltaic and wind power generation data of solar energy, and preprocessing the photovoltaic and wind power generation data of the solar energy to obtain processed data; spearman correlation analysis is carried out on the processed data, and input characteristics with high correlation with the generating capacity are screened out; inputting the input features into a pre-constructed DTCN-FFT model, and outputting prediction results of solar photovoltaic power generation and wind power generation; according to the method, frequency domain information and time domain information are fused, original time sequence data are converted into a frequency domain through fast Fourier transform, global features such as periodic components, trend terms and high-frequency noise are extracted, and the defect that a traditional model is insufficient in long-range dependence capture is overcome.

The invention provides a short-term wind power prediction method based on GWO-VMD-FE and TCN-BiGRU. The short-term wind power prediction method is used for improving the accuracy and prediction efficiency of short-term wind power prediction. The method comprises the following steps: firstly, carrying out automatic optimization on a penalty factor alpha and a modal decomposition number K of VMD by utilizing GWO, and decomposing wind power data into an intrinsic mode function (IMF) component through the VMD; secondly, calculating a fuzzy entropy value of the IMF component by adopting an FE algorithm, reconstructing the IMF component into a trend component and a random component according to the entropy value, fusing the trend component and the random component with an optimal feature screened by a Spearman correlation coefficient to obtain a trend sample and a random sample, taking the trend sample and the random sample as input of a TCN-BiGRU model for prediction, and superposing prediction results of the two samples to obtain a TCN-BiGRU model; and obtaining a wind power prediction result. According to the method, the time sequence characteristics and the front-and-back dependency relationship of the data can be more sensitively captured, and the accuracy of wind power prediction can be effectively improved.

The present invention relates to the field of wind power generation prediction and control technology, and discloses a relative humidity-based wind turbine icing and grid-trip risk warning method and system. The method comprises: obtaining weather forecast information for each forecast time point; for any forecast time point where the forecast temperature is less than zero degrees, substituting the forecast humidity information into a first calculation model fitted according to historical data to obtain visibility at the forecast time point; then substituting the solved visibility into a second calculation model fitted according to historical data to obtain liquid water content at the forecast time point; substituting the calculated liquid water content into a third calculation model fitted according to historical data to obtain the maximum number of wind turbine blades in the wind farm corresponding to the forecast time point that are iced and grid-tripped; and simultaneously substituting the calculated liquid water content into a fourth calculation model fitted according to historical data to obtain the expected number of wind turbines in the wind farm corresponding to the forecast time point that are iced and grid-tripped.

The application discloses a method for fine-tuning a pre-trained large model for wind power generation prediction, which comprises: obtaining a historical multi-dimensional time series dataset, generating a wind speed trend sequence through space-time feature fusion by a sliding window; generating a step adjustment parameter by a normalization function based on the wind speed gradient of adjacent time windows; dynamically adjusting the pre-trained time series prediction model based on the step adjustment parameter, combining a hierarchical transfer learning strategy, and adjusting the top network parameters of the time series prediction model through an adaptive optimization algorithm to obtain an optimized time series prediction model; and processing real-time multi-dimensional time series data sets by using the optimized model to generate wind power prediction results. Through dynamic adjustment of model parameters and the hierarchical transfer learning strategy, the accuracy and model adaptation capability of wind power prediction are effectively improved, and the method is suitable for time series prediction scenarios in the field of wind power generation.

The application discloses a kind of with self-attention mechanism and gate image-based wind power short-term prediction method and system, belong to wind power generation prediction technical field, wherein, the method includes: obtaining NWP meteorological data and SCADA fan data is preprocessed, and the data after preprocessing is divided into training set and test set;Training set is decomposed into continuous multiple historical time subsequences, and is reconstructed into two-dimensional image;Residual-based deep convolutional neural network is established, and gate convolutional neural network layer and self-attention mechanism are added, to obtain residual-based deep convolutional neural network with self-attention mechanism and gate;Two-dimensional image is input into the neural network training;Test set is input into the neural network trained and is predicted, to obtain short-term wind power.The method comprehensively uses historical time series data and meteorological grid data, extracts features and converts into images, uses advanced image processing technology to predict wind, greatly improves the prediction accuracy.

The invention belongs to the technical field of energy prediction, and particularly relates to a long historical data-oriented periodic sensing wind power prediction method, which comprises the following steps of: obtaining wind power generation historical data; determining a data cycle value based on autocorrelation function analysis, and performing fragmentation processing and sequence decomposition on historical data according to a cycle length to obtain a seasonal component and a trend component; performing multi-scale feature enhancement on the fragmented data, and extracting rich time sequence dynamic information; and based on the enhanced fragment representation, outputting a plurality of fragment prediction results through a prediction model, and performing weighted fusion according to cosine similarity to obtain a final wind power generation power prediction result. And carrying out optimization training on the prediction model by adopting a time-frequency domain difference loss function. According to the method, periodic information and time sequence characteristics in the long historical window can be effectively utilized, the accuracy and calculation efficiency of wind power generation power prediction are remarkably improved, and reliable support is provided for power grid dispatching and energy management.

This invention provides a short-term wind power forecasting method based on dynamic time-series fusion of multi-source heterogeneous data. The method includes: reading and preprocessing multi-source data to extract time-series features; applying an adversarial hash generator to construct a unified feature representation space, performing perturbation-immune training and projection diversity regularization; integrating the physical laws of the wind power system and data distribution characteristics into an enhanced hash code, performing constrained projection and distribution calibration, constructing a heterogeneous relationship graph based on the processed hash code, and correcting it using historical data with similar conditions; and applying GCN to the dynamic fused relationship graph to perform short-term wind power forecasting. This method significantly improves forecast accuracy and robustness, particularly reducing forecast error by approximately 50% under extreme weather conditions.