375 results about "Numerical weather forecast" patented technology

A double-path ultra-short-term wind power prediction method based on numerical weather forecast and multi-order time sequence dynamic gating fusion comprises the following steps: acquiring wind power generation historical data and numerical weather forecast data of a wind power plant, and screening weather factors highly related to wind power by using an MIC; the CEEMDAN is adopted to decompose the power sequence into a plurality of intrinsic mode functions (IMF); a dual-path prediction architecture is constructed, one path adopts xLSTM to predict an intrinsic mode function (IMF), all subsequences are superposed, and a prediction result is obtained; in the other path, the XGBoost is combined with key meteorological characteristics of an intrinsic mode function (IMF) and a numerical weather forecast (NWP) for prediction, and all the subsequences are superposed to obtain a prediction result; the method comprises the following steps: designing an MT-DGFusion module through an enhanced attention and dynamic gating network; and fusing the dual-path prediction results through an MT-DGFusion module to obtain a final prediction result. According to the method, double breakthrough of prediction precision and stability is realized, and a new technical path is provided for a complex time sequence prediction task.

The invention discloses a risk scheduling method for a water-wind-solar complementary system, and the method comprises the steps: collecting historical data and power grid topological parameters, accessing global and regional numerical weather forecast data, employing a coupling model, fusing meteorological grid data with a historical power station output sequence, and generating hourly reservoir incoming water amount and wind-solar power probability prediction results. According to the predicted time sequence and the generated scene, calculating the scene probability based on the generated scene; according to the prediction time sequence, using a quantification method to obtain a peak regulation risk quantification value; calculating power grid power flow distribution and critical clearing time according to the generation scene and the power grid topological parameters, and calculating a system stability margin; a multi-target optimization model is constructed according to a peak regulation risk quantized value after splitting and a system stability margin, the system stability margin is introduced as an optimization target, the overall stability of the system is improved, a meteorological-hydrological-output three-mode feature mapping method is provided, and meteorological feature extraction of a key grid region is enhanced through an attention mechanism.

The invention discloses a photovoltaic power generation power prediction method and system based on a large language model. The method comprises the following steps: converting historical power data and numerical weather forecast data into time sequence embedded representation; through cross-modal semantic alignment, semantic embedding representation is generated; constructing a natural language prompt containing task context information, encoding the natural language prompt into prompt embedding, combining prompt embedding with semantic embedding representation to form a fusion input sequence, inputting the fusion input sequence into a pre-trained large language model, and outputting implicit features; synchronously generating an initial power prediction result and a weather prediction result obtained by correcting the numerical weather prediction data through a parallel collaborative prediction mechanism; and taking the meteorological prediction result as a correction signal, performing joint optimization on the preliminary power prediction result, and outputting a power generation power prediction value. According to the method, the problem of deep fusion of heterogeneous data is effectively solved, and the prediction accuracy is improved.

The invention discloses a variational deviation correction method and device, and relates to the field of weather forecast, and the method comprises the steps: obtaining the observation data of a current time window and an updated deviation correction coefficient of a previous time window; calculating a satellite observation deviation correction amount based on the deviation correction coefficient, the observation information and the mode background field, calculating a deviation between simulation observation and actual observation based on an observation operator, and carrying out correction and quality control on the deviation; according to the satellite observation data after quality control, on the basis of the improved target functional and the gradient thereof, in the minimization iteration process, under the joint constraint of the observation data and a numerical weather forecast mode, a deviation correction coefficient is updated online; wherein the improvement process of the target functional comprises the steps that a deviation correction coefficient is used as a control variable to be combined with a mode state variable to construct a new target functional, and variable transformation is carried out in an increment form. The assimilation analysis quality of observation data is improved, and the numerical weather forecast level is improved.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention relates to a runoff hydropower station generating capacity prediction method based on depth data fusion, and the method comprises the following steps: S1, carrying out data layer fusion, integrating multi-source hydro meteorological data, obtaining multi-source data from a meteorological station, a hydrological station, a satellite remote sensing platform and a numerical weather forecasting system, carrying out the cleaning, interpolation and normalization processing of the data, and carrying out the prediction of the generating capacity of a runoff hydropower station; performing data space-time alignment; s2, carrying out feature layer fusion, firstly inputting a multi-source data set of a unified space-time reference, carrying out feature extraction, carrying out feature fusion based on a self-attention mechanism, embedding a water balance equation in a deep learning model, and obtaining a comprehensive feature vector; and S3, performing prediction, inputting the comprehensive feature vector in the step S2, and performing power generation prediction by using a deep neural network.

