Abstract
This paper develops and evaluates wind speed prediction models for Jeju City based on artificial neural networks, aiming at more integrating renewable energies into the power system. 3-layer neural network models take the appropriate training pattern from the history data accumulated during the last 10 years. First, the monthly model classifies the months into rainy, winter, and remaining periods according to the error size. The auto correlation function analysis confirms that the modeling error can be considered as white noise. Next, a 5-day forecast model takes wind speed for 5 previous days as inputs and generates 5 outputs for next 5 days. The 1-day advance tracing error is 1.28 mps (meter per seconds) in March and 0.66 mps in August on average. In addition, the prediction error is 0.45 mps for the next first day forecast and 1.99 mps for the 5-th day forecast in July.
Keywords
This research was supported by the MKE (The Ministry of Knowledge Economy), Republic of Korea, under IT/SW Creative research program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2012-(H0502-12-1002)).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Wang, C., Lu, Z., Qiao, Y.: Modeling of Wind Pattern and Its Application in Wind Speed Forecasting. In: International Conference on Sustainable Power Generation and Supply, pp. 1–6 (2009)
Lee, J., Kim, H., Park, G., Kang, M.: Energy Consumption Scheduler for Demand Response Systems in the Smart Grid. Journal of Information Science and Engineering 28, 955–969 (2012)
Annakkage, U., Jacobson, D., Muthumum, D.: Method for Studying and Mitigating the Effects of Wind Variability on Frequency Regulation. In: CIGRE/IEEE PES Join Symposium on Integration of Wide-Scale Renewable Resources into the Power Delivery System (2009)
Zhou, H., Hwang, M., Wu, X.: Forecast of Wnd Speed and Power of Wind Generator based on Pattern Recognition. In: Int. Conference on Industrial Mechatronics and Automation, pp. 504–508 (2009)
Nissen, S.: Neural Network Made Simple. Software 2.0 (2005)
Abdel-Karim, N., Small, M., Ilic, M.: Short Term Wind Speed Prediction by Finite and Infinite Impulse Response Filters: A State Space Model Representation using Distcrete Markov Rrocess. In: IEEE Bucharest Power Tech. Conference (2009)
Jursa, R.: Variable Selection for Wind Power Prediction using Particle Swarm Optimization. In: 9th Annual Conference on Genetic and Evolutionary Computation, pp. 2059–2065 (2007)
Lee, J., Park, G., Kim, E., Kim, Y., Lee, I.: Wind Speed Modeling based on Artificial Neural Networks for Jeju Area. International Journal of Control and Automation 5, 81–88 (2012)
Methaprayoon, K., Yingvivatanapong, C., Lee, W., Liao, J.: An Integration of ANN Wind Power Estimation into Unit Commitment Considering the Forecasting Uncertainty. IEEE Transactions on Industry Applications 43, 1441–1448 (2007)
Lee, J., Park, G., Kim, S., Park, C.: Monitoring-based Temporal Prediction of Power Entities in Smart Grid Cities. To appear at ACM Research in Advanced Computer Systems (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, J., Park, GL., Kim, EH. (2012). Development of Wind Speed Prediction Model in Jeju City. In: Kim, Th., Ramos, C., Abawajy, J., Kang, BH., Ślęzak, D., Adeli, H. (eds) Computer Applications for Modeling, Simulation, and Automobile. MAS ASNT 2012 2012. Communications in Computer and Information Science, vol 341. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35248-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-35248-5_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35247-8
Online ISBN: 978-3-642-35248-5
eBook Packages: Computer ScienceComputer Science (R0)