31 results about "Ensemble prediction" patented technology

Systems and methods are described for generating an enhanced prediction from a 2D and 3D image-based ensemble model. In various embodiments, a computing device can be configured to obtain one or more sets of 2D and 3D images and to standardize each of the 2D and 3D images to allow for comparison and interoperability. Corresponding 2D3D image pairs can be determined from the standardized 2D and 3D pairs where the 2D and 3D images correspond based on a common attribute, such as a similar timestamp or time value. The enhanced prediction can use separate underlying 2D and 3D prediction models where the 2D and 3D images of a 2D3D pair are each input to the respective underlying 2D and 3D prediction models to generate respective 2D and 3D predict actions.

The invention provides a reservoir flood control risk rate prediction method based on runoff ensemble forecasting. The reservoir flood control risk rate prediction method based on runoff ensemble forecasting comprises the steps that (1) a plurality of sets of runoff forecasting processes are obtained according to runoff ensemble forecasting results obtained on the basis of a plurality of forecasting schemes; (2) a reservoir outflow threshold and a reservoir water level threshold are set, and a reservoir flood control risk event is defined; (3) the reservoir upstream flood control risk rate and the reservoir downstream flood control risk rate are predicated on the basis of the runoff forecasting processes, the reservoir outflow threshold, the reservoir water level threshold and a current reservoir flood control scheduling scheme. According to the reservoir flood control risk rate prediction method based on runoff ensemble forecasting, the reservoir flood control risk rates can be analyzed in a systemized and complete mode, the reservoir flood control risk rate prediction method can be widely applied to reservoir flood control scheduling, and the basis is provided for scientific decision making of reservoir flood control scheduling.

The present invention proposes a global seamless typhoon power set prediction method and system, which assimilates multivariate observation data into the pre-established climate system model through the method of growth analysis and updating IAU, so as to provide the initial field, and then perform parallel calculation on the supercomputer Generate 36-year back calculation data sets and real-time forecast data sets; according to the back calculation data sets and real-time forecast data sets, use the method of direct recognition of typhoon signals to select typhoon signals from the pre-established climate system model prediction results out; the selected typhoon signals are post-processed to generate forecast pictures and released; the global seamless typhoon power set prediction method and system of the present invention can realize global typhoon multi-time scale prediction, and the climate system model coupling process takes into account Ocean and sea ice modules to improve forecast accuracy.

The invention discloses a short-term load forecasting method of a distribution network based on multi-mode fusion, the main steps of which are as follows: 1) collecting historical load time series data X of a power network. 2) Perform STL time series decomposition on historical load time series data X. 3) Get the trend item sequence X trend LSTM neural network model with N structures, residual item sequence X remainder LSTM neural network model with N structures and integrated prediction model. 4) Obtain the period item prediction result O s . 5) Obtain forecast samples. 6) Input the forecast sample into the forecast model to obtain the trend item forecast result O t and the prediction result O of the remaining items r . 7) Integrating the prediction results of periodic items O s , Trend item prediction result O t and the prediction result O of the remaining items r , and use the integrated prediction to obtain the final prediction result. The present invention helps to improve the prediction accuracy of the model while improving the robustness and generalization ability of the load prediction model.

The invention provides a maritime distress target drift set prediction method considering feedback information. The method comprises the steps of establishing a maritime search and rescue emergency guarantee database; establishing a maritime distress target drift trajectory prediction model which comprehensively considers the combined action of wind, wave and current; performing full-dimensional disturbance on the wind field, the flow field, the wave field, the distress place, the distress time, the wind-induced drift coefficient, the wave-induced drift coefficient and the flow-induced drift coefficient, constructing set members, and performing offshore distress target drift track set prediction; on the basis of the maritime distress target drift trajectory prediction model, maritime distress target drift trajectory set prediction is carried out, a search and rescue range is further calculated, and a maritime distress target drift set prediction result is obtained; in the process of searching the distress target, performing matching analysis on the set prediction result according to feedback information received in real time; optimizing and adjusting the disturbance vector according to an analysis result, and re-carrying out offshore distress target drift trajectory set prediction until a distress target is found.

The invention provides an LSSVM pulsating wind speed prediction method based on the integration of ant colony and particle swarm, which includes the following steps: performing normalization processing; calculating the pheromone concentration of each ant; moving other ants in the ant colony to the position of the head ant. Global search; update the ant pheromone concentration at each position during the iterative process, check whether the iteration termination condition is satisfied, if not, return to the third step; otherwise, the algorithm ends and outputs the optimal parameter combination; initializes the relevant parameters of the particle swarm; The fitness value of each particle's own optimal position is compared with the fitness value of the group's optimal position; the predicted fluctuating wind speed time history spectrum is obtained. The invention has the characteristics of high optimization precision, high convergence precision, few iteration times, high success rate and the like.

The invention discloses a semi-supervised integrated real-time learning method for soft measurement of Mooney viscosity of industrial mixing rubber. The present invention aims at the problem of poor prediction performance of the traditional soft-sensing method caused by the lack of marked samples and sufficient non-marked samples in the process of industrial rubber mixing. Based on the Gaussian process regression model, combined with the real-time learning method, a diverse JITGPR sub-model is constructed. Adaptive ensemble prediction is performed on selected unlabeled samples, and high-confidence pseudo-labels are selected to augment the training sample set. Finally, the final predicted value of Mooney viscosity is obtained through the fusion of the expanded training set, diverse JITGPR sub-models and limited mixing mechanism. The invention overcomes the problems of less marked samples, sufficient non-marked samples, increased cost, and difficulty in improving product quality due to the lag in obtaining the Mooney viscosity value during the rubber mixing process, realizes the online real-time prediction of the Mooney viscosity, and effectively improves the traditional Predictive performance of soft-sensing modeling of compound Mooney viscosity.