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45results about How to "Prediction results are stable" patented technology
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Bus load prediction method
ActiveCN106485262AHigh precisionMeet real-time requirementsCharacter and pattern recognitionEnsemble learningData dredgingDecision taking
The invention discloses a bus load prediction method. The method comprises the following steps: correcting abnormal values in historical load data by use of a transverse comparison method, and determining key influence factors of bus load by use of a grey relation projection method; putting load curves with similar features in the same category by use of an improved K-Means clustering method to get a plurality of typical load patterns, building a random forest classification model, and establishing the mapping relationship between influence factors and clustering results; for each load pattern, training a plurality of prediction models by use of a multivariate linear regression method; and determining the category of a day under test, and selecting a matching regression model to realize load prediction. A data mining method is introduced to analyze the changing rules of bus load, and a prediction model library is built. Model matching is realized based on the category of a day under test. The accuracy and real-time performance of short-term bus load prediction are improved. More accurate decision support is provided for power grid planning and real-time dispatching.
Owner:JINCHENG POWER SUPPLY COMPANY OF STATE GRID SHANXI ELECTRIC POWER +3
Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters
InactiveCN102693451AReal-time monitoring of desulfurization efficiencyThe method is scientific and reasonableDispersed particle separationBiological neural network modelsPredictive methodsSlurry
The invention relates to a method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters. The method is characterized by comprising the following steps of: selecting four different artificial intelligent computation models and taking parameters acquired in an ammonia process desulphurization system operational process such as multiple groups of flue gas amounts, flow of a circulating pump, the flow of a concentration pump, the ammonia concentration, the concentration of absorption liquid, the liquid-gas ratio, the inlet flue gas temperature, the ammonia consumption, the density of spraying slurry, the pH value of slurry of a spraying tower and the pH value of the slurry of a pre-washing tower as input variables of the four models; respectively training each model, and establishing a non-linear function relationship between four desulphurization parameters and the desulphurization efficiency; then respectively transmitting parameters monitored in real time into the trained artificial intelligent model, and predicting the desulphurization efficiency; and taking the average value of two predicted values in the middle as a final predicted value... The method disclosed by the invention can be used for better predicting the ammonia process desulphurization efficiency and has the characteristics of higher stability and stronger prediction capability compared with single model prediction.
Owner:NORTHEAST DIANLI UNIVERSITY
Machine learning model training method, device and system, equipment and storage medium
PendingCN112257873AEvenly distributedPrediction results are stableMachine learningMachine learningEngineering
The invention provides a machine learning model training method, device and system, electronic equipment and a computer readable storage medium. The method comprises the steps that service party equipment sends a machine learning model to multiple pieces of training participant equipment, so that the multiple pieces of training participant equipment independently train the machine learning model based on training samples stored in the multiple pieces of training participant equipment, wherein the first loss function used by the training participant equipment for training the machine learning model is used for balancing the distribution of parameters of the machine learning model after the training participant equipment is trained; receiving training results returned by the plurality of training participant devices; aggregating the training results returned by the plurality of training participant devices to obtain a global machine learning model; and training the global machine learning model according to the training sample stored in the service side device. According to the method and the device, the prediction stability of distributed learning can be realized on the basis of realizing intensive utilization of resources for training the machine learning model.
Owner:WEBANK (CHINA)
Multi-parameter mine spraying heat exchange efficiency calculation method-based spraying system
The invention relates to the field of mine spraying heat exchange and specifically relates to a multi-parameter mine spraying heat exchange efficiency calculation method-based spraying system. Four different artificial intelligence calculation models are selected; multiple groups of parameters of flue gas quantities, circulating pump flow quantities, concentration, liquid-gas ratios, inlet flue gas temperature, spray slurry density, post-spray temperature and the like collected in spraying heat exchange system operation processes are used as input variables of the four models. Each model is subjected to training operation, and four nonlinear function relations between spraying parameters and heat exchange efficiency are established. Parameters that are monitored in real time are respectively transmitted to the trained artificial intelligence models, and predictions on spraying efficiency are made. The average value of two predicted values positioned at the middle of results is used as a final prediction value, the number of spray headers to be opened and a spray flow quantity are determined according to implementation data calculated via a control device, and the heat exchange efficiency can be well monitored via use of the method; compared with single model prediction, the method is characterized by high stability, strong monitoring capability and the like.
