43 results about "Quantile regression model" patented technology

The invention discloses an optimal scheduling method and system for a wind-light-hydrogen storage micro-grid, and relates to the field of wind-light-hydrogen storage micro-grids, and the method comprises the steps: obtaining multi-source time sequence data, and carrying out the combined denoising and dynamic time alignment to generate a standardized input sequence; adopting a quantile regression model fused with a space-time attention mechanism to output a power prediction interval of wind and light output and load demand in a future time period; constructing a layered multi-objective optimization model; an improved adaptive particle swarm optimization algorithm is adopted to solve the layered multi-objective optimization model, and an equipment scheduling instruction set is generated; and dynamic correction is carried out, and micro-grid instruction distribution is carried out according to the equipment response priority. According to the method, multi-target conflicts such as power balance, equipment loss and energy efficiency are effectively balanced through multi-source data efficient preprocessing, space-time joint prediction interval generation and hierarchical multi-target optimization, and efficient and stable operation of the wind-light-hydrogen storage micro-grid is achieved.

The invention discloses a machine learning-based flight wheel block removal time prediction method, and relates to the technical field of flight prediction, and the method comprises the steps: collecting flight preorder state data, airport resource distribution data and meteorological data in real time, and generating an original data set through multi-source heterogeneous data fusion; constructing spatio-temporal features including a preorder flight delay propagation chain, a stand-vehicle conflict energy matrix and a meteorological attenuation factor, and screening and optimizing a feature set through distribution drift detection; training and verifying the Bayesian depth quantile regression model, and outputting a prediction result with a confidence interval; and combining the airport Internet of Things positioning feedback optimization feature set and parameters to generate a prediction deviation diagnosis report. According to the method, a preorder flight delay propagation chain and a stand-vehicle conflict energy matrix are constructed, flight dynamics, resource allocation and weather attenuation factors are embedded into a unified spatial-temporal feature space, and the problem of feature information loss caused by data isolation is solved.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention provides a wetland forest and grass ecosystem restoration strength evaluation method under drought stress. The method comprises the steps of data collection, daily-scale ecosystem service function index ESS construction, ecological drought identification and restoration elasticity curve construction. Wherein the time resolution of the index is accurate to the day, the capture of short-time drought below the monthly scale is enhanced, and the accurate description of the dynamic process of ecological system restoration under the drought stress is realized; combined identification is carried out on the drought events in combination with the standardized ecological water deficit, and the accuracy of identification of the drought events causing significant differences of ecological system service functions is improved; a quantile regression model is utilized to quantify the ecological system recovery capability of different vegetation types, and data characteristics of different recovery time distributions of the ecological system under the same drought intensity are considered, so that the defect that a traditional mean value regression model cannot fully describe drought complex characteristics is overcome; and a theoretical support is provided for sustainable development of an ecological system under an extreme climate condition.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The disclosure provides an electricity consumption management method and an electricity consumption management system. The method includes the following steps. Historical electricity consumption data of an electricity field is obtained. A plurality of target feature variables are determined by performing feature selection based on the historical electricity consumption data. An electricity baseline prediction model using the plurality of target feature variables is established based on the historical electricity consumption data. The electricity consumption baseline prediction model is a quantile regression model. The target percentile of the quantile regression model is determined by comparing actual electricity consumptions of the electricity field with first baseline electricity consumptions predicted by the electricity baseline prediction model. A second baseline electricity consumptions for a unit period is predicted based on the target percentile using the electricity consumption baseline prediction model, and electricity management function is performed based on the second baseline electricity consumption.

