Under the background of big data, the use of massive online data to improve the real-time characteristics and reliability of wind power prediction and to reduce the impact of wind farms on the power grid makes the power supply and demand balance important problems to solve. This paper provides a new solution for short-term wind power forecasting to address these problems. In this paper, an improved random forest short-term prediction model based on the hierarchical output power is proposed, and it is used to forecast the power output of a real wind farm located in Northwest China. First, a chi-square test is adopted to discretize the power data to divide the large-scale training data and remove abnormal data. The novelty of this study is the establishment of a classification model with the output wind power as the classification target and the use of Poisson re-sampling to replace the bootstrap method of the random forest, that is, to improve the training speed of the random forest algorithm. The results indicate that the proposed technique can estimate the output wind power with an MSE of 0.0232, and the comparison illustrates the effectiveness and superiority of the proposed method.

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Accurate wind speed forecast plays an important role in the safe and stable operation of large-scale wind power integrated grid system. In this paper, a new hybrid model for short-term wind speed forecasting based on hyperparameter optimization and error correction is proposed, where the forecasting period is 5, 10, and 15 min, respectively, for three sites. The empirical wavelet transform is used to decompose the original wind speed series. Then, the Elman neural network and kernel extreme learning machine, which adopt Bayesian optimization algorithm for hyperparameter optimization, are used as predictors for wind speed prediction and error processing, respectively. In addition, a new error correction model using wind speed as model input is proposed. In order to verify the performance of the proposed model, three datasets collected from different real-world wind farms in Gansu and Xinjiang were considered as a case study to comprehensively evaluate the prediction performance of ten forecasting models. The results reveal that the proposed model has higher prediction accuracy and better prediction performance than the contrast models.

Zebrafish provide a convenient and unique model for studying human cancers, owing to the high similarity between zebrafish and human genomes, the availability of genetic manipulation technologies, and the availability of large numbers and transparency of zebrafish embryos. Many researchers have recently used zebrafish cancer models to examine the functions of macrophages in tumorigenesis, tumor growth and metastasis. Here, we present evidence that zebrafish cancer cells produce signals that are conserved with respect to those in humans and lead to the recruitment of heterogeneously activated macrophages in response to specific tumor types and tumorigenic stages, thereby promoting cancer initiation and progression. We also summarize how cancer cells interact with macrophages, emphasizing live imaging studies for visualization of dynamic material interchange.