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A comparison between reconstruction methods for generation of synthetic time series applied to wind speed simulation
Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India. Department of Engineering-Renewable Energy and Thermodynamics, Aarhus University, Denmark..
Departamento de Enxeñería Eléctrica-Universidade de Vigo, Campus de Lagoas-Marcosende, Vigo, Spain..
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Mining and Geotechnical Engineering.ORCID iD: 0000-0002-6790-2653
Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam..
2019 (English)In: IEEE Access, E-ISSN 2169-3536Article in journal (Refereed) Epub ahead of print
Abstract [en]

Wind energy is an attractive renewable sources and its prediction is highly essential for multiple applications. Over the literature, there are several studies have been focused on the related researches of synthetic wind speed data generation. In this research, two reconstruction methods are developed for synthetic wind speed time series generation. The modeling is constructed based on different processes including independent values generation from the known probability distribution function, rearrangement of random values and segmentation. They have been named as Rank-wise and Step-wise reconstruction methods. The proposed methods are explained with the help of a standard time series and the examination on wind speed time series collected from Galicia, the autonomous region in the northwest of Spain. Results evidenced the potential of the developed models over the state-of-the-art synthetic time series generation methods and demonstrated a successful validation using the means of mean and median wind speed values, autocorrelations, probability distribution parameters with their corresponding histograms and confusion matrix. Pros and cons of both methods are discussed comprehensively.

Place, publisher, year, edition, pages
UK: IEEE, 2019.
Keywords [en]
energy sustainability, synthetic data, time series, wind speed, wind energy.
National Category
Engineering and Technology Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-76072DOI: 10.1109/ACCESS.2019.2941826OAI: oai:DiVA.org:ltu-76072DiVA, id: diva2:1353096
Available from: 2019-09-20 Created: 2019-09-20 Last updated: 2019-09-23Bibliographically approved

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Citation style
  • apa
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