The application discloses a kind of static stability margin tail risk prediction method, device and medium of power system, belong to risk prediction technical field.Its method includes: using quantile regression model to carry out multi-quantile prediction to wind power, photovoltaic and other new energy output, obtains the cumulative distribution function of each new energy node output;Further, the discrete probability density function of new energy output is constructed by discretization and difference method, to avoid the modeling error caused by continuous distribution assumption;On this basis, combined with thermal power output configuration and static stability margin based on converter dynamic parameters, the mapping relationship between new energy random injection and system static stability margin is established, to realize the quantitative prediction of stability margin probability distribution and its tail risk.The application can accurately reflect the distribution characteristics of receiving-end power system static stability margin in the sense of probability, especially the stability margin change law under low-probability, high-risk working condition.

The invention discloses a wind power plant power generation prediction and uncertainty analysis method and system based on a hybrid intelligent algorithm, and belongs to the technical field of wind power plant power generation prediction, and the method comprises the steps: employing a fuzzy K-means clustering algorithm to carry out the classification of wind speed data, and dividing a data set according to the wind speed grade; a plurality of support vector regression models are established, each model corresponds to different wind speed grades, and parameters of each support vector regression model are optimized by using an enhanced harmony search algorithm; and carrying out uncertainty analysis on a prediction result of the support vector regression model by adopting a quantile regression method, calculating prediction intervals under different confidence levels, and optimizing parameters of the quantile regression model by utilizing an enhanced harmony search algorithm. The method improves the organization and correlation of the data, enables the prediction model to be trained and optimized for different wind speed grades, effectively avoids the problems of local optimum and overfitting, improves the precision and generalization capability of wind power prediction, and achieves the efficient and precise prediction of the wind power.

The present invention provides a method for quantitatively assessing the resilience of wetland forest and grassland ecosystems under drought stress. The method comprises: data collection, construction of a daily-scale ecosystem service function index (ESS), ecological drought identification, and construction of a recovery resilience curve. The temporal resolution of the index is refined to the daily level, enhancing the capture of short-term droughts on a sub-monthly scale and accurately depicting the dynamic process of ecosystem recovery under drought stress. Drought events are jointly identified using a standardized ecological water deficit, improving the accuracy of identifying drought events that cause significant differences in ecosystem service functions. A quantile regression model is used to quantify the ecosystem resilience of different vegetation types, taking into account the data characteristics of the different recovery time distributions of ecosystems under the same drought intensity. This overcomes the shortcomings of traditional mean regression models, which cannot fully describe the complex characteristics of drought, and provides theoretical support for the sustainable development of ecosystems under extreme climate conditions.

The invention relates to the technical field of remote sensing and geographic information, and discloses a method for identifying the influence of mining intensity on a mining industry urban society-ecosystem, which comprises the following steps: extracting year-by-year mining industry activity land vector data of a mining industry centralized development area, and establishing a space density field model; noctilucent remote sensing data and GDP statistical data are fused, and a social economic development index is constructed; integrating four key ecological factors of greenness, humidity, dryness and heat, and constructing comprehensive indexes for evaluating the regional ecological environment quality; constructing a mining city society-ecosystem analysis framework to systematically quantify an interaction relationship between social economy and ecological environment components; and analyzing the influence of the mining strength index on the interaction relationship by adopting a panel quantile regression model. According to the method, quantitative evaluation of the social-ecological system interaction relationship is realized, and the threshold feature of the influence of the mining intensity index on the system is revealed.

The invention discloses a micro-grid operation random optimization method based on source load probability prediction. The method comprises the following steps: firstly, performing energy-sensitive self-organizing segmentation on source-load historical time sequence data, and extracting morphological fingerprint features; and adopting an improved affinity propagation clustering algorithm fused with a power system operation constraint penalty mechanism to identify a typical operation mode. Secondly, a quantile regression model based on a gated pulse neural P system is established in each mode for probability prediction, and a probability scene library with weights is generated; and finally, constructing a two-stage stochastic optimization model with the goal of minimizing the expected operation cost, and solving by adopting a Benders decomposition algorithm to obtain a fixed equipment plan and a flexible operation strategy. According to the method, through refined mode recognition and probability modeling, on the basis of fully considering the uncertainty of the source load, robust optimization of economic operation of the micro-grid is realized, and the expected operation cost of the system is effectively reduced.