The invention belongs to the technical field of wind power prediction, and particularly relates to a wind power prediction method and system based on multi-source data fusion. According to the method, a cascade prediction architecture composed of a wind speed prediction model and a power prediction model is constructed, and the wind speed prediction module extracts cross-variable association features of multi-dimensional meteorological parameters from numerical weather forecast data by adopting a variable-level attention mechanism and a patch-level attention mechanism; capturing a long and short time dependence mode from the actually measured historical operation data; the power prediction model decomposes a predicted wind speed sequence through one-dimensional average pooling, and constructs a nonlinear power mapping model. Besides, an attention fusion module is provided, a learnable global variable is introduced as an information exchange bridge, information interaction between meteorological data and actually measured data is realized, and the collaborative utilization efficiency of multi-source data is effectively improved. Experiments show that the method is superior to an existing prediction model based on deep learning.

The invention relates to the field of photovoltaic power prediction, in particular to a photovoltaic power prediction method, and the method comprises the steps: obtaining the spectral response function data of different photovoltaic modules in a photovoltaic power station, and measuring the spectral irradiance distribution of the surface of each photovoltaic module in real time; according to the spectral response function data and the spectral irradiance distribution, calculating the effective spectral irradiance of each component type in a grouping manner according to the component types; counting the temperature coefficient of the batch to which each photovoltaic module belongs according to historical data, and establishing a module-level temperature-power correction factor; according to the string topological relation, string-level temperature correction power is obtained, and the string-level temperature correction power, the effective spectral irradiance and the global irradiance data of the numerical weather forecast are output into a corrected power station-level photovoltaic power predicted value; the problem of low photovoltaic power prediction precision caused by inaccurate modeling due to parameter difference and temperature influence of photovoltaic modules in photovoltaic power station power prediction is solved.

The invention provides an HY-2A ocean water vapor inversion method based on machine learning, and the method comprises the steps: constructing a high-quality HY-2A scanning microwave radiometer data set through the data preprocessing, matching, feature extraction and normalization processing of HY-2A satellite scanning microwave radiometer data and ERA5 reanalysis data. A plurality of machine learning models are used for training and testing, Bayesian optimization is used for realizing hyper-parameter automatic adjustment, an optimal solution in finite time is obtained, an SHAP method is used for explaining the models, contribution of each characteristic variable is clear, interpretability of the used machine learning models is improved, and finally high-precision inversion of the water vapor over the sea is realized. The method has efficient calculation performance, can realize rapid processing of large-scale marine meteorological data, and improves the interpretability of a machine learning model. The method has important application value in numerical weather forecast, ocean extreme weather forecast, climate monitoring and other geophysical and meteorological fields.

The invention discloses a new energy weather forecast method based on numerical weather forecast and AI revision, and particularly relates to the technical field of weather forecast, and the method comprises the steps: obtaining the historical actually measured weather data of a new energy power station, correcting the numerical weather forecast data of multiple sources through employing an artificial intelligence algorithm, and building a correction model; inputting the current numerical weather forecast data into the correction model for correction, extracting the internal structure characteristics of the correction data in combination with a high-dimensional geometric mapping and manifold learning method, and evaluating the matching degree between the manifold distribution of the correction data and the historical data; analyzing and evaluating the data complexity by using multi-scale entropy, and ensuring that the data meets a preset standard; on the basis of matching degree and complexity evaluation, selecting suitable numerical weather forecast data for new energy short-term and medium-term meteorological parameter forecast; the method improves the prediction accuracy and applicability, and is suitable for the generation power prediction and optimization scheduling of the new energy power station.

