53 results about "Quantile regression model" patented technology

The invention discloses a nuclear extreme learning machine quantile regression-based wind power interval prediction method. The method comprises the steps that a wind power plant output power and windspeed data is collected; the data is processed simply, and unreasonable data is deleted; a nuclear extreme learning machine quantile regression model is built; by means of a particle swarm algorithm,nuclear extreme learning machine quantile regression parameters are optimized, and a regression module is determined; test data is put, and a wind power prediction interval is obtained. Accordingly,the nuclear extreme learning machine quantile regression principle and a nuclear extreme learning machine model are effectively combined, and the optimal model parameter is obtained by conducting search and optimization through the particle swarm algorithm, uncertain information in wind power can be effectively grasped, then a better prediction result is obtained, and the basis can be provided forsafe and stable running of wind power integration.

The invention provides a regional wind power prediction method and system based on space-time quantile regression. The method comprises the following steps: collecting the operation and numerical weather prediction data of a plurality of wind power plants in a preset time period, converting the collected data into a feature map, and building a training set, a verification set and a test set; establishing a space-time quantile regression model, and training and optimizing the model by utilizing the training set, the training set, the verification set and the test set; acquiring operation data and environment data of each wind power plant in real time, and predicting regional wind power generation in a certain time period in the future according to the optimized space-time quantile regression model. According to the invention, short-term non-parameterized probability prediction is carried out on regional wind power through the space-time quantile regression model; the selection problem of explanatory variables in regional wind power prediction with large input information is solved, the prediction accuracy and reliability are greatly improved, and a specific solution is provided forregional wind power generation probability prediction with big data.

The invention discloses a flight running time prediction method based on reliability, and belongs to the technical field of flight running time prediction. The method aims to solve the technical problem that flight running time under different reliability is difficult to accurately predict in the prior art. The method comprises the following steps: establishing a meteorological message and flightoperation basic database; based on the meteorological message and the flight operation basic database, at least two time periods are extracted from one day to serve as delay time windows respectively;establishing a quantile regression model of each delay time window based on the meteorological message and the flight operation basic database; and predicting flight operation time under different reliabilities according to flight planned departure time and the quantile regression model.

The invention discloses a QRNN-based reasonable range estimation method for low-voltage transformer area line loss rate in the technical field of line loss rates. The method aims to solve the technical problem that in the prior art, the line loss rate evaluation basis cannot reflect the difference of a transformer area in structure, power supply range and load level, and comprises the steps of collecting transformer area operation data based on an existing power utilization management system, and screening out transformer area line loss rate influence factors; correlation analysis is carried out on the line loss rate of the transformer area, and line loss rate key factors influencing the line loss rate are extracted; calculating a reasonable range of the line loss rate of the low-voltage transformer area based on a neural network quantile regression model; and according to the calculated reasonable range of the line loss rate of the specific transformer area, diagnosing whether the transformer area is an abnormal transformer area, and adjusting the transformer area. Key factors influencing the line loss rate are extracted on the basis of actual operation data of an existing power utilization information system, an existing marketing system, an existing PMS system and the like, the reasonable range of the line loss rate can be given for a specific transformer area, and a reliable basis is provided for diagnosing a transformer area with abnormal line loss.

The invention relates to an energy demand condition density prediction method. The method comprises the following steps of establishing a support vector quantile regression module; establishing a support vector weighing quantile regression module for energy demand; estimating the parameters of the models; predicting the condition density, and the like. The method has the beneficial effects that by combining the advantages of non-linear processing capability of a support vector machine and complete description capability of quantile regression on the condition distribution feature, the support vector quantile regression module for predicting the energy demand is established; on one hand, the non-linear structure of an energy system in a low-dimension space is mapped into a high-dimension space by the support vector machine, and is converted into a linear structure, so the complexity of modeling is reduced; on the other hand, the change rule of the whole condition distribution of energy demand is depicted by the quantile regression, and more available information is provided; a non-parameter kernel density estimation technology is adopted to establish the energy demand condition density prediction method, and the complete prediction of whole condition distribution feature of energy demand is realized.

The invention discloses a disturbance voltage maximum amplitude quantification method and device of in-situ intelligent measurement equipment. The disturbance voltage maximum amplitude quantification method of the local intelligent measurement equipment comprises the following steps: extracting an inter-fracture breakdown voltage and an nth micropulse amplitude of a port disturbance voltage of nth breakdown of a VFTO in the same disconnecting switch operation process; forming an event pair by the extracted breakdown voltage between the fractures and the micro-pulse amplitude so as to form a sample set of a quantile regression model; and training a quantile regression model based on the sample set to generate a corresponding quantile regression equation, the generated quantile regression equation being capable of quantitatively evaluating the port disturbance voltage amplitude of the in-situ intelligent measurement device under the operation of the isolation switch. Therefore, the disturbance level of the on-site intelligent measurement equipment port of the specific GIS test loop under the operation of the isolating switch can be quantitatively evaluated, and a targeted immunity test assessment scheme is formulated.

The invention discloses a QRNN-based method for estimating the reasonable range of the line loss rate of the low-voltage station area in the field of line loss rate technology, aiming to solve the problem that the evaluation basis of the line loss rate in the prior art cannot reflect the structure, power supply range, and load level of the station area Based on the existing power management system to collect operating data of the station area, the factors affecting the line loss rate of the station area are screened out; the correlation analysis is carried out on the line loss rate of the station area, and the line loss rate that affects the line loss rate is extracted The key factor; based on the neural network quantile regression model, the reasonable range of the line loss rate of the low-voltage station area is calculated; according to the calculated reasonable range of the line loss rate of the specific station area, it is diagnosed whether it is an abnormal station area, and it is adjusted. The method of the present invention is based on the actual operation data of the existing electricity information system, marketing system, PMS system, etc., extracts the key factors affecting the line loss rate, and can give a reasonable range of the line loss rate for a specific station area, for It provides a reliable basis for diagnosing the abnormal line loss area.