The present invention discloses a photovoltaic power ultra-short-term non-parametric probability prediction method based on fuzzy sample particles. First, the present invention analyzes the sample characteristics of the photovoltaic power time series, combines the meteorological forecast irradiance data in the numerical weather forecast, and constructs a sample particle processing method based on merging-decomposition. In addition, a hierarchical clustering method based on sample particles is studied. According to different clusters of different sample particles, combined with the characteristics of the preset sample input to be predicted, the sample particles are adaptively expanded by a reasonable multiple and then restored to the original samples to realize the dynamic adjustment of the sample weights. Finally, based on the quantile regression model of the extreme learning machine, the photovoltaic probability prediction model is trained respectively according to the situation that the sample to be measured belongs to different clusters. The method of the present invention has good reliability and overall performance, and greatly improves the practicability and accuracy of the ultra-short-term non-parametric probability prediction technology for photovoltaic power generation.

The application discloses a logistics transportation cost accounting method, system, device and medium, and relates to the field of cost accounting. In the method, pickup stage characteristics, transportation stage characteristics, delivery stage characteristics and manual operation characteristics are constructed according to order demand information and dynamic cost data; a first predicted cost of the pickup stage, a second predicted cost of the transportation stage and a third predicted cost of the delivery stage are predicted, and a manual operation cost is calculated; a preliminary total predicted cost is obtained according to the above costs; a preset quantile regression model is used to predict a cost median, a first target quantile cost and a second target quantile cost, a price interval is determined according to the first target quantile cost, the second target quantile cost and a preset buffer coefficient, and if the preliminary total predicted cost is in the price interval, the preliminary total predicted cost is used as a final total cost. By implementing the technical scheme provided in the application, the logistics transportation cost can be more accurately accounted.

The invention relates to a power distribution network weight and overload probability prediction method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: respectively establishing a power distribution network general load training set and an electric vehicle charging load training set; respectively constructing a general load extreme learning machine prediction model and a charging load extreme learning machine prediction model according to the power distribution network general load training set and the electric vehicle charging load training set, and respectively carrying out model training; based on the trained general load extreme learning machine prediction model and the trained charging load extreme learning machine prediction model, respectively carrying out calculation by combining a quantile regression model, and respectively obtaining a power distribution network general load prediction result and an electric vehicle charging load prediction result at the prediction moment; and according to the power distribution network general load prediction result and the electric vehicle charging load prediction result at the prediction moment, calculating to obtain a power distribution network heavy overload probability prediction result. The method can improve the stability of the whole power distribution network.

This invention discloses a digital twin-based water environment monitoring station operation and maintenance simulation system, belonging to the field of environmental monitoring and operation and maintenance management technology. The system includes a multi-source sensing unit for the station, a digital twin modeling engine, an operation and maintenance simulation decision-making platform, and a station execution feedback unit, constructing a three-layer collaborative architecture. A high-fidelity digital twin is constructed through multi-source data fusion and generative 3D reconstruction. A two-stage anomaly detection model accurately identifies equipment and water quality anomalies, and an improved quantile regression model is used to predict future trends, ultimately generating an optimal operation and maintenance strategy and forming a closed loop. This invention solves the problems of passive operation and maintenance and extensive resource allocation in existing systems, upgrading the operation and maintenance mode from passive repair to proactive predictive maintenance, improving operation and maintenance efficiency, and reducing operation and maintenance costs.

The application provides a new energy power generation prediction method and system based on numerical weather prediction, and relates to the technical field of new energy power generation. First, the numerical weather prediction spatial grid multi-element data of a target area is obtained, feature construction is performed, and time domain features and space domain features are obtained. The time domain features and the space domain features are spliced to obtain space-time features. A preset point prediction model and a quantile regression model are trained to obtain trained point prediction models and quantile regression models. Finally, the space-time features are input into the trained point prediction models and quantile regression models, and new energy power generation prediction results are output. The application improves the accuracy of new energy power generation prediction by extracting the space-time features of the numerical weather prediction spatial grid multi-element data.

