96 results about "Maximal information coefficient" patented technology

The invention relates to the field of new energy, and discloses a wind power generation power prediction method based on space-time diagram convolution and gating attention, and the method comprises the steps: obtaining the geographic position information, meteorological information and historical wind power generation power data of each fan in a wind power plant, and obtaining the normalized data; constructing a dynamic adjacency matrix based on the maximum information coefficient among the historical power data of each fan in the wind power plant, and generating a graph structure; a node set of the graph structure corresponds to each station in the wind power cluster, and an edge set is dynamically determined by a maximum information coefficient of historical power data between the stations; spatial feature extraction is carried out by using a graph convolutional network, and a graph structure learning module is introduced; and inputting the sequence output by the graph structure learning module into a gating circulation unit, introducing an Informer encoder based on a sparse attention mechanism, and generating a wind power prediction result of a future time step. According to the invention, high-precision prediction of the wind power generation power in a multi-fan scene is realized.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses an electric vehicle charging load prediction method, which comprises the following steps: firstly, screening variables with high correlation through a maximum information coefficient, selecting features with high correlation as model input, and taking a charging load as output; reconstructing the data training set by adopting cross validation; a charging load integration model is constructed, the integration model is divided into a base learning device and a meta learning device, the base learning device comprises a DCformer model, a BiTCN model and an artificial neural network ANN model, and a lightweight gradient elevator LightGBM is used as the meta learning device; the parameters of the LightGBM model are optimized by adopting an improved star graffiti optimization algorithm INOA; an error sequence is obtained after a preliminary prediction value is obtained through model prediction, and a final prediction value is obtained by taking LightGBM as an error prediction model. According to the method, data features are effectively extracted by integrating a plurality of single models, the prediction accuracy is improved, and compared with a traditional model, the stability is better, and the generalization ability is higher.

The invention discloses a wind driven generator temperature monitoring method based on a graph space-time attention network, and the method comprises the following steps: carrying out the preprocessing of collected SCADA data, and obtaining a data set; each sensor is used as a node, the top-k neighbor relation of each node is calculated, edge weights among the nodes are calculated through a Gaussian kernel function, a weighted adjacency matrix is constructed based on the neighbor relation and the edge weights of all the nodes, and therefore a multi-sensor time sequence diagram of the wind driven generator is obtained, time sequence characteristics are given to the nodes by time sequence data collected by different timestamps, and a multi-sensor time sequence diagram of the wind driven generator is obtained. Forming a space-time diagram structure, and outputting time sequence characteristics of each sensor; a graph attention network is introduced into wind driven generator temperature state monitoring, and global and connectivity characteristics of a multi-sensor network are modeled. According to the method, the generator temperature prediction precision is remarkably improved by fusing a graph space-time two-dimensional attention mechanism and key variable screening, redundant variables are eliminated by adopting the maximum information coefficient MIC, and noise interference is reduced.

The invention belongs to the technical field of power system load prediction, and particularly relates to a short-term power load prediction method based on a hybrid model, and the method comprises the steps: S1, carrying out the preprocessing of original data; s2, searching an optimal combination [k, alpha] by adopting a firefly and sparrow algorithm; s3, decomposing the original data by adopting variational mode decomposition; s4, determining a demarcation point by adopting an adaptive algorithm, and dividing each signal component; s5, selecting features by adopting the maximum information coefficient, and establishing an input matrix; s6, substituting each signal component into the model for prediction; and S7, reconstructing the prediction result, and outputting a final prediction value. According to the method, a combined prediction model based on feature screening and frequency division prediction is provided, signal components are extracted from original data, the signal components are separated into a high-frequency part and a low-frequency part, after feature screening is conducted on the signal components, the signal components are substituted into the model for prediction, and finally all signal component prediction results are reconstructed. And obtaining a final prediction value.