Owner:SHANDONG XINHE ENERGY SAVING TECH CO LTD
Short-term electrical load on-line predicting method based on self-adaptation enhancing algorithm
InactiveCN103793887AEliminate the effect of forecast accuracyPrediction is accurateImage enhancementGenetic modelsLoad forecastingPredictive methods
The invention discloses a short-term electrical load on-line predicting method based on the self-adaptation enhancing algorithm in the technical field of electrical load predicting. The short-term electrical load on-line predicting method comprises the step of selecting M factors affecting meteorological data and extracting actual measurement values of factors affecting the meteorological data in past L days to form a meteorological data matrix SL*M, the step of extracting electrical load data of n time points of each day in the past L days to form an electrical load data matrix DL*n, the step of selecting m factors with maximum association with the electrical load data from the factors affecting the meteorological data, serving the m factors as valid constituents and forming a valid meteorological data matrix TL*m according to the actual measurement values of the valid constituents of the past L days, the step of solving a short-term electrical load predicting model according to the valid meteorological data matrix TL*m and the electrical load data matrix DL*n, and the step of carrying out electrical load prediction according to the short-term electrical load predicting model. According to the short-term electrical load on-line predicting method, the effect on the model predicting precision of data noise can be effectively eliminated, and a more accurate and stable predicting result can be obtained.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Lithium battery life estimation method based on IMM-UPF
InactiveCN109932656ADecrease in accuracy dependenceImprove efficiencyElectrical testingUltrasound attenuationEstimation methods
The invention discloses a lithium battery life estimation method based on IMM-UPF. The method uses a new fusion model interactive multi-model for fusion calculation of different attenuation models. Inconsideration that Kalman filtering may cause a large error because of the non-Gaussian and nonlinear attenuation trend of the lithium battery, the method filters each model by using unscented particle filtering, thereby on the one hand solving the lack of particles in particle filtering during resampling, and on the other hand, achieving a more accurate prediction result than the Kalman filtering. The provided IMM-UPF method is verified by the comparison of simulation results and experimental data. The results show that the method can improve the accuracy of lithium battery life prediction.
Owner:HEFEI UNIV OF TECH
Neural network architecture search method based on convolution kernel prediction
InactiveCN111723914AGuaranteed stabilityPrediction results are stableNeural architecturesNeural learning methodsEngineeringData mining
The invention provides a neural network architecture search method based on convolution kernel prediction. The method comprises the steps of: constructing a super network for neural network architecture search, wherein the super network comprises a teacher network and a student network, the teacher network is a pre-trained network, and the student network is composed of a plurality of basic units;training the student network by taking a training set and the coding information of a neural network architecture as input, and predicting to obtain an optimal convolution kernel when a loss functionconverges to a minimum value, wherein the loss function is determined according to the loss function of the teacher network and the loss function of the student network; and updating the coding information of the neural network architecture according to the loss function of the student network to obtain an optimal neural network architecture. According to the neural network architecture search method based on convolution kernel prediction, the teacher network is introduced as guidance, and a convolution kernel prediction module in the student network can accurately predict the optimal convolution kernel, so that the search efficiency is greatly improved, and the global optimum of the search result can be ensured.
