40results about How to "Reduce scheduling difficulty" patented technology

This application is about a method for determining the operating status of equipment based on a smart park and a big data center. Applying the scheme of this application, the first step is to determine the j group of equipment operating parameters corresponding to the i production equipment, the second step is to generate a parameter list based on the equipment type information of the i production equipment and the operating period parameters of the j group of equipment operating parameters, and the third step The first step is to perform feature extraction on the parameter list to obtain the feature list, and the fourth step is to correct the state feature vector with the largest state weight in each target list unit based on the j group of equipment operating parameters of each production equipment to obtain the corresponding operation of each production equipment state vector. By correcting the state eigenvector with the largest state weight to eliminate the influence between production equipment, noise removal can be achieved when determining the operating state vector, and the working state of each production equipment can be accurately determined to reduce the difficulty of production equipment scheduling.

The invention discloses a wind electricity probability prediction-based dispatch demonstration method and belongs to the electric power industry regulation technical field. The method includes the following steps: S1, establishing a wind electricity power probability prediction model based on component sparse Bayesian learning, and obtaining prediction information according to the wind electricity power probability prediction model; S2, establishing a model constraining electric power probability dispatch upper limit and considering system risks according to the prediction information, and formulating a power generation plan arrangement strategy according to the result of an electric power system probability dispatch model; S3, performing wind electricity probability prediction according to the power generation plan arrangement strategy, and obtaining and demonstrating the result of wind electricity probability prediction; and S4, realizing wind electricity probability prediction-based dispatch according to the demonstration result. With the method the invention adopted, the errors of short-term wind field output power value prediction values can be decreased, and the accuracy of power generation plans can be improved, and risks that power generation of a power system does not satisfy loads can be eliminated, nearly 10% of wind electricity absorption volume can be increased, and the economic benefits of power grid enterprises can be increased.

The invention provides a lateral error translation modification method of a wind electricity power forecast system. The method includes the steps that wind electricity power historical forecast and actual measurement data sequences are collected and are classified according to four seasons, and data of each season every year are divided into three kinds of power trend sections, that is a continuous power increasing section, a continuous power lowering section and an unchanged power section; duration time sequences of each power trend section are obtained according to power trend duration models, probability statistic analysis is carried out to obtain a macroscopic centralized distribution value, a wind electricity power forecast lateral error model is input to obtain a lateral error of each season every year, and a weighted average value of the lateral errors of each identical season of N years serves as a lateral error modification value of each season; translation modification is carried out on forecast power sequences supplied to power grid dispatching by the wind electricity power forecast system by means of the weighed average value, and the translation modification effect is evaluated on the whole. The method has common adaptability for power short-term or exceedingly-short-term forecast systems of wind electricity, photoelectricity and the like and can further improve precision of various power forecast systems.