The invention discloses an SNCR outlet NOx concentration prediction method and system based on a random forest, and the method comprises the steps: S1, obtaining the operation data of an SNCR system, selecting a plurality of input variables affecting the outlet NOx concentration, and carrying out the normalization processing of the input variables; s2, carrying out maximum information coefficient analysis on the normalized data set, determining the delay time of each input variable relative to the NOx concentration of the outlet, and reconstructing the data set according to the delay time; s3, dividing the reconstructed data set into a training set and a test set, constructing a random forest model, and initializing model parameters; s4, optimizing parameters of the random forest model to obtain an optimal parameter combination; and S5, predicting by using the random forest model of the optimal parameter combination to obtain a predicted value of the NOx concentration at the outlet of the SNCR system. The method has the advantages of high prediction precision and the like.

The invention discloses a power system probability load prediction method, system and device, and a storage medium, relates to the technical field of hydrogen energy ship power system load prediction, and aims to solve the technical problems that multi-source data and physical information rules are not integrated and multi-scale chaos in an HPV complex operation environment is not considered in the prior art. The method specifically comprises the steps of collecting and preprocessing multi-source data; screening strong correlation data by using the maximum information coefficient, and reconstructing multi-source data into a unified spatial-temporal characteristic matrix; constructing an electrical and environmental parameter chaos model based on a Lorentz dynamic equation as a physical constraint term of a loss function; a BERT-PINN framework with an attention mechanism is established, and multi-step probability prediction is realized through two-stage training. According to the method, the data driving model and the physical information rule of the multi-source data are effectively integrated, the feature fusion problem of the multi-scale chaotic features is solved, and the model prediction performance is improved.

A photovoltaic power generation associated physical quantity mining method based on historical time series data analysis, and a related apparatus. The method comprises: acquiring historical time series data of multiple dimensions during photovoltaic power generation; calculating degrees of mutual information between the historical time series data of the multiple dimensions of photovoltaic power and the photovoltaic power; selecting the historical time series data of which the degree of mutual information satisfies a set requirement to serve as data related to photovoltaic data; and using a linear discriminant analysis (LDA) method to perform feature dimension reduction on the selected historical time series data to obtain data having undergone dimension reduction processing. In the present invention, by using a maximal information coefficient (MIC) feature selection method, data most related to photovoltaic power generation is selected from original feature variables, and then by using an LDA-based feature dimension reduction method, high-dimensional data is mapped to a lower-dimensional space. By means of the MIC feature selection method and the LDA-based feature dimension reduction method, the accuracy of photovoltaic power generation prediction is effectively improved.

The invention discloses an air conditioning system-oriented multi-control object modeling method, which comprises the following steps of: acquiring multi-source operation data acquired by an air conditioning system sensor, performing principal component analysis and time shift correlation analysis on the multi-source operation data, and screening out a core input variable corresponding to a target control object; the target control object comprises virtual indoor temperature, air source heat pump unit energy consumption, water pump energy consumption and fan energy consumption; according to the time-shifting correlation analysis, the lag step length of each variable is determined through the maximum information coefficient, and key time sequence features are screened; and on the basis of the screened core input variables, data driving models of all the control objects are constructed, and the combined type multi-control-object simulation framework is used for predicting the energy consumption and the environment state of the air conditioning system. According to the invention, the dependence of a test method on an actual system and hardware is overcome, and the test cost and risk are reduced. The source code can be tested before system and hardware configuration, and the problem of frequent equipment control caused by testing in an actual system is avoided.

The invention provides a converter station power supply side wind power and photovoltaic novel power supply output prediction model and method, and belongs to the field of electric power prediction. Data are processed and analyzed through various technologies such as an isolation forest algorithm, autocorrelation, partial autocorrelation analysis, Pearson correlation coefficients and maximum information coefficients. And then, constructing a power generation output prediction model of wind, light and water renewable resources by adopting a bidirectional long-short-term memory network based on an Attention mechanism, and realizing accurate prediction of future new energy power generation output. According to the method, various data processing and prediction technologies are integrated, various factors influencing new energy power generation output are comprehensively analyzed, the prediction accuracy and reliability are improved, and the method plays an important role in promoting optimal scheduling and operation safety of a power grid.