Owner:SUN YAT SEN UNIV
Radar-aircraft system adversarial simulation confidence evaluation method
ActiveCN112285664ASimple inputImprove versatilityWave based measurement systemsNeural architecturesFeature vectorFlight vehicle
The invention discloses a radar aircraft system adversarial simulation confidence evaluation method. The method comprises the following steps of automatically encoding index vectors of subsystems of two complex and diverse adversarial parties into low-dimensional feature vectors with fixed dimensions by using a self-encoding model, simplifying index input of a flight target, and inputting the index vectors into a confidence prediction model in a stable form; and based on an attention mechanism of a deep network, guiding training of a confidence evaluation model, using a small amount of radar-aircraft system confrontation actual measurement data as a verification reference, and predicting the confidence of a simulation confrontation system according to the similarity degree of a simulationsystem and an actual measurement system. Therefore, the correlation and influence coefficients generated by the indexes of the input subsystems in the confrontation process are indirectly learned, a weighting matrix does not need to be set manually, and the objectivity of simulation confidence evaluation is improved.
Owner:NANJING UNIV OF INFORMATION SCI & TECH +1
Slope displacement fractal forecasting method improved by grey theory
InactiveCN104102853AImprove forecast accuracyImprove predictive abilitySpecial data processing applicationsSmall sampleBusiness forecasting
The invention discloses a slope displacement fractal forecasting method improved by a grey theory and relates to the high slope deformation forecasting field of strip mine. The slope displacement fractal forecasting method improved by the grey theory includes that using a grey model (1, 1) to carry out fitting prediction on a fractal dimension sequence according to a fractal theory and a grey forecasting theory to obtain fractal dimensions of next few periods, and using an inversion extrapolation method to inversely calculate the forecast fractal dimensions to obtain a precise slope deformation forecast value. The slope displacement fractal forecasting method improved by the grey theory is suitable for forecasting the high slope deformation of the strip mine and capable of effectively solving the problems of low high mine slope deformation forecasting precision and bad forecasting stability of the traditional fractal forecasting under the condition of small sample monitored data with volatility, and accordingly the forecasting error range is controlled effectively, the reliance of a forecasting model on large quantity of monitored data is reduced, and the practicability of the fractal forecasting is improved in application.
Owner:WUHAN UNIV OF TECH
Traffic flow prediction method based on spatio-temporal data embedding
PendingCN114169649APrediction is accuratePrediction results are stableForecastingNeural architecturesData transformationEngineering
The invention discloses a traffic flow prediction method based on spatio-temporal data embedding. The method comprises the following steps: acquiring historical traffic flow data; performing spatio-temporal data embedding based on historical traffic flow data, wherein the spatio-temporal data embedding comprises the following steps: performing interval representation of traffic flow: determining an interval to which each traffic flow belongs, and converting the determined interval into a corresponding traffic flow interval; generating traffic flow vectors: taking all the traffic flow intervals as input data, and converting the input data into embedded data, namely corresponding traffic flow vectors, by adopting a Word2vec model; time features are extracted based on the traffic flow vector to obtain a node feature matrix, and correlation between the electronic police devices is extracted to obtain a dynamic association graph; and inputting the node feature matrix and the dynamic association graph into a graph convolutional neural network to obtain a prediction result output by the graph convolutional neural network. According to the method, implicit correlation between traffic flows can be quantified and measured, high-level time features and a dynamic association graph are extracted for effective modeling, and accurate and stable traffic flow prediction is obtained.
Owner:ZHEJIANG UNIV OF FINANCE & ECONOMICS
Material constitutive model prediction method based on PSO-LSSVM
PendingCN111310348APrediction results are stableReduce manual interventionKernel methodsArtificial lifeTest sampleLeast squares support vector machine
The invention discloses a material constitutive model prediction method based on PSO-LSSVM. The method can be used for establishing a material constitutive model of various alloy materials in a mechanical property experiment, and comprises the following steps: firstly, obtaining material mechanical property indexes through the mechanical property experiment, recording experiment parameters, and dividing obtained data into a test sample and a training sample; optimizing key model parameters in an LSSVM (Least Square Support Vector Machine) by utilizing a PSO (Particle Swarm Optimization) algorithm and using the training sample; substituting the model parameters to predict the material constitutive model by using an LSSVM algorithm, and analyzing a prediction result by using a test sample. According to the method, the constitutive relation prediction model is established for the alloy material based on the PSO-LSSVM algorithm, the prediction precision is good, error evaluation indexes are maintained within a small range, and a new method is provided for establishing the material constitutive model.
