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In order to achieve high-precision short-term wind power prediction, a short-term wind power combination prediction method based on improved entropy weight method and SECEEMD (sample entropy complementary ensemble empirical mode decomposition) was proposed, taking into account the volatility of wind power data and the impact of multi-dimensional meteorological data on wind power prediction. First of all, a comprehensive correlation analysis model was proposed, which combined multiple feature selection methods to achieve comprehensive evaluation of multi-dimensional meteorological features, accurately screened meteorological features with high correlation with wind power, and improved prediction accuracy. Secondly, in view of the problem that CEEMD has too many decomposition components and increases the degree of modal aliasing, the SECEEMD decomposition algorithm was proposed to reduce the number of components and the degree of modal aliasing, while improving the training speed of the model. Then, NWPLSTM(numerical weather prediction-long short term memory) and SECEEMD-BP prediction models were established respectively, and the structure of long short-term memory neural network and BP neural network were optimized by Bayesian optimization algorithm. Finally, the optimal weight combination was found through the improved entropy weight method. The experiment takes the wind power data and meteorological data of Biliuhe wind farm in Inner Mongolia as the experimental data. It has been verified that the prediction model proposed can greatly improve the prediction accuracy. Compared with the general prediction model, R 2 has increased by 4% and 0. 6% respectively, and MAE has decreased by 44% and 1. 1% respectively. The experiment proves that the wind power prediction method proposed in this paper has higher prediction accuracy and faster training speed, and is more suitable for wind power prediction. [ABSTRACT FROM AUTHOR]

In recent years, deep learning algorithms have been widely used to generate various types of data. The mapping relationship between logging data and NMR T2 spectrum was analyzed, then random forest and long short-term memory (LSTM) network model were used to realize the reconstruction of NMR T2 spectrum. T2 spectrum obtained at each depth of NMR logging shows morphological changes through different number of points in different time series. Random forest algorithm can process high-dimensional NMR T2 spectrum data without feature selection, while LSTM can well control the influence of neurons at different depths on T2 spectrum distribution points. After optimizing the parameters of the two network models, the prediction results of the same well were compared and analyzed. Well logging data of A Oilfield in CNOOC were selected as examples for method testing. Firstly, the correlation between T2 geometric mean and logging curve was analyzed by using gray correlation degree algorithm. The logging curve whose correlation is higher than the set value was selected, and the normalized scaling of the logging curve at 0~1 was used as the input of the random forest and LSTM models to predict the distribution law of T2 spectrum morphology in the same stratum and compare the advantages and disadvantages of the algorithm. After comparing the NMR T2 spectrum processed by the software with the predicted results, the reasons for the differences between them were analyzed. The results show that the data predicted by LSTM neural network model is more consistent with the real stratum data than the random forest algorithm, and the consistency can reach more than 90%. [ABSTRACT FROM AUTHOR]

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Double bottom blowing continuous copper smelting process has the characteristics of continuity and instant, and its pmduc-lion operation parameter control is more complex than other copper smelting pmcesses. In order to ensure the stability of pmduct quality and optamaization of funtace condition, it is mx,essary to study the key rotund parameters and their influence law of double bottom blowing continuous copper smelling process. Using the MetCal desk computing platform, a metallurgical calculation model of double bottom blo-wing c s copper smelting process was established based on the principle of conservation of material flow and energy flow. On this basis, through a single adjustment of oxygen rich air volume, quartz stone volume, electrolysis remnant pole volume and other parame-ters, the influence on the key smelting paramders "mane grade, iron-silicon ratio and temperature of smelting slag " and the key conver-ting parameters "coarse copper mule, iconsilicon ratio and temperature of converting slag " in double bottom blowing continuous copper smelting pmcess were investigated. The research results show that the grade of matte is positively correlated with the amount of smelting quartz and oxygenized air, but negatively correlated with the amount of coal. The ironsilicon ratio of smelting slag is negatively correlated with the amount of quartz and coal. The smelting slag temperature is positively correlated with the amount of oxygenized air and coal. The crude copper grade is positively correlated with the amount of oxygen-rich air and electrolysis renmant pole. The iron-silicon ratio of con-verting slag was positively correlated with the electrolysis remnant pole and negatively correlated with the amount of converting quartz. The converting slag temperature is positively correlated with the amount of oxygenized air and electrolysis remanant pole. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Industry is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)