The invention discloses a seismic fault identification method based on Bayesian optimization and an interpretable tree model, and belongs to the technical field of seismic exploration, and the method comprises the following steps: S1, synthesizing seismic data through a convolution model; s2, extracting seismic attributes related to a fault by using the synthesized seismic data obtained in S1; s3, performing feature selection on the seismic attributes by using the maximum information coefficient to obtain a feature subset; s4, constructing an integrated tree learning model of Bayesian optimization, and automatically searching an optimal hyper-parameter combination through a Bayesian optimization algorithm; s5, explaining the optimal integrated tree model; and S6, preprocessing the actual seismic data set, inputting the actual seismic data set into the final model, and obtaining a fault identification result. According to the seismic fault identification method based on Bayesian optimization and the interpretable tree model, the fault identification precision and reliability are improved, the fault identification accuracy is improved, and the specific influence of different types of seismic attributes on the prediction result is explained.

The present invention relates to an intelligent control method for coal slurry water concentration and dosing, which belongs to the field of intelligent control and includes the following steps: (a) collecting various parameters of the sedimentation process of the concentration tank; (b) preprocessing and feature engineering the collected data; (c) analyzing the correlation between the process parameters of the concentration tank and the sedimentation index, and calculating the maximum information coefficient between the variables through the MINE algorithm; (d) constructing a multi-algorithm prediction meta-model, and obtaining the optimal model weight combination through model fusion; (e) according to the real-time process parameters of the concentration tank and the height of the clear water layer and the overflow turbidity at the current moment, on the basis of the prediction meta-model, establishing an optimization model based on a genetic algorithm, and automatically adjusting the dosing frequency of the concentration tank under the premise of meeting the process parameter control index. The advantages of the present invention are: comprehensively considering the control conditions of the history and current period of the concentration tank, according to the model trained by historical data, the automatic control of the dosing system of the concentration tank is realized, and the dosing control operation can be timely and accurate.

The invention discloses a multivariable cross-correlation spatial-temporal feature fusion dissolved oxygen content prediction method and system. The method comprises the following steps: acquiring a water quality sample data set; aiming at the water quality sample data set, screening an influence factor sequence of which the correlation with dissolved oxygen is greater than a preset value by adopting a maximum information coefficient, and screening a dissolved oxygen sequence; standardizing the screened influence factor sequence and the dissolved oxygen sequence to obtain standardized data; the standardized data are adopted to train a dissolved oxygen content prediction model, a trained dissolved oxygen content prediction model is obtained, and the dissolved oxygen content prediction model comprises a spatial feature extraction network, a local feature extraction network, a multi-channel attention network and an encoder-decoder long-short-term memory network combined with an attention mechanism; inputting to-be-detected water quality data into the trained dissolved oxygen content prediction model for prediction, and outputting a dissolved oxygen content prediction result at the next moment. The method can effectively improve the prediction precision of the dissolved oxygen content.

The invention discloses a day-ahead electricity price prediction method and system based on similar day adaptive screening and SHAP compensation. The method mainly comprises the following steps: preprocessing historical electricity price, load, new energy output and meteorological data; calculating the maximum information coefficient of the features and the electricity price based on a sliding window, and carrying out the self-adaptive screening of similar days through combining the clustering and grey correlation degree after dynamic weighting; the method comprises the following steps: constructing a prediction model by fusing a conditional variation auto-encoder with a multi-head attention mechanism, capturing historical sequence multi-scale time sequence characteristics and future condition information through a double-path encoder, and generating a day-ahead electricity price point prediction result; and performing feature contribution decomposition on the prediction error by using an SHAP tool, and training an error compensation model to correct an initial prediction value. The method can dynamically adapt to market changes, the prediction precision and interpretability are improved, closed-loop self-optimization is achieved, and reliable decision support is provided for electricity market transactions.

The invention discloses a method, a system and equipment for predicting the aggregation adjustable capability of a virtual power plant based on a cloud-side cooperation mechanism, and the method comprises the steps: carrying out the correlation analysis and feature reconstruction of input data based on a cloud-side cooperation system network system according to a maximum information coefficient, and carrying out the pre-preprocessing of the data; point prediction is carried out on a future aggregation adjustable capability value through a CNN-BiLSTM-QR model, probability intervals of the adjustable capability under different quantiles are reliably represented through a probability prediction model of quantile regression, and a point prediction result and an interval prediction result of the aggregation adjustable capability are output at the same time. According to the method, the adjustable capability of the demand side resources in the virtual power plant can be accurately predicted, so that the virtual power plant effectively participates in power system scheduling, and the operation efficiency and stability of the system are improved.