Owner:JIANGXI UNIV OF SCI & TECH
Information popularity prediction method and device, and storage medium
ActiveCN110334275AImprove stabilityImprove predictive performanceWebsite content managementSpecial data processing applicationsComputer sciencePrediction methods
Owner:北京中科闻歌科技股份有限公司
Reservoir surface temperature prediction method based on space-time bidirectional attention mechanism
InactiveCN114155429AAddresses the lack of absolute measurements as a calibrationHigh precisionImage enhancementImage analysisAlgorithmWeather station
The invention discloses a reservoir surface temperature prediction method based on a space-time bidirectional attention mechanism, and the method comprises the following steps: S1, data preprocessing; s2, performing time sequence analysis on the LST time sequence obtained after the preprocessing operation in the step S1 in the research area, comparing the time sequence with the temperature of an adjacent meteorological station, and extracting time sequence characteristics; s3, using a PCAN network to extract a microclimate boundary feature map of the research area; and S4, constructing a space-time bidirectional attention prediction model based on LSTM + Attention on the basis of the LSTM, and calculating a prediction result. Compared with BPNN and LSTM prediction, the prediction performance of the prediction method based on the space-time bidirectional attention mechanism provided by the invention is obviously improved; the influence difference of the microclimate effect characteristics on the prediction result is obvious, the prediction result in the same microclimate characteristic is more stable, the variation fluctuation of the prediction result in the microclimate boundary region is large, and the abnormal value deviation is more obvious; and the space attention mechanism has an obvious inhibition effect on different coverage feature boundary abnormal values.
Owner:信阳学院
Method for predicting ammonia process flue gas desulphurization efficiency based on multiple parameters
InactiveCN102693451BReal-time monitoring of desulfurization efficiencyThe method is scientific and reasonableDispersed particle separationBiological neural network modelsSlurryVolumetric Mass Density
Owner:NORTHEAST DIANLI UNIVERSITY
Physical-statistical hybrid two-stage wind power prediction method
ActiveCN108832623AImprove forecast accuracyConducive to scheduling decision optimizationForecastingAc network circuit arrangementsElectricityAlgorithm
The invention discloses a physical-statistical hybrid two-stage wind power prediction method. Wind power prediction obtained by a physical prediction method and a statistical prediction method is usedas input of a second stage, and through a BP neural network, a wind power sequence on a target day is predicted. The method comprises the following specific steps: a physical prediction neural network model is built and trained; with each day as the target day, wind speed general similarity coefficients of other days relative to the target day are calculated respectively; a statistical predictionneural network model is built and trained; with wind speed and wind direction data of 24 h per day as input, a wind power physical prediction sequence for each day in former 180 days is sorted; withwind power sequences corresponding to former five similar days of each day as input, a wind power statistical prediction sequence for each day in former 180 days is obtained; and a BP neural network prediction model in a second stage is built and trained until a training error is smaller than a given value, and a two-stage prediction model is obtained finally.
