Published January 2018 | Version v1
Journal article

A new method for wind speed forecasting based on copula theory

  • 1. Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210023 (China)
  • 2. Shandong Electric Power Engineering Consulting Institute Corp, Ltd., Jinan 250013 (China)
  • 3. Development Research Center of the Ministry of Water Resources, Beijing 100038 (China)
  • 4. Nanjing Hydraulic Research Institute, Nanjing 210029 (China)

Description

Highlights: • A copula method is developed to determine the representative wind speed in wind farm. • The method proposed can determine the joint distribution without assuming the variables to be independent. • The representative wind speed obtained by the conditional distribution is more reasonably. How to determine representative wind speed is crucial in wind resource assessment. Accurate wind resource assessments are important to wind farms development. Linear regressions are usually used to obtain the representative wind speed. However, terrain flexibility of wind farm and long distance between wind speed sites often lead to low correlation. In this study, copula method is used to determine the representative year's wind speed in wind farm by interpreting the interaction of the local wind farm and the meteorological station. The result shows that the method proposed here can not only determine the relationship between the local anemometric tower and nearby meteorological station through Kendall's tau, but also determine the joint distribution without assuming the variables to be independent. Moreover, the representative wind data can be obtained by the conditional distribution much more reasonably. We hope this study could provide scientific reference for accurate wind resource assessments.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.envres.2017.09.034

Additional details

Identifiers

DOI
10.1016/j.envres.2017.09.034;
PII
S0013935117315554;

Publishing Information

Journal Title
Environmental Research
Journal Volume
160
Journal Page Range
p. 365-371
ISSN
0013-9351
CODEN
ENVRAL

INIS

Country of Publication
United States
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53027092
Subject category
S54: ENVIRONMENTAL SCIENCES;
Descriptors DEI
COOLING TOWERS; FLEXIBILITY; FORECASTING; METEOROLOGY; POWER TRANSMISSION TOWERS; RESOURCE ASSESSMENT; WIND TURBINE ARRAYS
Descriptors DEC
MECHANICAL PROPERTIES; MECHANICAL STRUCTURES; TENSILE PROPERTIES

Optional Information

Copyright
Copyright (c) 2017 Elsevier Inc. All rights reserved.