The invention belongs to the field of geological disaster prediction, and particularly provides a reservoir landslide displacement prediction method based on dynamic lag recognition and fuzzy entropy optimization, which comprises the following steps: acquiring and preprocessing landslide time sequence monitoring data; combining the distributed lag nonlinear model with the maximum information coefficient, dynamically analyzing the lag relationship between the displacement and the rainfall and reservoir water level through a sliding window, outputting a self-adaptive lag stage and constructing a lag feature set; adaptively decomposing the displacement sequence by using variation mode decomposition of fuzzy entropy optimization, determining the optimal mode number according to the minimum fuzzy entropy, and reconstructing the intrinsic mode function into trend, period and random items; the method comprises the following steps: extracting local features of a multi-lag feature space through CNN, inputting reconstructed displacement components into GRU to capture time dependence, introducing an attention mechanism to weight a key time step, and outputting a predicted value and a confidence interval through quantile regression; according to the method, dynamic lag capture, adaptive decomposition and CNN-GRU-Attention are fused, and high-precision and high-robustness prediction is realized.

The invention discloses an electricity price prediction method based on dynamic mode decomposition fusion LSTM + CKDE. The method comprises the following steps of: 1, collecting data, including historical electricity prices containing time sequence characteristics and price fluctuation indexes, power generation parameters, power market supply and demand data and external environment factor parameters; 2, performing data preprocessing, abnormal value processing and missing value interpolation on the collected data; 3, performing feature input variable screening on the data processed in the step 2 by adopting a maximum information coefficient method or a Pearson correlation coefficient analysis method; 4, performing dynamic modal decomposition on the screened feature input variables to obtain dynamic feature vectors; step 5, constructing an electricity price prediction model by using an LSTM algorithm, and inputting a dynamic feature vector; and 6, evaluating the electricity price prediction model by adopting a CKDE method. According to the invention, the accuracy of electricity price prediction is improved.

The invention discloses a maximum information coefficient-based feature selection method and system for image classification, electronic equipment, a storage medium and a program, and belongs to the technical field of electronic information. The method comprises the following steps: acquiring an image data set to be subjected to image classification; calculating a redundancy matrix and a maximum information coefficient according to the image data set; constructing a target function model based on the redundancy matrix and the maximum information coefficient; performing feature calculation on the image data set by using the target function model to obtain a feature score; sorting the feature scores according to a set condition, and selecting target features to form a feature subset; and training an image classifier by using the feature subset, and inputting a test set of the image data set into the trained image classifier to obtain a classification result. According to the method, the features with high correlation with the target are selected by maximizing the mutual information between the features and the classification information, unnecessary features are reduced by controlling the redundancy between the features, the feature redundancy and the new classification information are balanced, and the picture classification precision is improved.

The invention relates to the technical field of energy system management and optimization, in particular to multi-element load prediction and optimization for a park integrated energy system. A park comprehensive energy system relates to conversion, storage and distribution of various energy types, and complex coupling relations exist among various loads. A traditional load prediction method usually only aims at a single load type and ignores the coupling characteristic between loads, so that the prediction precision is limited. In order to solve the problem, the invention provides a multi-element load joint prediction model and an optimization method for a park integrated energy system. According to the method, a coupling relation between a load and various factors is mined by using a maximum information coefficient (MIC), and combined prediction of the multi-element load is realized based on a time convolution network (TCN). Meanwhile, an improved particle swarm optimization (MPSO) algorithm is introduced to optimize model parameters, so that the prediction precision and the model generalization ability are improved.

