7 results about "Quantile regression model" patented technology

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.

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.

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.

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.

This invention provides a method and apparatus for predicting renewable energy power in extreme scenarios based on dual-input features and nonparametric quantile regression. First, historical power data and historical meteorological data are acquired. Then, based on the historical meteorological data and the physical response mechanism of renewable energy output under extreme weather conditions, a dual-input feature vector is constructed. Finally, the historical power data and the dual-input feature vector are input into a nonparametric quantile regression model to obtain the probability prediction result for the extreme scenario. This invention constructs a dual-input feature vector based on the physical response mechanism of renewable energy output under extreme weather conditions and combines it with a nonparametric quantile regression model with bandwidth parameter optimization for nonlinear fitting. This accurately characterizes the complex coupling relationship between meteorology and power output and achieves risk quantification, thereby effectively improving the prediction accuracy and reliability of renewable energy power in extreme scenarios.