This invention discloses an intelligent early warning dosing method, system, and medium based on t-Copula and quantile regression, belonging to the technical field of water treatment. Data acquisition involves collecting historical operational data and constructing a joint fluctuation risk feature using a t-Copula model based on the volatility sequence of influent variables. An input feature set is constructed based on the joint fluctuation risk and influent variables, with effluent turbidity as the target variable, to build a quantile regression model. A multi-objective optimization function is constructed based on the joint fluctuation risk feature and quantile prediction results to obtain the optimal dosing dosage. This invention uses a t-Copula model to jointly model the volatility of multiple influent water quality parameters, quantifying the tail risk of multi-parameter coordinated fluctuations. This overcomes the shortcomings of existing technologies that ignore the correlation between parameters and cannot capture joint extreme risks, demonstrating good practicality.

The invention relates to the technical field of new energy battery detection, and provides a method and a system for predicting the remaining life of a decommissioned battery based on multi-electric parameter fusion, and the method comprises the steps: firstly, extracting a multi-dimensional health factor closely related to a battery aging mechanism from a specific voltage window through a standardized data collection process; and a temperature compensation function and a current adaptive calibration mechanism are introduced to normalize the features, so that the problem of feature drift caused by ambient temperature and charging current fluctuation is effectively overcome, and the generalization ability of the model is remarkably improved. On this basis, a dual-guarantee model fusing anomaly detection and uncertainty prediction is constructed: abnormal battery identification and interception are carried out by using a mahalanobis distance, and invalid prediction is completely eradicated; and synchronously outputting a point estimation value and a confidence interval of the residual life by adopting a quantile regression model, and realizing quantification of prediction uncertainty. The invention provides a high-reliability evaluation tool for echelon utilization of the retired battery.

The invention discloses a multi-industry power load prediction method, device and equipment, a medium and a program product, and the method comprises the steps: obtaining the power load data of at least one data source, and determining a target industry field to which the power load data belongs based on the data characteristics of the power load data; obtaining a load probability prediction model corresponding to the target industry field, wherein the load probability prediction model at least comprises a conformal quantile regression model; and determining a load prediction interval of the power load data based on the load probability prediction model. According to the method, the target industry field is accurately identified by analyzing the data features of the power load data, and then the model is selected in a targeted manner, so that prediction deviation caused by industry difference of a traditional model is avoided, and prediction accuracy is improved; the prediction uncertainty is converted into the load prediction interval through the load probability prediction model, the problem that power supply and demand are difficult to accurately match due to the fact that a traditional prediction method cannot quantify the uncertainty is solved, and the prediction reliability is improved.

The invention discloses an energy future price prediction method based on a quantile feature reduction technology, and the method comprises the steps: capturing the heterogeneity influence of a high-dimensional tail risk factor on the condition distribution of the energy future price through a quantile regression (LQAR) model based on an LASSO algorithm, and achieving the prediction of the uncertainty of the energy future price. The limitation that the traditional OLS only predicts the mean effect is broken through; meanwhile, an LASSO algorithm is introduced to dynamically screen high-dimensional tail risk factors, the problems of noise interference and overfitting are solved, and the robustness of a prediction model is improved; in addition, by constructing a high-frequency high-dimensional tail risk factor set, the defect that a traditional method depends on low-frequency fundamental plane data is overcome, and therefore the accuracy of energy future price prediction is improved.