Owner:ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
Power transformer fault prediction method and system driven by state holographic sensing data
PendingCN114397526AGrasp the trend of fault developmentIncrease reflectionTesting dielectric strengthSensing dataReal time analysis
The invention discloses a state holographic perception data driven power transformer fault prediction method and system. The method comprises the following steps: establishing a historical fault data set of a transformer; the method comprises the following steps: collecting the concentration of gas dissolved in oil in the operation process of a transformer, environment meteorological data reflecting the state of the transformer and other operation data, forming an optimal data set through a sudden change point detection and phase-space reconstruction mode, and building a recent prediction SARIMA model of the concentration of the gas dissolved in the transformer oil; obtaining a prediction result of the concentration change of the dissolved gas in the recent oil of the transformer; based on the relationship between the concentration of the dissolved gas in the oil and other state quantities of the transformer, the concentration of the dissolved gas in the oil changing in real time is obtained; building a transformer fault diagnosis model based on a DBN network; and taking a prediction result of the change of the concentration of the dissolved gas in the oil recently or the concentration of the dissolved gas in the oil changing in real time as an input characteristic quantity of the transformer fault diagnosis model to realize transformer fault prediction. And comprehensive real-time analysis and auxiliary decision making of the operation risk level of the power grid transformer are realized.
Owner:STATE GRID LIAONING ELECTRIC POWER RES INST +2
Prediction method of unbalanced data set based on generative adversarial network
PendingCN113298230AAvoid distancePrediction results are stableForecastingNeural architecturesData setGenerative adversarial network
The invention discloses an unbalanced data set prediction method based on a generative adversarial network. The method comprises the following steps: receiving a prediction request; collecting data to form a data set, and determining features and labels in the data set and the number of minority class samples and majority class samples; converting a non-numerical feature column and a label column in the data set into classification numerical values; standardizing the processed data set, and separating majority class samples and minority class samples in the standardized data set; synthesizing minority class samples a by using a deep convolutional adversarial network to form a balanced data set; dividing the balanced data set into a training set and a test set; constructing a convolutional neural network, and training the convolutional neural network by using the divided training set to obtain a trained convolutional neural network; finally, inputting the test set into the trained convolutional neural network to obtain a prediction result. According to the prediction method, the problem that minority class samples are very difficult to generate or even cannot be generated when big data are processed in an existing method is solved.
Owner:XIAN UNIV OF TECH
Model for predicting coke sulfur conversion through ash components and volatile components
ActiveCN108520169APrediction results are stableReduce manufacturing costChemical processes analysis/designSpecial data processing applicationsChemical industrySulfur
The invention relates to the technical field of coking chemical industry, in particular to a model for predicting coke sulfur conversion through ash components and volatile components. The model for predicting the coke sulfur conversion through the ash components and the volatile components takes three influence factors into account, wherein the three influence actors include the sulfur componentsof coal fed into an oven, the ash components of the coal and the volatile components of the coal fed into the oven. By taking several factors with most obvious influences on a coke sulfur conversionrate into account, the prediction model is more accurate. A prediction result of the coke sulfur conversion rate is stabilized, and a guiding meaning is achieved on production. When the coke sulfur components are stabilized, the production cost is reduced.
Owner:汾渭清洁能源控股(河北)有限责任公司
Well-to-seismic collaborative sedimentary microfacies division method based on high-precision sequence framework model
ActiveCN112505754AImprove efficiencyPrediction results are stableSeismic signal processingSeismic attributeAssise
The invention discloses a well-to-seismic collaborative sedimentary microfacies division method based on a high-precision sequence framework model, and the method comprises the steps: building a matching relation of well-to-seismic sedimentary facies features based on the lithologic combination of a stratum under a high-precision sequence stratum shelf and the seismic response features of sedimentary body cycle features, and optimizing the seismic attributes sensitive to the types of stratum sedimentary facies; and establishing a three-dimensional data volume for researching sedimentary subfacies and microfacies of a target layer through well-to-seismic collaborative simulation. And verifying the reliability and accuracy of the sedimentary facies three-dimensional data volume through a phase sequence rule, and finally obtaining a sedimentary facies division result suitable for industrial standards. According to the three-dimensional data volume of the sedimentary microfacies, the working efficiency of sedimentary facies division can be greatly improved, the transverse resolution and the longitudinal resolution are high, and the determinacy and the reliability of sedimentary microfacies fine division results are higher.
