Deep belief network based k-means cluster approach for short-term wind power forecasting

Wang, Kejun; Qi, Xin; Liu, Hongda; Song, Jiuzhou

doi:10.1016/j.energy.2018.09.118

Cited by 229 publications

(57 citation statements)

References 34 publications

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“…The k-means algorithm identifies k centroids and then allocates every data point to the nearest cluster [53]. This algorithm has been used in a study done by Wang et al [55], where the k-means clustering algorithm was used to find the largest historical samples that had the greatest influence on forecasting accuracy to improve the efficiency of the proposed model. A method for clustering was proposed by Deng et al [52], which used a Weibull distribution to establish that an unclustered dataset P can be represented using Equation 9:…”

Section: Wind Clusteringmentioning

confidence: 99%

Seasonal Wind Energy Characterization in the Gulf of Mexico

2019

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In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980–2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency.

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Section: Wind Clusteringmentioning

confidence: 99%

Seasonal Wind Energy Characterization in the Gulf of Mexico

2019

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“…The silhouette method is a measure of the similarity of an object to its cluster compared to other clusters. It ranges from -1 to 1, where a large value means that the object is highly matched to its own cluster and low matched to the other clusters (Wang et al, 2018). If the majority of objects have high values, then the clustering result is considered successful.…”

Section: ) Silhouette Methodsmentioning

confidence: 99%

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

ElMenshawy¹,

Helmy²,

El-Tazi³

2019

Journal of Computer Science

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Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and realtime information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.

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“…The results validate that their method outperforms the benchmark approaches in terms of reliability, sharpness and overall skill. In [189] a DBN model in conjunction with the k-means clustering algorithm is proposed for wind power forecasting. The proposed approach demonstrated significantly increased forecasting accuracy compared to a BPANN and a Morlet wavelet neural network on data-sets obtained from a wind farm in Spain.…”

Section: F Power Systemsmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

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Deep belief network based k-means cluster approach for short-term wind power forecasting

Cited by 229 publications

References 34 publications

Seasonal Wind Energy Characterization in the Gulf of Mexico

Seasonal Wind Energy Characterization in the Gulf of Mexico

A Clustering based Approach for Contextual Anomaly Detection in Internet of Things

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

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