Precise wind speed prediction is increasingly practical for sustained and stable energy utilization considering the growing portion of in global electric grid. Although plenty forecasting approaches have been devoted to improving performance, majority neglected error information, integrated value every component with simple ensemble approaches, ignored stability, which may make results poor. Considering above drawbacks, a two-stage system performed our study based on data preprocessing...

With the increasing capacity of grid-connected wind power systems, forecasting has become a major research problem in systems under background dual-carbon policy, and it is great practical significance to develop reliable methods. In order overcome difficulties data noise reduction, feature extraction uncertainty estimation, new system proposed. The improved variational mode decomposition algorithm used denoise data, overcoming subjective parameter selection traditional method. time...

In practice, classification problems have appeared in many scientific fields, including finance, medicine and industry. It is critically important to develop an effective accurate model. Although numerous useful classifiers been proposed, they are unstable, sensitive noise slow computation. To overcome these drawbacks, the combination of feature selection techniques with traditional machine learning models great help. this paper, a novel method called opposition-based seagull optimization...

Wind speed forecasting helps to increase the efficacy of wind farms and prompts comparative superiority energy in global electricity system. Many theories have been widely applied forecast speed, which is nonlinear, unstable. Current strategies can be various time series. However, some models neglect prerequisite data preprocessing objective simultaneously optimizing accuracy stability, results poor forecast. In this research, we developed a combined strategy that includes several...

This paper introduced the personalized recommendation technology to news system. Especially, in order meet demand of users' personality and ease problem data sparse, research work proposed hybrid collaborative filtering algorithm based on recommendation. By improving correlation coefficient formula via adding hot parameter when calculating similarity users, is used forecast ratings make user-rating matrix non-zero values. Experimental results illustrated that can effectively increase...

Deep Learning (DL) has been broadly applied to solve big data problems in biomedical fields, which is most successful image processing. Recently, many DL methods have analyze genomic studies. However, usually too small a sample size fit complex network. They do not common structural patterns like images utilize pre-trained networks or take advantage of convolution layers. The concern overusing motivates us evaluate methods' performance versus popular non-deep Machine (ML) for analyzing with...

This paper introduced the personalized recommendation technology to news system. In order meet demand of users' personality and ease problem data sparse, research work proposed hybrid collaborative filtering algorithm based on recommendation. It improved correlation coefficient formula by adding hot parameter when calculating similarity users, then used forecast ratings make user-rating matrix non-zero values. Experimental results illustrated that can effectively increase accuracy stability...

In recent decades, the integration of solar energy sources has gradually become main challenge for global consumption. Therefore, it is essential to predict radiation in an accurate and efficient way when estimating outputs system. Inaccurate predictions either cause load overestimation that results increased cost or failure gather adequate supplies. However, forecasting a challenging task because resources are intermittent uncontrollable. To tackle this difficulty, several machine learning...

Abstract Electricity price forecasting (EPF) is an emergent research domain that focuses on the future electricity market both deterministically and probabilistically. EPF has attracted enormous interest from practitioners scholars since deregulation of power wide applications renewable energy sources, such as wind solar energy. However, accurately efficiently extremely challenging task because its high volatility, randomness, fluctuation. Although quantile regression averaging (QRA) been...

In order to implement the "dual carbon" goal and sustainable development strategy, this paper proposes a method for interval prediction of wind power through Bayesian-optimized bidirectional long short-term memory network. By combining existing deep learning algorithms, CEEMDAN-BO-QRBiLSTM model was constructed. The empirical analysis results show that newly established is feasible has higher accuracy better effect than other models.

This paper proposed an anomaly detection method that can be used in high speed network backbone. To adapt for the need of online cost-sensitive data processing, some attributes are extracted from header packets and recorded by sketching a fixed time window. Based on non-extensive entropy with different parameters, original distribution values is decomposed to dimensional features enlarge characteristics small amount hidden large normal data. Using these detailed features, model based random...

Main microblog research is focus on the structural analysis of social networks, rather than text and topic analysis. Traditional detection methods could not be applied due to short features characteristics. We taken advantage availability latent dirichlet allocation (LDA) expand feature space, used frequency statistics for our ranking, improved it based nontext element data word element. into account both context similarity semantic in order make possible that traditional clustering method...

Nowadays, medical information is increasingly used many hospitals around the world. Then, need of sharing from different sources an obvious consequence such proliferation systems. Unfortunately, integrating not a trivial issue, because data and knowledge exchange among users systems present challenges. We must deal with heterogeneity problem, which increase complexity integration approaches. This paper provides advantageous environment for interoperability interoperation, according to...

In recent years, solar energy has attracted a great deal of attentions from scientific researchers because it is clean and renewable form energy. To make good use energy, an effective way to forecast radiation essential guarantee the reliability grid‐connected photovoltaic installations. Although artificial neural network (ANN) importance, irrelevant variables are utilized which results in complex model intractable computation cost. remove these variables, combination variable selection...