The invention belongs to the technical field of data processing, and particularly relates to a new energy generation power proportion prediction method and device in a power system based on xLSTM and XGBoost, and a computer readable storage medium. The method comprises the following steps: collecting new energy power generation data of a power grid, and preprocessing the data; after data preprocessing, feature screening is carried out on the data set through the maximum information coefficient to obtain a preprocessed data set; dividing the preprocessed data set; carrying out dimension reduction processing by utilizing the trained IPCA model; modeling is carried out by using an xLSTM model and an XGBoost model, and prediction is realized; and carrying out weight optimization on the xLSTM model and the XGBoost model by adopting HEOA to obtain a final combined prediction result. According to the method, the problems that new energy power generation proportion prediction is complex and nonlinear, and a linear model is limited in effect and low in precision when the new energy power generation proportion prediction is processed are solved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention relates to the field of intelligent building management, and particularly discloses a carbon emission prediction method and device based on a COA-Elman model. Comprising the steps of obtaining annual historical data of carbon emission related factors of a target building, constructing a carbon emission influence factor index system according to the annual historical data, performing influence factor correlation degree screening based on the carbon emission influence factor index system by using grey correlation analysis and a maximum information coefficient, determining key influence factors, and determining the carbon emission related factors of the target building according to the key influence factors. And based on the key influence factors and the annual historical data, performing optimization training on key parameters in an ELMAN neural network model by using a raccoon algorithm to obtain a carbon emission training model, and based on the key influence factors, the annual historical data and the carbon emission training model, performing carbon emission prediction to obtain a carbon emission prediction result. The objective of the invention is to improve global optimization capability and prediction precision of a network.

The invention relates to a method and a model for predicting the lifetime of a non-tumor peaceful patient based on clinical data. The method comprises the following steps of: collecting and sorting previous hospitalization clinical data of a non-tumor peaceful treatment death patient, and carrying out standardized pretreatment; carrying out attribute selection and reduction on the high-dimensional data from two perspectives of correlation and redundancy by using a maximum information coefficient (MIC); the method comprises the following steps: respectively carrying out attribute weighting by applying a Pearson correlation coefficient and a Spearman rank correlation coefficient; a weighted naive Bayes classification model is used for modeling; according to the method, clinical factors related to the lifetime of the non-tumor Anning treatment patient can be objectively analyzed and predicted, and guidance is provided for Anning clinical admission standard formulation and early and later treatment plans. The model can be realized in the form of a doctor workstation and the like, and the medical work efficiency and accuracy can be greatly improved.

The invention provides an evolution mode recognition method and system based on time sequence tensor segmentation, and the method comprises the steps: collecting three-order time sequence tensor data, and taking the three-order time sequence tensor data as input data; dividing the third-order time sequence tensor into a plurality of tensor segments through a sliding window; extracting a core tensor of the tensor segment based on Tuck decomposition, and performing clustering based on a time sequence weighted clustering method to obtain a coarse-grained segmentation result; optimizing the coarse granularity segmentation result based on information gain to obtain a fine granularity segmentation result; extracting correlation characteristics of each fragment based on the maximum information coefficient; and introducing a neighbor rule, and identifying an evolution mode corresponding to each fragment by adopting an unsupervised three-way clustering algorithm. The invention further constructs an evolution mode recognition system based on time sequence tensor segmentation, a data acquisition part, a time sequence tensor segmentation and evolution mode mining part and a monitoring APP part are connected with one another to form a complete system, and effective recognition of segmentation of the time sequence tensor and the evolution mode is achieved.

The present invention discloses a multi-control object modeling method for an air-conditioning system, comprising: obtaining multi-source operating data collected by air-conditioning system sensors, performing principal component analysis and time-shift correlation analysis on the multi-source operating data, and screening out core input variables corresponding to target control objects; target control objects include virtual indoor temperature, energy consumption of air-source heat pump units, water pump energy consumption, and fan energy consumption; time-shift correlation analysis determines the lag step length of each variable through the maximum information coefficient, and screens out key time series features; based on the screened core input variables, respectively constructs a data-driven model for each control object, and a combined multi-control object simulation framework is used to predict the energy consumption and environmental status of the air-conditioning system. The present invention overcomes the dependence of the test method on the actual system and hardware, and reduces the test cost and risk. The source code can be tested before the system and hardware are configured, avoiding the frequent equipment control problem caused by testing in the actual system.