The invention discloses a real-time calculation method and system for distributed photovoltaic power generation meteorological elements, and solves the technical problem of regional distributed photovoltaic power generation meteorological information loss. The method comprises the following steps: selecting adjacent sites based on power station operation data, and repairing target site data by using adjacent site data; constructing an irradiance calculation model by using the reconstructed power data, and establishing a model among numerical weather forecast, distributed photovoltaic station power, irradiance and environment temperature; and based on the established model, calculating the solar irradiance and temperature of the position of the distributed photovoltaic station by taking the real-time power and the current value of the numerical weather forecast as input. In order to complement the power of the distributed photovoltaic station, a meteorological information calculation model is established, a high-cost-performance and high-precision solution is provided for complementing the meteorological information of distributed photovoltaic power generation, and the accuracy of output power prediction of the distributed photovoltaic station is improved.

The invention discloses a high-precision distributed photovoltaic output prediction method and system based on micrometeorology and feature hierarchical clustering, and relates to the technical field of distributed photovoltaic output prediction.The method comprises the steps that numerical weather forecast data are collected, an initial micrometeorological field is generated through space-time alignment and self-adaptive KNN interpolation, and the initial micrometeorological field is subjected to feature clustering; a WRF-LES system and a bidirectional LSTM are combined to establish cross-scale mapping, a dynamic residual correction field is fused to generate hectometer-level high-resolution micrometeorological data, and the problem of insufficient resolution of traditional numerical forecasting is solved. MIC and PA-DTW are used for jointly analyzing the characteristics of the power station, and dynamic clustering is achieved through a sliding time window and incremental spectral clustering. According to the method, a physical information graph network and causal expansion convolution are coupled to extract features, federal learning cross-power-station cooperative training is combined, the distributed photovoltaic output prediction precision and robustness are improved, and privacy security is considered.

The invention discloses a wind power prediction method based on a pre-training model and low-rank self-adaption, and relates to the technical field of wind power generation, and the method comprises the steps: obtaining historical meteorological data and numerical values of a wind power plant, obtaining weather forecast data, and obtaining historical operation data and numerical weather forecast data of a wind motor; cleaning and normalizing the acquired data, dividing historical meteorological data and historical operation data into a training set and a test set, selecting a pre-training model according to a wind power prediction demand, and training the pre-training model by using the training set. According to the method, the pre-training model is combined with low-rank self-adaption, so that the model can accurately capture the change rule of wind power, prediction errors are reduced, prediction precision is improved, low-rank decomposition is carried out on a model parameter matrix, the parameter quantity of model training is reduced, calculation complexity is reduced, rapid training and real-time prediction of the model are facilitated, and the method is suitable for large-scale popularization and application. The stability of the power system of the wind power plant is improved.

The invention discloses a drainage basin intelligent flood control scheduling method and system based on digital twinborn, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting static data and dynamic data of a drainage basin, building a hydrological and hydrodynamic coupling model based on the static data and the dynamic data, and forming a drainage basin digital twinborn body; inputting the received numerical weather forecast into the digital twin of the watershed for simulation, generating a plurality of flood routing scenes in a future time period, and calculating a dynamic flood risk probability graph; the method comprises the following steps: constructing a simulation training environment by using historical flood data and a high-precision drainage basin digital twinborn body, carrying out offline training on a scheduling strategy network in the simulation training environment based on a reinforcement learning algorithm, and outputting a scheduling instruction according to a real-time drainage basin state to complete training of the scheduling strategy network. According to the method, the core problem that the traditional method is insufficient in decision timeliness and weak in adaptive capacity in an uncertain environment is effectively solved.

The invention relates to the technical field of wind resource assessment and wind power plant optimization, and discloses a multi-source information fusion measurement system combining laser radar data and wind resource assessment, and the system comprises a data collection module which is used for collecting laser radar data, synthetic aperture radar data, numerical weather forecast data and ground weather station data; the data preprocessing module is used for carrying out noise filtering, time alignment, space alignment and data calibration on the collected data; the information fusion module is used for dynamically fusing the preprocessed multi-source data and outputting high-precision wind speed and turbulence characteristic distribution; and the wind field modeling and prediction module is used for constructing a dynamic three-dimensional wind field based on the fusion data and performing short-time wind speed prediction. Through multi-source data deep fusion, dynamic wind field modeling and intelligent optimization modules, the comprehensive effects of high-precision wind field evaluation, nonlinear wind speed prediction and fan layout global optimization are achieved.

