Generation of simulated wind data using an intelligent algorithm

R. Weissbach, Wen-li Wang, B. M. Hodge, M. H. Tang, J. Sonnenmeier

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 languageEnglish (US)
Title of host publication2014 North American Power Symposium, NAPS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959044
DOIs
StatePublished - Nov 21 2014
Event2014 North American Power Symposium, NAPS 2014 - Pullman, United States
Duration: Sep 7 2014Sep 9 2014

Publication series

Name2014 North American Power Symposium, NAPS 2014

Other

Other2014 North American Power Symposium, NAPS 2014
CountryUnited States
CityPullman
Period9/7/149/9/14

Fingerprint

Energy storage
Wind power
Probability density function
Time series
Neural networks

All Science Journal Classification (ASJC) codes

  • Fuel Technology
  • Energy Engineering and Power Technology

Cite this

Weissbach, R., Wang, W., Hodge, B. M., Tang, M. H., & Sonnenmeier, J. (2014). Generation of simulated wind data using an intelligent algorithm. In 2014 North American Power Symposium, NAPS 2014 [6965405] (2014 North American Power Symposium, NAPS 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NAPS.2014.6965405
Weissbach, R. ; Wang, Wen-li ; Hodge, B. M. ; Tang, M. H. ; Sonnenmeier, J. / Generation of simulated wind data using an intelligent algorithm. 2014 North American Power Symposium, NAPS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. (2014 North American Power Symposium, NAPS 2014).
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Weissbach, R, Wang, W, Hodge, BM, Tang, MH & Sonnenmeier, J 2014, Generation of simulated wind data using an intelligent algorithm. in 2014 North American Power Symposium, NAPS 2014., 6965405, 2014 North American Power Symposium, NAPS 2014, Institute of Electrical and Electronics Engineers Inc., 2014 North American Power Symposium, NAPS 2014, Pullman, United States, 9/7/14. https://doi.org/10.1109/NAPS.2014.6965405

Generation of simulated wind data using an intelligent algorithm. / Weissbach, R.; Wang, Wen-li; Hodge, B. M.; Tang, M. H.; Sonnenmeier, J.

2014 North American Power Symposium, NAPS 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6965405 (2014 North American Power Symposium, NAPS 2014).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Weissbach R, Wang W, Hodge BM, Tang MH, Sonnenmeier J. Generation of simulated wind data using an intelligent algorithm. In 2014 North American Power Symposium, NAPS 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6965405. (2014 North American Power Symposium, NAPS 2014). https://doi.org/10.1109/NAPS.2014.6965405