scholarly journals Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms

Energy ◽  
2019 ◽  
Vol 186 ◽  
pp. 115704 ◽  
Author(s):  
Xiuxing Yin ◽  
Xiaowei Zhao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Wind Farm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Farm ◽  
Multi Objective

Operational Data to Maintenance Optimization: Closing the Loop in Offshore Wind O&M

2018 ◽  
Author(s):  
Alexios Koltsidopoulos Papatzimos ◽  
Tariq Dawood ◽  
Philipp R. Thies
Keyword(s):  
Machine Learning ◽  
Wind Farm ◽  
Service Providers ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Offshore Wind ◽  
Sensor Data ◽  
Support Vector ◽  
Offshore Wind Turbine ◽  
Offshore Wind Farm

Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operation and maintenance (O&M) of these assets has to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous data related to O&M processes. These data contain rich information about the condition of the assets, which is rarely fully utilized by the operators and service providers. Academic and industrial research and development efforts have led to a suite of tools trying to apply sensor data and build machine learning models to diagnose, trend and predict component failures. This study presents a decision support framework incorporating a range of different supervised and un-supervised learning algorithms. The aim is to provide guidance for asset owners on how to select the most relevant datasets, apply and choose the different machine learning algorithms and how to integrate the data stream with daily maintenance procedures. The presented methodology is tested on a real case example of an offshore wind turbine gearbox replacement at Teesside offshore wind farm. The study uses k-nearest neighbour (kNN) and support vector machine (SVM) algorithms to detect the fault using supervisory control and data acquisition (SCADA) data and an autoregressive model for the vibration data of the condition monitoring system (CMS). The implementation of all the algorithms has resulted in an accuracy higher than 94%. The results of this paper will be of interest to offshore wind farm developers and operators to streamline and optimize their O&M planning activities for their assets and reduce the associated costs.

Efficient and Rapid Machine Learning Algorithms for Big Data and Dynamic Varying Systems

2017 ◽  
Vol 47 (10) ◽  
pp. 2625-2626 ◽  
Author(s):  
Fuchun Sun ◽  
Guang-Bin Huang ◽  
Q. M. Jonathan Wu ◽  
Shiji Song ◽  
Donald C. Wunsch II
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

Machine Learning Algorithms for Big Data

2019 ◽  
pp. 195-215
Author(s):  
C.S.R. Prabhu ◽  
Aneesh Sreevallabh Chivukula ◽  
Aditya Mogadala ◽  
Rohit Ghosh ◽  
L.M. Jenila Livingston
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

Essentiality of Machine Learning Algorithms for Big Data Computation

2016 ◽  
pp. 156-167
Author(s):  
Manjunath Thimmasandra Narayanapppa ◽  
T. P. Puneeth Kumar ◽  
Ravindra S. Hegadi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Large Scale ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Computational Environment ◽  
Large Scale Data Processing

Recent technological advancements have led to generation of huge volume of data from distinctive domains (scientific sensors, health care, user-generated data, finical companies and internet and supply chain systems) over the past decade. To capture the meaning of this emerging trend the term big data was coined. In addition to its huge volume, big data also exhibits several unique characteristics as compared with traditional data. For instance, big data is generally unstructured and require more real-time analysis. This development calls for new system platforms for data acquisition, storage, transmission and large-scale data processing mechanisms. In recent years analytics industries interest expanding towards the big data analytics to uncover potentials concealed in big data, such as hidden patterns or unknown correlations. The main goal of this chapter is to explore the importance of machine learning algorithms and computational environment including hardware and software that is required to perform analytics on big data.

What is Machine Learning? A Primer for the Epidemiologist

2019 ◽  
Author(s):  
Qifang Bi ◽  
Katherine E Goodman ◽  
Joshua Kaminsky ◽  
Justin Lessler
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Computer Science ◽  
Research Methods ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Epidemiologic Research ◽  
Learning Techniques ◽  
Applications Of Machine Learning

Abstract Machine learning is a branch of computer science that has the potential to transform epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new tools to tackle problems for which classical methods are not well-suited. In order to critically evaluate the value of integrating machine learning algorithms and existing methods, however, it is essential to address language and technical barriers between the two fields that can make it difficult for epidemiologists to read and assess machine learning studies. Here, we provide an overview of the concepts and terminology used in machine learning literature, which encompasses a diverse set of tools with goals ranging from prediction to classification to clustering. We provide a brief introduction to 5 common machine learning algorithms and 4 ensemble-based approaches. We then summarize epidemiologic applications of machine learning techniques in the published literature. We recommend approaches to incorporate machine learning in epidemiologic research and discuss opportunities and challenges for integrating machine learning and existing epidemiologic research methods.

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

2019 ◽  
Vol 24 (34) ◽  
pp. 3998-4006
Author(s):  
Shijie Fan ◽  
Yu Chen ◽  
Cheng Luo ◽  
Fanwang Meng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Precision Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Accelerated Studies ◽  
Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

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