6 results about "Wind power forecasting" patented technology

The present invention relates to a method for predicting wind power generation suitable for complex terrain, comprising: a weather information collection step for collecting weather information including forecasts for wind components predicted for a plurality of eta layers arranged vertically from the ground; a vertical characteristic integration step for deriving additional input characteristics using a characteristic extraction method that considers the physical characteristics of wind components for the wind components predicted for the plurality of eta layers; and a power generation prediction step for predicting power generation by applying the weather information and the additional input characteristics to a machine-learned algorithm for predicting power generation, wherein the algorithm is characterized by being learned using weather information for wind components, additional input characteristics derived using a characteristic extraction method that considers the physical characteristics of wind components for the wind components of the plurality of eta layers, and power generation. The present invention has the effect of improving the accuracy of wind power generation prediction for complex terrain by applying input characteristics derived by applying a characteristic extraction method that considers the physical characteristics of wind components predicted in a plurality of vertically arranged eta layers together with a conventional method for predicting wind power generation using weather information.

This invention discloses a wind power generation prediction method based on a variable-priority adaptive grey model. The method involves collecting annual wind power generation data for a target area and establishing an original sequence; introducing weight parameters and an adaptive variable weight function to construct a variable-priority accumulation operator; calculating a variable-priority accumulation sequence using the original sequence and the variable-priority accumulation operator; introducing the variable-priority accumulation sequence and a double nonlinear term to improve the grey discrete model, thus constructing a variable-priority adaptive grey discrete model; optimizing the nonlinear parameters of the model using a hybrid algorithm of social spider and differential evolution to obtain optimal nonlinear parameter values; and then solving the parameters of the model's time response equation using the least squares method to obtain the optimal model; finally, using the optimal model to predict future wind power generation. The introduction of a variable-priority accumulation operator adaptively adjusts the data priority to reduce the impact of uncertainty in new wind power data, thereby improving the prediction accuracy of wind power generation.

This invention belongs to the field of wind power generation prediction technology and discloses a method for predicting wind power output in complex terrain based on high-precision meteorological and terrain coupling. The method includes: acquiring digital elevation model (DEM) data and global reanalysis meteorological data; constructing a multi-scale nested meteorological simulation system, embedding the DEM into the reanalysis data, enabling large eddy simulation at the innermost layer, and parameterizing the wind turbine in the form of a momentum sink to generate three-dimensional wind field data; extracting static terrain feature vectors; calculating transient air density based on real-time air pressure, temperature, and humidity to correct the theoretical power of the wind turbine; and inputting the three-dimensional wind field data, static terrain feature vectors, and corrected theoretical power into a hybrid neural network prediction model to output predicted active power. This invention solves the problems of low prediction accuracy and lack of multi-physics coupling in wind power generation in complex terrain, significantly improving prediction accuracy and applicable to wind farm planning and operation and maintenance decisions.

This invention relates to the field of wind power generation technology and discloses a wind power generation prediction method, system, electronic device, and storage medium. The method includes: acquiring a high-dimensional feature representation, which is obtained by mapping multi-source heterogeneous spatiotemporal sequence data to a decoupled latent space through a feature variational autoencoder; based on the high-dimensional feature representation, performing physical simulation and data-driven prediction in parallel to generate a power prediction sequence; constructing a digital twin environment for wind farm operation based on real-time wind farm operation data and the power prediction sequence; and in the digital twin environment for wind farm operation, solving the conflicts between multiple optimization objectives through multi-round iterative game to obtain a power generation plan and bidding strategy. This invention achieves autonomous collaborative optimization of the power generation plan and bidding strategy, improving the accuracy, continuity, and collaborative optimization capability of prediction.

The application belongs to the technical field of wind power prediction, and particularly relates to a double-branch space-time wind power prediction method and system based on multi-modal fusion. The method comprises the following steps: S1, performing maximum minimum normalization on input original wind power sequence data to obtain standardized wind power sequence data; S2, performing multi-mode embedding processing on the standardized wind power sequence to obtain time sequence features integrating space-time information and feature correlation; meanwhile, performing mode conversion processing on the standardized wind power sequence to obtain image features; S3, respectively performing time sequence feature extraction and image feature extraction on the time sequence features and the image features obtained in S2 to obtain enhanced time sequence features and enhanced image features; S4, performing multi-modal feature fusion on the enhanced time sequence features and the enhanced image features obtained in S3 to obtain fusion features comprising time sequence modes and image modes; and S5, generating multi-step wind power prediction by using a prediction architecture based on GRU to provide prediction results for multiple future time periods.

The present application relates to the field of short-term wind power generation prediction, and discloses a few-sample wind power prediction method fusing mechanism migration modeling, step 1) wind power generation data preprocessing and dataset division; step 2) MIC characteristic variable screening; step 3) defining characteristic variables and labels on the source domain and the target domain; step 4) establishing a KAN mechanism fusion model on the source domain; step 5) source domain pre-training and physical guided migration to the target domain; step 6) model training, prediction and model performance evaluation on the target domain; the present application introduces a migration strategy of freezing the physical layer, migrates the trained physical network layer in the source domain to the target domain, and only fine-tunes the prediction module, so as to realize wind power modeling under the condition of low samples; the strategy fully retains the physical feature expression ability in the source domain, significantly improves the prediction accuracy and stability under the condition of small sample learning of the target domain, and shows good migration generalization ability.