The invention discloses a fog boundary layer parameterization scheme correction method based on a multi-scale physical coupling network, relates to the crossing field of numerical weather forecast and artificial intelligence, and aims to improve the performance of different boundary layer parameterization schemes in a WRF mode, construct a fusion framework of a physical mode and deep learning, adopt a double-branch heterogeneous network structure, and improve the performance of a fog boundary layer parameterization scheme. Establishing a nonlinear mapping relation between different parameterization scheme deviations and large eddy simulation turbulence characteristics by combining a space-time attention mechanism; by correcting systematic deviation existing in a parameterization scheme, the analysis capability of a turbulence structure is improved, and physical description of a boundary layer process is optimized; compared with a traditional boundary layer scheme, the method has the advantages that the fog zone simulation precision is remarkably improved, the fog zone visibility, the liquid water content and the root-mean-square error of a liquid water path are reduced by 66.7%, 50% and 58.3% respectively, the problem that turbulence intermittent characterization is insufficient under a stable boundary layer in a traditional method is effectively solved, and a new normal form is provided for refined forecasting of the fog generation and elimination process.

The invention relates to the field of numerical weather forecast and deep learning, and discloses a numerical forecast wind field data error correction method and system based on a neural network, a medium and equipment. A data set obtained after data preprocessing is divided into a training set, a verification set and a test set, and space-time matching and normalization are conducted on the data set; establishing a many-to-many variable mapping relationship in a horizontal space by using the training set, expanding a time dimension on the basis of the established multiple mapping relationship, and establishing a 3D U-Net-based deep learning model; performing training and parameter tuning on a 3D U-Net-based deep learning model by using the training set and the verification set, and constructing a deep learning correction model of multi-element multi-time forecast; and correcting the test set by using the trained deep learning correction model. According to the method, the problem of multi-forecast aging and multivariable collaborative correction can be solved.

The invention relates to the technical field of weather forecast, in particular to a weather downscaling method fusing numerical weather forecast and AI weather forecast, which comprises the following steps: acquiring numerical weather forecast data and satellite remote sensing observation data, and performing unified preprocessing, interpolation and mapping to generate refined forecast data and observation reference data. Then, local compensation based on a deep neural network and model-independent element learning are adopted to carry out regional adaptive compensation, a joint objective function is constructed to implement variational assimilation and joint optimization, and meanwhile, a generative adversarial network and a graph neural network are combined with reinforcement learning to realize error compensation and node-level adaptive correction; and finally, continuously updating parameters through closed-loop feedback iteration to generate updated forecast data. According to the method, the problems of insufficient local details and poor model adaptability in the downscaling process in the prior art are effectively solved, and the forecasting precision and robustness are improved.

The invention provides a new energy station energy storage control system and method, and the system comprises a new energy output high-precision prediction module which is used for fusing local laser radar and numerical weather forecast data, and outputting an output predicted value; the SOC curve initialization module is used for initializing an energy storage SOC reference curve based on the output predicted value and the day-ahead market electricity price information; the real-time node electricity price prediction module is used for predicting a real-time electricity price trend; and the rolling optimization control module of the energy storage system is connected with other modules, and dynamically optimizes the charging and discharging instruction and the SOC state according to the output prediction value, the energy storage SOC reference curve and the electricity price prediction value of the real-time node electricity price prediction module. According to the invention, by fusing the multi-source prediction result and the real-time market information, on the premise of satisfying various operation and market constraint conditions, a highly adaptive energy storage dynamic optimization regulation and control mechanism is constructed, and key support is provided for economical operation of a new energy station in a spot market environment.

The invention relates to the technical field of photovoltaic prediction, in particular to a distributed photovoltaic power prediction method and system based on high-dimensional gridding numerical weather forecast, and the method comprises the steps: carrying out the standardization of the numerical weather forecast data and photovoltaic power historical data of a target region, and achieving the time-space alignment based on a preset grid, generating a gridding data set; utilizing convolution processing to extract local space features, and converting and fusing the local space features into a feature sequence containing space and historical time sequence information at the same time; modeling is carried out through an encoder-decoder architecture, an encoder excavates historical power dependence, and a decoder dynamically couples future meteorological characteristics with historical power through an attention mechanism and outputs a grid-level predicted value; aggregating to obtain a system total power prediction result; by establishing a unified space-time grid, refined alignment of data is realized, cross-space-time dynamic fusion is performed in combination with convolution and an attention mechanism, and prediction precision and stability can be kept in complex weather.