Owner:中国石油集团工程咨询有限责任公司
Acoustic network model training method and device and electronic equipment
ActiveCN111540344AImprove training efficiencyPrediction results are stableSpeech recognitionNeural architecturesEngineeringNetwork model
The embodiment of the invention provides an acoustic network model training method and device and electronic equipment, and belongs to the technical field of data processing, and the method comprisesthe steps: obtaining a training sample for the training of an acoustic network model, and enabling the training sample to comprise one or more voice files; performing peak detection on voice containedin the voice file by using a BLSTM network so as to divide letters contained in the voice file based on the detected peak to form segmented voice; performing semantic prediction on the segmented voice based on a preset CTC segmentation network to obtain a text prediction result; and after the prediction result meets a preset performance requirement, stopping training the acoustic network model soas to carry out character prediction on the input voice by using the acoustic network model. Through the processing scheme of the invention, the training efficiency of the acoustic network model canbe improved.
Owner:BEIJING BYTEDANCE NETWORK TECH CO LTD
Glomerular filtration rate estimation method based on WASP-BAS
InactiveCN110070941AImprove pruning efficiencyPrediction is accurateHealth-index calculationNeural architecturesCreatinine riseData set
The invention discloses a glomerular filtration rate estimation method based on WASP-BAS (Weights and Structure Policy-Beetle Antennae Search). The glomerular filtration rate estimation method based on WASP-BAS includes the steps: dividing experimental data into a data set, a verification set and a test set; taking the number of times that a smaller error cannot be found continuously as a constraint condition, and circulating under the condition that the number of times is not exceeded, i.e., one pruning process; temporarily determining the structure of the neural network after primary pruning, and then simplifying the neural network to reduce the number of hidden layer neurons, namely a secondary pruning process; finally, obtaining the neural network with the determined structure and weight threshold value, and estimating the glomerular filtration rate through seven inputs of the gender, the age, the height, the weight, the albumin, the serum creatinine and the urea of the neural network. According to the glomerular filtration rate estimation method based on WASP-BAS, a secondary pruning part is optimized by using a BAS, and a weight threshold from an input layer to a hidden layerand a weight from the hidden layer to an output layer are used as solution vectors for optimization; and the pruning efficiency of the WASP is higher, and the prediction result is more accurate and more stable.
Owner:HANGZHOU DIANZI UNIV
Data processing method and device based on artificial intelligence, terminal and storage medium
ActiveCN112199417AReduce instabilityImprove stabilityDigital data information retrievalCharacter and pattern recognitionData setOriginal data
The invention relates to the technical field of artificial intelligence, and provides a data processing method and device based on artificial intelligence, a terminal and a storage medium, and the method comprises the steps: segmenting an important data set selected from an original data set through employing an XGBoost model into a plurality of branch data sets; training and testing a lightGBM model by using each sub-data set to obtain a test passing rate; pre-standardizing the training data and the test data corresponding to the first field in the sub-data set to obtain a new sub-data set; training and testing the lightGBM model by using the new sub-data set to obtain a test passing rate; judging whether the data corresponding to the first field needs to be standardized or not accordingto the passing rate of the two tests; and repeatedly executing the process until whether the data corresponding to the last field in the important data set needs to be standardized or not is judged, and updating the important data set according to all judgment results to obtain a target data set. According to the method, the data set with relatively high stability and relatively high contributiondegree to the prediction model can be selected.