The application provides a non-standard rock sample compressive strength correction method, comprising: collecting rock samples generated under different formation conditions from different oil and gas blocks, measuring the height and diameter of each sample, determining the corresponding sample type, and simultaneously obtaining the lithology of each sample, and measuring the compressive strength of each sample by setting a conventional experiment; the height, diameter, confining pressure, and compressive strength are dimensionally standardized, and the sample data is randomly divided into a training set and a test set with consistent sample type and lithology distribution according to a ratio of 7:3; a quantile regression model based on GBDT is constructed using the training set, the hyperparameters of the quantile regression model are optimized using a particle swarm optimization algorithm, the test set is used to verify the quantile regression model to output the compressive strength correction values of multiple key quantiles, and the uncertainty range of the correction result is quantified. The method can accurately establish a unified standard strength benchmark that adapts to different height-diameter ratios and different lithologies, and greatly improves the correction accuracy.

This application discloses a load forecasting method for rural transformer substations. The method includes: collecting load data corresponding to transformer substations in rural areas; inputting the load data corresponding to the transformer substations into a preset quantile regression model for regression analysis to construct a first forecast interval for each transformer substation; selecting a corresponding target hierarchical calibration amount from a preset conformal forecasting mechanism based on the quarterly and line attributes associated with the load data, and calibrating the first forecast interval based on the target hierarchical calibration amount to obtain a calibration interval for each transformer substation; aggregating the calibration intervals corresponding to all transformer substations in rural areas to obtain an aggregated interval for rural areas, thereby obtaining the forecast result. The embodiments of this application can improve the reliability of the predicted calibration interval for a single transformer substation and the predicted aggregated interval for the entire rural area.

The invention relates to the field of tower-type photo-thermal mirror fields, and discloses a tower-type photo-thermal mirror field heat absorber output thermal power prediction method, which comprises the following steps: on the basis of establishing a tower-type photo-thermal heat absorber thermal power prediction generalized linear mixed quantile regression model, solving unknown parameters in the model; predicting the output thermal power of the heat absorber at a certain moment in the future by using the obtained model; according to the method disclosed by the invention, the generalized linear mixed quantile model is adopted, and the model has no requirements on independent variables and dependent variables, can be suitable for data of any hierarchical structure, considers a fixed effect and a random effect, is relatively high in prediction precision, and is more stable in performance on outliers or abnormal values; quantiles are also considered, so that the integrality of the thermal power prediction effect of the heat absorber is more accurate and flexible; according to the method, various random effects including periodic factors, wind speed, temperature and the like are considered, and the precision for measuring the heat loss of the heat absorber is higher.

The invention relates to the technical field of intelligent traffic and itinerary planning, in particular to an intelligent risk insight system based on flight event flow prediction and itinerary re-planning. Comprising a journey planning unit, a flight event flow prediction and journey planning unit and a client. The route planning unit fuses real-time traffic and airport operation data, and provides route planning and time estimation before boarding for users. The core flight event flow prediction and route planning unit is used for predicting flight time probability distribution by utilizing a quantile regression model through multi-source data fusion and real-time feature engineering, predicting a next key event by utilizing a survival analysis model, and carrying out quantification and interpretable attribution on risks; an intelligent re-planning scheme with a trigger condition is generated based on multi-objective optimization; and the final result is encapsulated and then presented through the client. According to the invention, active perception, accurate prediction and intelligent decision-making assistance of flight trip full-chain risks are realized.

The application provides an ultra-short-term probability wind power prediction method based on space-time multi-scale and K-SDW, and comprises the following steps: S1, decomposing normalized target wind speed into multiple sub-sequences through variational mode decomposition; S2, reconstructing the sub-sequences and adjacent space wind speed sequences into space-time candidate features, and performing space-time multi-scale feature selection; and S3, predicting each sub-sequence by using a quantile regression model. The application can realize wind power prediction.

