Daily Wind Speed Forecasting Through Hybrid AR-ANN and AR-KF Models
DOI:
https://doi.org/10.11113/jt.v72.3946Keywords:
Wind speed forecasting, ARIMA, KF, ANN, hybrid forecastingAbstract
The nonlinearity and the chaotic fluctuations in the wind speed pattern are the reasons of inaccurate wind speed forecasting results using a linear autoregressive integrated moving average (ARIMA) model. The inaccurate forecasting of ARIMA model is a problem that reflects the uncertainty of modelling process. This study aims to improve the accuracy of wind speed forecasting by suggesting more appropriate approaches. An artificial neural network (ANN) and Kalman filter (KF) will be used to handle nonlinearity and uncertainty problems. Once ARIMA model was used only for determining the inputs structures of KF and ANN approaches, using an autoregressive (AR) Instead of ARIMA may be resulted in more simplicity and more accurate forecasting. ANN and KF based on the AR model are called hybrid AR-ANN model and hybrid AR-KF model, respectively. In this study, hybrid AR-ANN and hybrid AR-KF models are proposed to improve the wind speed forecasting. The performance of ARIMA, hybrid AR-ANN, and hybrid AR-KF models will be compared to determine which had the most accurate forecasts. A case study will be carried out that used daily wind speed data from Iraq and Malaysia. Hybrid AR-ANN and AR-KF models performed better than ARIMA model while the hybrid AR-KF model was the most adequate and provided the most accurate forecasts. In conclusion, the hybrid AR-KF model will result in better wind speed forecasting accuracy than other approaches, while the performances of both hybrid models will be provided acceptable forecasts compared to ARIMA model that will provide ineffectual wind speed forecasts.
References
Benth, J. Š., and F. E. Benth. 2010. Analysis and Modelling of Wind Speed in New York. J. Appl. Stat. 37(6): 893–909.
Shi, J., X. Qu, and S. Zeng. 2011. Short-term Wind Power Generation Forecasting: Direct Versus Indirect ARIMA-based Approaches. Int. J. Green Energy. 8(1): 100–112.
Zhu, X., and M. G. Genton. 2012. Short-term Wind Speed Forecasting for Power System Operations. Int. Stat. Rev. 80(1): 2–23.
Cadenas, E., and W. Rivera. 2007. Wind Speed Forecasting in the South Coast of Oaxaca, México. Renew. Energy. 32(12): 2116–2128.
Cadenas, E., and W. Rivera. 2010. Wind Speed Forecasting in Three Different Regions of Mexico, Using a Hybrid ARIMA–ANN Model. Renew. Energy. 35(12): 2732–2738.
Pourmousavi-Kani, S. A., and M. M. Ardehali. 2011. Very Short-term Wind Speed Prediction: A New Artificial Neural Network–Markov Chain Model. Energy Conv. Manag. 52(1): 738–745.
Assareh, E., M. Behrang, M. Ghalambaz, A. Noghrehabadi, and A. Ghanbarzadeh. 2012. An Analysis of Wind Speed Prediction Using Artificial Neural Networks: A Case Study in Manjil, Iran. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 34(7): 636–644.
Bilgili, M., and B. Sahin. 2013. Wind Speed Prediction of Target Station from Reference Stations Data. Energy Sources Part A-Recovery Util. Environ. Eff. 35(5): 455–466.
Peng, H., F. Liu, and X. Yang. 2013. A Hybrid Strategy of Short Term Wind Power Prediction. Renew. Energy. 50: 590–595.
Li, G., and J. Shi. 2010. On Comparing Three Artificial Neural Networks for Wind Speed Forecasting. Appl. Energy. 87: 2313–2320.
Guo, Z., W. Zhao, L. Haiyan, and J. Wang. 2012. Multi-step Forecasting for Wind Speed Using A Modified EMD-based Artificial Neural Network Model. Renew. Energy. 37: 241–249.
Liu, H., H. Tian, and Y. Li. 2012. Comparison of Two New ARIMA-ANN and ARIMA-Kalman Hybrid Methods for Wind Speed Prediction. Appl. Energy. 98: 415–424.
Malmberg, A., U. Holst, and J. Holst. 2005. Forecasting Near-surface Ocean Winds with Kalman Filter Techniques. Ocean Eng. 32(3): 273–291.
Galanis, G., P. Louka, P. Katsafados, I. Pytharoulis, and G. Kallos. 2006. Applications of Kalman Filters Based on Non-linear Functions to Numerical Weather Predictions. Ann. Geophys. 24(10): 2451–2460.
Louka, P., G. Galanis, N. Siebert, G. Kariniotakis, P. Katsafados, I. Pytharoulis, and G. Kallos. 2008. Improvements in Wind Speed Forecasts for Wind Power Prediction Purposes Using Kalman Filtering. J. Wind Eng. Ind. Aerodyn. 96(12): 2348–2362.
Cassola, F., and M. Burlando. 2012. Wind Speed and Wind Energy Forecast Through Kalman Filtering of Numerical Weather Prediction Model Output. Appl. Energy. 99: 154–166.
Tatinati, S., and K. C. Veluvolu. 2013. A Hybrid Approach for Short-term Forecasting Of Wind Speed. Sci. World J. 2013.
Chen, K., and J. Yu. 2014. Short-term Wind Speed Prediction Using an Unscented Kalman Filter Based State-space Support Vector Regression Approach. Appl. Energy. 113: 690–705.
Liu, L.-M. 2006. Time Series Analysis and Forecasting. 2nd ed. Illinois, USA: Scientific Computing Associates Corp.
Zhang, G. P. 2003. Time Series Forecasting Using A Hybrid ARIMA and Neural Network Model. Neurocomputing. 50: 159–175.
Khashei, M., and M. Bijari. 2010. An Artificial Neural Network (P,D,Q) Model for Time Series Forecasting. Expert Syst. Appl. 37(1): 479–489.
Gould, P. G., A. B. Koehler, J. K. Ord, R. D. Snyder, R. J. Hyndman, and F. V. Araghi. 2008. Forecasting Time Series with Multiple Seasonal Patterns. Eur. J. Oper. Res. 191: 207–222.
Madsen, H. 2007. Time Series Analysis. CRC Press.
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