Owner:CHINA PING AN LIFE INSURANCE CO LTD
Intelligent water level detection equipment, intelligent water level monitoring management device and detection method
ActiveCN111292371AAvoid direct contactImprove reliabilityImage enhancementImage analysisGate arrayDigital storage
The invention discloses intelligent water level detection equipment, an intelligent water level monitoring management device and a detection method. The intelligent water level detection equipment iscomposed of a processor, a field programmable gate array, a random access memory, a digital memory card and a network communication module. The field-programmable gate array is preset to train a CNN convolutional neural network based on deep learning and predict a water line. The intelligent water level detection method based on deep learning is implemented through the intelligent water level monitoring management device. The method specifically comprises the following steps: setting a system state as a normal state, capturing an image according to a capturing strategy to perform image cuttingprocessing, predicting a water line, calculating a virtual water level, judging whether the water level exceeds an early warning virtual water level, judging whether to enter a system early warning state for the first time, updating early warning information, generating an early warning record, and sending an early warning short message. According to the invention, the problems of complex detection method and low efficiency of the image-based detection method for the water area in the prior art are solved.
Owner:YUNNAN UNIV +1
Short-term power sale quantity predicting method and system based on daily cumulative distributed electric quantity
InactiveCN106682840AAccurate predictionAvoid the problem of large fluctuations in forecast resultsResourcesLinear modelModel parameters
The invention discloses a short-term power sale quantity predicting method based on daily cumulative distributed electric quantity. The method comprises the following steps: step S1, establishing a linear function relation between a daily cumulative distributed electric quantity of a predicted month and a daily cumulative distributed electric quantity of the day in same period last year, taking the daily cumulative distributed electric quantity of the predicted month as a dependent variable, and taking the daily cumulative distributed electric quantity of the day in same period last year as an independent variable; step S2, substituting daily cumulative distributed electric quantity data from the first day to the predicted day of the predicted month and daily cumulative distributed electric quantity data of the day in the same period last year, and calculating model parameters; step S3, substituting the model parameters into the linear function relation to obtain a short-term power sale quantity linear model; and step S4, substituting the daily cumulative distributed electric quantity data of the last day of the month in the same period last year into the short-term power sale quantity linear model to obtain the predicted monthly power demand. The daily cumulative distributed electric quantity data accurately reflect fluctuation situations such as weather and economy of the same month, if the predicted day is close to the end of the month, the daily cumulative distributed electric quantity data used during modeling are more, and the accuracy is higher.
Owner:BEIJING CHINA POWER INFORMATION TECH +2
Multi-step wind power forecasting method based on singular spectrum analysis and locality sensitive hashing
ActiveCN107895206AThe physical meaning of the components is clearShort forecast timeForecastingEngineeringLocality-sensitive hashing
The invention discloses a multi-step wind power forecasting method based on singular spectrum analysis and locality sensitive hashing. The method is charactierzed by decomposing historical wind powerdata of a wind power plant into two independent components through singular spectrum analysis, the independent components being a low-frequency average trend component for reflecting wind energy overall change trend and a high-frequency fluctuation component for reflecting intermittency and fluctuation of wind respectively; reconstructing the two components in a phase space to obtain an average trend section and a fluctuation component section; and finding similar average trend sections of an average trend section to be forecasted through locality sensitive hashing, and carrying out locality predication. To prevent accumulated errors brought by separate forecast of each component and fixed error brought by forecast of only one component, the forecast input is combination of the similar average trend sections and corresponding fluctuation component sections, and finally, a prediction result of wind power output power is obtained. The method is clear in physical significance in wind power plant generation power prediction, short in prediction time and accurate and stable in prediction results; and the prediction results do not rely on prior knowledge of users.
Owner:SOUTH CHINA UNIV OF TECH
Data completeness test and feature learning method for building load prediction
PendingCN114282730AImprove forecast accuracyImprove robustnessForecastingFeature screeningFeature set
The invention discloses a data completeness test and feature transfer learning method for building load prediction, and the method comprises the following steps: proposing a feature screening method based on diffusion kernel density estimation and maximum correlation minimum redundancy, and determining an optimal feature set under different load prediction models, establishing a data set feature completeness test method through the determination of the correlation between the features of the new feature set and the optimal feature set and the feature distribution similarity; and establishing a load prediction model of feature migration. According to the method, the problem that the building load prediction precision is reduced due to incomplete data features can be solved, the stability of the prediction result can still be kept when the input of the model generates tiny change, and meanwhile, the calculation complexity of the building load prediction model is reduced. The method has the advantages that building load prediction precision is greatly improved, robustness of the load prediction model is enhanced, and calculation time is shortened.