The invention belongs to the technical field of wind power prediction, and discloses an ultra-short-term wind power prediction method based on Bayesian optimization XGboost-LSTM, and the method comprises the following specific steps: 1, inputting wind power data collected by a wind power plant and numerical weather prediction data of a corresponding time sequence; 2, data preprocessing, wherein missing value interpolation and data deduplication are carried out on input data; according to the method, an XGBoost feature optimization method is used for selecting key features influencing the wind power, the influence of irrelevant feature noise on the model prediction precision and accuracy is eliminated, then a Bayesian optimization algorithm is used for carrying out hyper-parameter tuning on an LSTM model, and finally an XGBoost-LSTM-BO model is constructed. XGboost feature optimization and Bayesian hyper-parameter optimization can obviously improve the prediction effect of the LSTM on future data and improve the model prediction precision, and compared with a traditional prediction model, the wind power data generalization ability of the model can be improved while the high prediction precision is kept, and higher prediction performance is achieved.

The invention relates to a cold region wind-solar power generation power multi-scale fusion prediction method, and relates to the cold region wind-solar power generation power prediction technology field, and the method comprises the steps: based on actually-measured wind-solar power generation base generator set wind-solar input and power output data under different wind speeds, wind directions and illumination intensities, different regions and different time scales; carrying out characteristic analysis on the time shifting property, volatility and complementarity of the generated power of the wind-solar power generation base generator set, and constructing a power characteristic database; correcting a numerical weather forecast wind speed / irradiance error by using time sequence transmission, and establishing a wind-solar power generation power prediction model; according to the wind-solar power generation power and load combined prediction based on deep learning, regional combination is performed on wind-solar power generation power prediction models in different regions for multi-scale fusion prediction, and the data quality and the analysis accuracy are remarkably improved. In a data acquisition link, reasonable acquisition frequency setting and comprehensive collection of various data provide abundant information sources for subsequent analysis.

The embodiment of the invention discloses a photovoltaic power prediction method and device, electronic equipment and a storage medium. The method comprises the steps that original photovoltaic power data and original weather forecast data are acquired; performing data preprocessing on the original photovoltaic power data and the weather forecast data to obtain photovoltaic power data and numerical weather forecast data; screening a meteorological element set with high correlation with the photovoltaic power from the numerical weather forecast data; and inputting the photovoltaic power data, the numerical weather forecast data and the meteorological element set into a pre-constructed SCBAM-TCN-UNet combined model, and outputting to obtain a photovoltaic power prediction result. According to the method, the precision and robustness of photovoltaic short-term power prediction can be improved, and efficient and reliable prediction support is provided for power grid dispatching and operation plans.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention discloses a multi-drive process multi-factor agricultural non-point source pollution prediction method based on coupling meteorological numerical forecasting. The method comprises the following steps: firstly, introducing numerical weather forecast data, and constructing a high-precision weather driving field with kilometer-level space and hour-level time resolution by combining WRF dynamic downscaling, DEM terrain correction and conservation resampling; secondly, establishing a multi-drive process coupling model system which comprises a meteorological drive layer, a hydrological response layer, a pollutant migration layer and a crop feedback layer and is used for simulating runoff production, sediment erosion, nitrogen and phosphorus migration and transformation and crop transpiration and root nutrient absorption processes; thirdly, performing precision verification on a simulation result by using observation data, and identifying key meteorological and hydrological factors through an error transfer matrix and a sensitivity analysis method; and finally, realizing parameter adaptive correction by adopting a long short-term memory network, finishing parameter optimization in combination with a multi-target genetic algorithm, packaging the model chain through a containerization technology, and realizing cross-platform deployment and visual output of a pollution load result. The method can be used for agricultural non-point source pollution prediction and management.