The invention relates to the technical field of electric energy storage, in particular to a method, a device and equipment for training and predicting a state of charge prediction model of an energy storage battery. The method comprises the following steps: constructing a data set by acquiring current, voltage, temperature and battery state-of-charge values of an energy storage battery at continuous moments; inputting samples in the sample pair into a preset first to-be-trained model for multi-scale feature extraction to obtain multi-scale output features at the current moment; inputting the multi-scale output feature at the current moment into a preset second to-be-trained model for prediction processing to obtain a prediction output result at the current moment; constructing a quantile loss function according to the prediction output result at the current moment, the label corresponding to the sample and a preset quantile regression model; the first to-be-trained model and the second to-be-trained model are adjusted according to the quantile loss function corresponding to the sample pair at each moment, a trained energy storage battery charge state prediction model is obtained, and the operation safety and management efficiency of an energy storage system are improved.

The invention discloses an electric power system short-time regional carbon emission prediction method and system, and the method comprises the steps: firstly, classifying electric power users, building a load prediction model, monitoring and updating the load classification division through employing a swinging resident voting mechanism, and obtaining a total load prediction interval containing uncertainty; secondly, adopting a quantile regression model to obtain a total new energy power generation prediction interval; then, a net load uncertainty set which must be met by thermal power generation is determined based on the two intervals, and based on the net load uncertainty set, economic cost and carbon emission serve as dual optimization targets, a thermal power generation optimization scheduling model is used for outputting an optimal scheme set containing multiple optimal tradeoff strategies for a decision maker to select. According to the invention, through combination of self-adaptive prediction at the front end and multi-target robust decision at the rear end, the accuracy of carbon emission prediction and the reliability and flexibility of power grid dispatching are improved.

This invention discloses a method, device, and medium for predicting the tail risk of static stability margin in power systems, belonging to the field of risk prediction technology. The method includes: using a quantile regression model to perform multi-quantile prediction of the output of new energy sources such as wind power and photovoltaics, obtaining the cumulative distribution function of the output of each new energy node; further constructing a discrete probability density function of the new energy output through discretization and differencing to avoid modeling errors caused by the assumption of continuous distribution; based on this, combining the thermal power output configuration and the static stability margin based on converter dynamic parameters, establishing a mapping relationship between the random injection of new energy and the static stability margin of the system, realizing the quantitative prediction of the stability margin probability distribution and its tail risk. This invention can accurately reflect the distribution characteristics of the static stability margin of the receiving-end power system in a probabilistic sense, especially the stability margin variation law under low-probability, high-risk operating conditions.

The invention discloses a driving factor analysis and effect verification method for industrial waste gas emission space unevenness. The method comprises the following steps: acquiring industrial waste gas emission, industrial energy consumption, total energy consumption, total domestic production value and population data of a plurality of sub-regions in a target region; calculating a total industrial waste gas emission Tele index of the target area, and decomposing the index into intra-group uneven components and inter-group uneven components; the per capita industrial waste gas emission of the sub-regions is decomposed into a product of a pollution intensity effect factor, an industrial structure effect factor, an energy intensity effect factor and an economic development effect factor; variable coefficients of differences of the four driving factors among the sub-regions in the target region are calculated respectively and serve as agent variables; establishing a metering economy model, and performing regression analysis by taking a Tele index as a dependent variable and an agent variable as an independent variable; and analyzing marginal effect coefficients of independent variables on different quantiles by adopting a multivariate quantile-quantile regression model.

The invention discloses an electric energy quality sensitive domain modeling method, system and device and a medium, and belongs to the technical field of electric energy quality modeling and evaluation.The method comprises the steps that user side electric energy quality feature data is extracted, a non-intersecting quantile regression model based on Huber-pinball loss function optimization is built, and a node electric energy quality feature probability representation model is obtained; extracting feature data of a user side and a network side, constructing a power flow fitting model based on an RBF neural network, deducing the overall power quality sensitivity of each node, and screening key nodes; in combination with the source load characteristic data under different confidence levels, constructing a power quality sensitive domain probability model, and constructing a sensitive domain boundary based on the most sensitive direction; quantitative evaluation and key area identification of the electric energy quality risk under the new energy access condition are realized, and the power grid regulation and control and operation decision-making capability is improved.