Owner:TIANJIN UNIV
Time sequence prediction-oriented drift pulse neural network construction method and application thereof
PendingCN114781597AIncrease sensitivityAdaptive Forecasting ProblemNeural architecturesHidden layerAlgorithm
The invention belongs to the technical field of time sequence prediction, and particularly relates to a time sequence prediction-oriented drift pulse neural network construction method and application thereof, and the method comprises a new coding method which comprises the steps: converting an original time sequence into time when neurons give out pulses for the first time; besides, the network input layer is responsible for issuing pulses at corresponding moments according to the coded z-domain time sequence data Z (xt), the cell state Ct-1 at the previous moment and the hidden state Z (ht-1); the hidden layer comprises a forgetting gate, an input gate and an output gate, and the input of each gate is a pulse corresponding to Z (xt) and Z (ht-1); the pulse generated by the forgetting gate is used for realizing a function of selectively forgetting Ct-1; the pulse generated by the input gate is used for updating the cell state at the current moment; the forgetting gate and the input gate jointly realize the long-time memory ability of the time sequence; the pulse generated by the output gate corresponds to the hidden state at the current moment. According to the invention, the problem of time delay between input and output neurons can be effectively solved, and the long-time memory capability is realized.
Owner:HUAZHONG UNIV OF SCI & TECH
Face visible region analysis and segmentation method, face makeup method and mobile terminal
PendingCN112613374APrediction results are stableImprove computing efficiencyImage enhancementImage analysisSample graphEngineering
The invention discloses a face visible region analysis and segmentation method, a face makeup method and a mobile terminal, and the method comprises the steps: obtaining an initial image through obtaining a face region image of a sample image; randomly selecting more than one shelter from a material library, and adding the shelter to a random region of the face region image to obtain a shelter image; forming an image pair by using the initial image and the shelter image, and training the image pair by using a U-Net network to obtain a face analysis model; and predicting a to-be-processed image by utilizing the face analysis model to obtain a segmentation result graph of the to-be-processed image. The invention can be applied to application scenarios such as makeup processing of the to-be-processed image according to the prediction result, the real-time performance and robustness of face analysis and segmentation processing are greatly improved, and the application range is wider.
Owner:XIAMEN MEITUZHIJIA TECH
A physical-statistical hybrid two-stage wind power prediction method
ActiveCN108832623BImprove forecast accuracyConducive to scheduling decision optimizationForecastingAc network circuit arrangementsWind runNetwork model
Owner:ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
Data processing method, device, terminal and storage medium based on artificial intelligence
ActiveCN112199417BReduce instabilityImprove stabilityDigital data information retrievalCharacter and pattern recognitionData setOriginal data
The invention relates to the technical field of artificial intelligence, and provides a data processing method, device, terminal and storage medium based on artificial intelligence, including: dividing an important data set selected from an original data set using an XGBoost model into multiple sub-data sets ; Use each sub-data set to train and test the lightGBM model to get the test pass rate; pre-standardize the training data and test data corresponding to the first field in the sub-data set to obtain a new sub-data set; use the new sub-data set to train And test the lightGBM model to get the test pass rate; judge whether the data corresponding to the first field needs to be standardized according to the two test pass rates; repeat the above process until it is judged whether the data corresponding to the last field in the important data set needs to be standardized processing, and update the important data set according to all the judgment results to obtain the target data set. The present invention can select a data set with strong stability and great contribution to the prediction model.
Owner:CHINA PING AN LIFE INSURANCE CO LTD
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