Enhancing Temporal Extrapolation of Wind Speed Using a Hybrid Technique: A Case Study in West Coast of Denmark
The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.
[1] Negnevitsky, M., Johnson, P., & Santoso, S. (2007). Short term wind power forecasting using hybrid intelligent systems. 2007 IEEE Power Engineering Society General Meeting. doi: 10.1109/pes.2007.385453.
[2] Hoolohan, V., Tomlin, A. S., & Cockerill, T. (2018). Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data. Renewable Energy, 126, 1043–1054. doi: 10.1016/j.renene.2018.04.019.
[3] Zhang, C., Wei, H., Zhao, X., Liu, T., & Zhang, K. (2016). A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 126, 1084–1092. doi: 10.1016/j.enconman.2016.08.086.
[4] Tascikaraoglu, A. and Uzunoglu, M., 2014. A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34, pp.243-254.
[5] Zhu, X. and Genton, M., 2012. Short-Term Wind Speed Forecasting for Power System Operations. International Statistical Review, 80(1), pp.2-23.
[6] Shao, H., Wei, H., Deng, X. and Xing, S., 2017. Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm. IET Renewable Power Generation, 11(4), pp.374-381.
[7] S. S. Soman, H. Zareipour, O. Malik and P. Mandal, "A review of wind power and wind speed forecasting methods with different time horizons," North American Power Symposium 2010, Arlington, TX, 2010, pp. 1-8, doi: 10.1109/NAPS.2010.5619586.
[8] Hu, J. and Wang, J., 2015. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy, 93, pp.1456-1466.
[9] Erdem Ergin, Shi Jing. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 2011;88:1405e14.
[10] Liua Hui, Tian Hong-qi, Li Yan-fei. An EMD-recursive ARIMA method to predict wind speed. J Wind End Ind Aerodyn 2015;141:27e38.
[11] Katris Christos, Daskalaki Sophia. Comparing forecasting approaches for Internet traffic. Expert Syst Appl 30 November 2015;42(21):8172e83.
[12] Li Yunhua, Ling Lina, Chen Jiantao. Combined grey prediction fuzzy control law with application to road tunnel ventilation system. J Appl Res Technol 2015;13:313e20.
[13] Hu Jianming, Wang Jianzhou, Zeng Guowei. A hybrid forecasting approach applied to wind speed time series. Renew Energy 2013;60:185e94.
[14] Zhang, C., Wei, H., Zhao, X., Liu, T. and Zhang, K., 2016. A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 126, pp.1084-1092.
[15] Cadenas, E., Rivera, W., Campos-Amezcua, R. and Cadenas, R., 2015. Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México. Neural Computing and Applications, 27(8), pp.2417-2428.
[16] Petelin, D., n.d. Gaussian Processes For Machine Learning.
[17] Alamaniotis, M. and Karagiannis, G., 2017. Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research, 5(3), pp.1-14.
[18] Parussini, L., Venturi, D., Perdikaris, P. and Karniadakis, G., 2017. Multi-fidelity Gaussian process regression for prediction of random fields. Journal of Computational Physics, 336, pp.36-50.
[19] Raissi, M. and Em Karniadakis, G., 2016. Deep Multi-fidelity Gaussian Processes. ArXiv,.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
[20] Hu J, Wang J. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 2015;1456:1466-93.
[21] Catalao JPS, Pousinho HMI, Mendes VMF. Short-term wind power forecasting in portugal by neural networks and wavelet transforms. Renew Energy 2011;36:1245-51.
[1] Negnevitsky, M., Johnson, P., & Santoso, S. (2007). Short term wind power forecasting using hybrid intelligent systems. 2007 IEEE Power Engineering Society General Meeting. doi: 10.1109/pes.2007.385453.
[2] Hoolohan, V., Tomlin, A. S., & Cockerill, T. (2018). Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data. Renewable Energy, 126, 1043–1054. doi: 10.1016/j.renene.2018.04.019.
[3] Zhang, C., Wei, H., Zhao, X., Liu, T., & Zhang, K. (2016). A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 126, 1084–1092. doi: 10.1016/j.enconman.2016.08.086.
[4] Tascikaraoglu, A. and Uzunoglu, M., 2014. A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34, pp.243-254.
[5] Zhu, X. and Genton, M., 2012. Short-Term Wind Speed Forecasting for Power System Operations. International Statistical Review, 80(1), pp.2-23.
[6] Shao, H., Wei, H., Deng, X. and Xing, S., 2017. Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm. IET Renewable Power Generation, 11(4), pp.374-381.
[7] S. S. Soman, H. Zareipour, O. Malik and P. Mandal, "A review of wind power and wind speed forecasting methods with different time horizons," North American Power Symposium 2010, Arlington, TX, 2010, pp. 1-8, doi: 10.1109/NAPS.2010.5619586.
[8] Hu, J. and Wang, J., 2015. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy, 93, pp.1456-1466.
[9] Erdem Ergin, Shi Jing. ARMA based approaches for forecasting the tuple of wind speed and direction. Appl Energy 2011;88:1405e14.
[10] Liua Hui, Tian Hong-qi, Li Yan-fei. An EMD-recursive ARIMA method to predict wind speed. J Wind End Ind Aerodyn 2015;141:27e38.
[11] Katris Christos, Daskalaki Sophia. Comparing forecasting approaches for Internet traffic. Expert Syst Appl 30 November 2015;42(21):8172e83.
[12] Li Yunhua, Ling Lina, Chen Jiantao. Combined grey prediction fuzzy control law with application to road tunnel ventilation system. J Appl Res Technol 2015;13:313e20.
[13] Hu Jianming, Wang Jianzhou, Zeng Guowei. A hybrid forecasting approach applied to wind speed time series. Renew Energy 2013;60:185e94.
[14] Zhang, C., Wei, H., Zhao, X., Liu, T. and Zhang, K., 2016. A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management, 126, pp.1084-1092.
[15] Cadenas, E., Rivera, W., Campos-Amezcua, R. and Cadenas, R., 2015. Wind speed forecasting using the NARX model, case: La Mata, Oaxaca, México. Neural Computing and Applications, 27(8), pp.2417-2428.
[16] Petelin, D., n.d. Gaussian Processes For Machine Learning.
[17] Alamaniotis, M. and Karagiannis, G., 2017. Integration of Gaussian Processes and Particle Swarm Optimization for Very-Short Term Wind Speed Forecasting in Smart Power. International Journal of Monitoring and Surveillance Technologies Research, 5(3), pp.1-14.
[18] Parussini, L., Venturi, D., Perdikaris, P. and Karniadakis, G., 2017. Multi-fidelity Gaussian process regression for prediction of random fields. Journal of Computational Physics, 336, pp.36-50.
[19] Raissi, M. and Em Karniadakis, G., 2016. Deep Multi-fidelity Gaussian Processes. ArXiv,.W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
[20] Hu J, Wang J. Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 2015;1456:1466-93.
[21] Catalao JPS, Pousinho HMI, Mendes VMF. Short-term wind power forecasting in portugal by neural networks and wavelet transforms. Renew Energy 2011;36:1245-51.
@article{"International Journal of Electrical, Electronic and Communication Sciences:80052", author = "B. Elshafei and X. Mao", title = "Enhancing Temporal Extrapolation of Wind Speed Using a Hybrid Technique: A Case Study in West Coast of Denmark", abstract = "The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.
", keywords = "Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.", volume = "14", number = "12", pages = "380-6", }