The invention relates to the technical field of load prediction, in particular to a load prediction method and system combining a maximum information coefficient method and deep learning. The method comprises the following steps: screening main factors influencing the load of the hydraulic turbine set to obtain the maximum information coefficient of each influence factor; taking the preprocessed data as input data of a first model, and determining basic hyper-parameters of the first model according to the input data; optimizing the first model by using an intelligent optimization algorithm to perform feature extraction so as to improve the feature capturing capability; and predicting the load of the hydraulic turbine set, and outputting a load prediction result.

The embodiment of the invention discloses a data processing method and device, a medium and a product, and the method comprises the steps: obtaining a first data set and a second data set, the first data set comprises a plurality of pieces of bad loan data, and the second data set comprises a plurality of preset bad loan index items used for representing bad loans; calculating a maximum information coefficient between each preset bad loan index item and each loan data item, and determining a plurality of first data items in the loan data items according to the maximum information coefficients; constructing a first matrix based on the plurality of first data items, and calculating a feature vector of the first matrix; and calculating a vector distance between a vector corresponding to each first data item in the first matrix and the feature vector of the first matrix, and determining a second data item associated with the preset bad loan index item in the loan data items according to the vector distance. According to the technical scheme provided by the embodiment of the invention, the data items associated with the bad loan indexes can be accurately reserved, and the analysis efficiency and accuracy are improved.

The invention discloses a photovoltaic module performance degradation reason determination method and device, relates to the technical field of new energy, and mainly aims to improve the accuracy of a photovoltaic module performance degradation reason determination result. According to the main technical scheme, the method comprises the steps of obtaining power operation data and infrared imaging data of a photovoltaic module; extracting a first feature vector and a second feature vector from the power operation data and the infrared imaging data respectively; on the basis of a maximum information coefficient algorithm, calculating nonlinear correlation intensity between each electric infrared feature pair in the first feature vector and the second feature vector, and constructing an electric heating correlation matrix according to the nonlinear correlation intensity; based on an interpretable machine learning model, quantifying marginal contribution of each power infrared feature pair in the electrothermal incidence matrix during prediction of performance degradation of the photovoltaic module to obtain contribution values of each feature pair; and according to the contribution value and in combination with a preset attenuation mechanism verification rule, determining a reason for performance attenuation of the photovoltaic module.

The invention discloses a gas insulation cabinet partial discharge monitoring method and device based on edge calculation, and relates to the technical field of gas insulation cabinets. The method comprises the following steps: collecting environmental data and background electromagnetic noise signals of the gas insulation cabinet, calculating an environmental noise correlation coefficient by using a maximum information coefficient method, combining an environmental deviation value to obtain a comprehensive environmental disturbance index, and correcting a preset background noise threshold value to obtain a corrected noise threshold value; if the real-time electric signal amplitude exceeds the corrected noise threshold, marking the data as partial discharge signal data; denoising the partial discharge signal data by adopting an improved ensemble empirical mode decomposition adaptive noise method, and extracting a discharge feature vector; calculating the mahalanobis distance of each feature vector in the discharge feature vector sequence, obtaining the discharge similarity, obtaining a homologous discharge link, extracting the feature data of the homologous link, inputting the feature data into a BP neural network model based on a dung beetle optimization algorithm, outputting the type of partial discharge, and carrying out the early warning, thereby achieving the monitoring of the partial discharge of the gas insulation cabinet.

The embodiment of the invention provides a meteorological data processing method and device and electronic equipment. The method comprises the following steps: acquiring generated power and at least one meteorological factor of N new energy power generation equipment at a historical moment; determining the correlation between each meteorological factor and the generated power according to the symmetry uncertainty; according to the sequence of the correlation from high to low, carrying out descending sorting on the meteorological factors for the first time; sequentially determining a maximum information coefficient value between each meteorological factor after one descending sorting and the generated power, and taking the maximum information coefficient value as a correlation score of the meteorological factors; and according to the correlation score of each meteorological factor and a Markov blanket method, redundant meteorological factors in the meteorological factors are screened out to obtain residual meteorological factors, and the residual meteorological factors are used for predicting the generated power of the new energy power generation equipment. According to the method, the meteorological data is screened, and the screened meteorological data is utilized, so that the prediction accuracy of the new energy power generation power can be improved.