The invention relates to a self-adaptive control method for a rain and snow mode of rail transit, electronic equipment and a medium, and the method comprises the steps: comprehensively studying and judging a rain condition based on multi-sensor data and numerical weather forecast data, and calculating a weather grade in the rain and snow mode, the multi-sensor data including video detection data, laser radar data and rainfall data; whether a rain and snow mode is set or not is judged according to the weather level, if yes, the current track slip degree is evaluated according to the multi-sensor data, and the GEBR value is adaptively updated based on the slip degree; the CC calculates the running safety braking envelope of the train by using the updated GEBR value, and feeds back a slip detection result and a dynamic parameter adjustment result in real time; and the ATS dynamically adjusts the operation plan of the train according to the slip detection result reported by the CC, and optimizes the operation scheduling of the whole train. Compared with the prior art, the method has the advantages that self-adaptive management of the rain and snow mode is achieved based on the dynamically-adjusted GEBR, and the running safety and reliability of the train in the rain and snow weather are effectively improved.

The invention provides a multi-scale photovoltaic power prediction method and system fusing numerical weather forecast and a physical information neural network, relates to the technical field of photovoltaic power prediction, and realizes rolling prediction of future meteorological parameters and horizontal radiation intensity through numerical weather forecast. The authenticity and the stability of the meteorological data are effectively improved by combining quantile mapping and cascade correction of the time sequence mode attention neural network; and through combined modeling of a physical information neural network and a time sequence characteristic network, meteorological and radiation data subjected to multi-level correction and conversion are deeply fused with a photovoltaic system physical law, and high-precision prediction of photovoltaic power is realized. The whole prediction process has automatic anomaly elimination and deletion complementation capabilities, the prediction robustness under complex meteorological conditions and extreme environments is enhanced, and the sequential response and physical consistency of photovoltaic power are optimized, so that the engineering applicability and intelligent level of the system are greatly improved.

The invention discloses an intra-day photovoltaic power rolling prediction method considering residual correction. The method comprises the following steps: firstly, acquiring numerical weather forecast of a target photovoltaic site in a prediction window and actual output of a photovoltaic site in a corresponding historical window as input; an intra-day photovoltaic power rolling prediction model based on the hybrid neural network is constructed, future numerical weather forecast in a prediction window and photovoltaic power station output in a historical window are used as input in each prediction, and future predicted photovoltaic power station power is output; and taking the residual error between the predicted value and the actual value as a prediction target, inputting the preliminarily predicted power of the photovoltaic power station into the residual error correction model, outputting a predicted residual error value, and combining and adding the predicted residual error value and the preliminarily predicted value to obtain a photovoltaic power prediction result considering residual error correction. According to the prediction model considering residual error correction, the accuracy of intra-day photovoltaic power rolling prediction can be improved, and the requirement of photovoltaic prediction is met.

The invention belongs to the technical field of meteorological forecast element correction, and provides a rainfall forecast correction method based on a U-CS-Net model in order to solve the technical problems that data features of an existing U-Net model are not fully utilized and the nonlinear relation of meteorological elements cannot be fully reflected, the rainfall forecast correction method based on the U-CS-Net model comprises the steps that 1, a data set is constructed; step 2, adding a channel attention mechanism (CAM) for learning importance of different time points in rainfall forecast in a jump connection part between an encoder and a decoder of the U-Net model, adding a space attention mechanism (SAM) for calculating correlation between space positions to generate attention weight, and adding a multi-layer perceptron (MLP) for enhancing representation capability of features; 3, training the U-CS-Net model to obtain a regional precipitation correction model; and related characteristics among multiple elements and multiple time phases are fully extracted, and correction of WRF numerical weather forecast mode data is realized.

The RIME-RF spatial downscaling-based photovoltaic output prediction method comprises the steps of collecting photovoltaic power data and local meteorological observation LMD data of a photovoltaic power station in a target area, extracting common data of numerical weather forecast NWP data and the local meteorological observation LMD data, and constructing an input feature set; the method comprises the following steps: optimizing hyper-parameters of a random forest (RF) algorithm based on a frost ice optimization (RIME) algorithm, constructing an RIME-RF model, and performing spatial downscaling on numerical weather forecast NWP data; a VMD-CNN-GRU-SE attention mechanism photovoltaic power prediction model optimized based on BKA is adopted, original numerical weather forecast NWP data is combined with photovoltaic power data to train the prediction model, and numerical weather forecast NWP data after spatial downscaling is combined with the photovoltaic power data to train the prediction model. According to the prediction method, changes of fine meteorological factors influencing the photovoltaic power can be more accurately captured, downscaling errors are remarkably reduced, and short-term power prediction precision is improved.