Generation of Simulated Wind Data using an Intelligent Algorithm

Brian Hodge, R. Weissbach, W.L. Wang, M.H. Tang, J. Sonnenmeier

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations


Wind energy is becoming an important renewable resource for a residence with no access to grid-based power. However, its variable and uncertain nature requires the use of energy storage to ensure high reliability of power to the residential loads. Therefore, variations in the wind profile need to be considered more accurately to predict energy storage requirements. Various models aim to develop simulated sets of wind data to meet this purpose, including Numerical Weather Prediction (NWP), statistical time series approaches such as Auto-Regressive Moving Average (ARMA) models, Artificial Neural Networks (ANN), and Markov models. Unfortunately, generating statistically significant wind data from these models for a single location has proven to be difficult. In this paper, an intelligent algorithm is developed to create and impose simulated wind data to tackle variations at a single site. It first incorporates the Markov approach to identify the general trend of the measured data and ensure fidelity of the Probability Density Function (PDF). A learning methodology is then employed to ensure the trend also satisfies seasonal and diurnal constraints.

Original languageAmerican English
Number of pages6
StatePublished - 21 Nov 2014
Event2014 North American Power Symposium, NAPS 2014 - Pullman, United States
Duration: 7 Sep 20149 Sep 2014


Conference2014 North American Power Symposium, NAPS 2014
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

NREL Publication Number

  • NREL/CP-5D00-60981


  • energy storage
  • Markov model
  • simulated annealing
  • wind energy


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