EFFECT OF TIME HORIZON ON WIND SPEED PREDICTION WITH ANN

Year 2018, , 1770 - 1779, 20.12.2017

Şaban Pusat

https://doi.org/10.18186/journal-of-thermal-engineering.372218

Cited By: 1

Abstract

Proper utilization of
renewable energy sources in electricity production is inevitable due to the
environmental concerns and global warming fight. Therefore, predictability of
renewable electricity is a very significant issue for a long time. Main aim of
this study, different from the literature, is to investigate the change of wind
speed prediction errors for different time horizons. Different prediction time
horizons (10, 30, 60, 90 and 120 minutes) were used, and the results were
compared through the error measures and the regression values. The mean squared
errors and the regression values vary between 0.819 and 5.570, and between
77.8% and 97.1%, respectively. The prediction error changes almost
logarithmically, and the rate of change decreases with the increasing time
horizon. A new analysis approach was proposed to see the change of the
prediction error with time horizon. The equation, y = 1.5413ln(x) - 2.7428, representing the change of
the mean squared error with time horizon was obtained.

Keywords

Wind Energy, Wind Speed Prediction, Artificial Neural Network, Time Horizon

References

• [1] Swift-Hook, D. T. (2013). The case for renewables apart from global warming. Renewable Energy, 49, 147–150.
• [2] Pusat, S., & Akkoyunlu, M. T. (2018). Evaluation of wind energy potential in a university campus. International Journal of Global Warming, 14(1), 118-130.
• [3] Tolga, T., & Demirci, O. K. (2014). Energy and Economic Analysis of the Wind Turbine Plant’s Draft for the Aksaray City. Applied Ecology and Environmental Sciences, 2(3), 82-85.
• [4] Pusat, S. (2017). Study to determine wind energy potential for Sakarya University. Pamukkale University Journal of Engineering Sciences, 23(4), 352-357 (in Turkish).
• [5] Milligan, M., & Parsons, B. A. (1997). Comparison and Case Study of Capacity Credit Algorithms for Intermittent Generators. NREL.
• [6] Roy, S. (2009). Economic Assessment of the Engineering Basis for Wind Power: Perspective of a Vertically Integrated Utility. Energy, 34(11), 1885–1897.
• [7] Scorah, H., Sopinka, A., & van Kooten, G. C. (2012). The economics of storage, transmission and drought: integrating variable wind power into spatially separated electricity grids. Energy Economics, 34(2), 536-541.
• [8] Sørensen, B. (2008). A sustainable energy future: Construction of demand and renewable energy supply scenarios. International Journal of Energy Research, 32, 436–470.
• [9] Cai, Y. P., Huang, G. H., Yeh, S. C., Liu, L., & Li, G. C. (2012). A modeling approach for investigating climate change impacts on renewable energy utilization. International Journal of Energy Research, 36, 764–777.
• [10] Potter, C., & Negnevitsky, M. (2006). Very short-term wind forecasting for Tasmanian power generation, IEEE Transactions on Power Systems, 21(2), 965–972.
• [11] Gertmar, L. (2003). In: Power Electronics and Wind Power, 10th European Conference on Power Electronics and Applications, Toulouse.
• [12] Outhred, H., & Thorncraft, S. (2010). In: Integrating Non-Storable Renewable Energy into the Australian Electricity Industry, Proceedings of the 43rd Hawaii International Conference on System Sciences.
• [13] Georgilakis, S. P. (2008). Technical challenges associated with the integration of wind power into power systems. Renewable and Sustainable Energy Reviews, 12(3), 852–863.
• [14] Mohandes, M. A., Rehman, S., Rahman, S. M. (2012). Spatial estimation of wind speed. International Journal of Energy Research, 36(4), 545–552.
• [15] Alpay, S., Bilir, L., Ozdemir, S., & Ozerdem, B. (2006). Wind speed time series characterization by Hilbert transform. International Journal of Energy Research, 30(5), 359–364.
• [16] Monfared, M., Rastegar, H., & Kojabadi, H. (2009). A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy, 34(3), 845–848.
• [17] Sfetsos, A. (2002). A novel approach for the forecasting of mean hourly wind speed time series. Renewable Energy, 27(2), 163–174.
• [18] El-Fouly, T. H. M., El-Saadan, E. F., & Salama, M. M. A. (2006). Grey predictor for wind energy conversion systems output power prediction. IEEE Transactions on Power Systems, 21(3), 1450–1452.
• [19] Bilgili, M., Sahin, B., & Yasar, A. (2007). Application of artificial neural networks for the wind speed prediction of target station using reference stations data. Renewable Energy, 32, 2350–2360.
• [20] Damousis, I. G., & Dokopoulos, P. (2001). In: A fuzzy model expert system for the forecasting of wind speed and power generation in wind farms, Proceedings of the IEEE International Conference on Power Industry Computer Applications (PICA), 63-69.
• [21] Ramírez-Rosado, I. J., & Fernández-Jiménez, L. A. (2004). An advanced model for short-term forecasting of mean wind speed and wind electric power. Control and Intelligent Systems, 31(1), 21–26.
• [22] Abdel-Aal, R. E., Elhadidy, M. A., & Shaahid, S. M. (2009). Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy, 34(7), 1686–1699.
• [23] Kariniotakis, G., Nogaret, E., & Stavrakakis, G. (1997). In: Advanced Short-Term Forecasting of Wind Power Production, Proceeding of the European Wind Energy Conference EWEC’97, Ireland, 751–754.
• [24] Kamal, L., & Jafri, Y. Z. (1997). Time series models to simulate and forecast hourly average wind speed in Quetta. Solar Energy, 61(1), 23–32.
• [25] Schlink, U., & Tetzlaff, G. (1998). Wind speed forecasting from 1 to 30 minutes. Theoretical and Applied Climatology, 60, 191–198.
• [26] Torres, J. L., García, A., de Blas, M., and de Francisco, A. (2005). Forecast of hourly averages wind speed with ARMA models in Navarre. Solar Energy, 79(1), 65–77.
• [27] Sfetsos, A. (2000). A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy, 21(1), 23–35.
• [28] Alexiadis, M. C., Dokopoulos, P. S., Sahsamanoglou, H. S., & Manousaridis, I. M. (1998). Short term forecasting of wind speed and related electric power. Solar Energy, 63(1), 61–68.
• [29] Cadenas, E., & Rivera, W. (2009). Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renewable Energy, 34(1), 274-278.
• [30] Akyuz, E., Demiral, D., Coskun, C., & Oktay, Z. (2013). Estimation of the Monthly Based Hourly Wind Speed Characteristics and the Generated Power Characteristics for Developing Bidding Strategies in an Actual Wind Farm: A Case Study. Arabian Journal for Science and Engineering, 38(2), 263-275.
• [31] Akinci, T. C., & Nogay, H. S. (2012). Wind Speed Correlation Between Neighboring Measuring Stations. Arabian Journal for Science and Engineering, 37(4), 1007-1019.
• [32] Heidari, M., Heidari, A., & Homaei, H. (2014). Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory. Computational Intelligence and Neuroscience, Article ID 571632.
• [33] Li, P., Li, Y., & Guo, X. (2014). A Red-Light Running Prevention System Based on Artificial Neural Network and Vehicle Trajectory Data. Computational Intelligence and Neuroscience, Article ID 892132.
• [34] Pusat, S., Akkoyunlu, M. T., Pekel, E., Akkoyunlu, M. C., Özkan, C., & Kara, S. S. (2016). Estimation of coal moisture content in convective drying process using ANFIS. Fuel Processing Technology, 147, 12-17.
• [35] Akkoyunlu, M. T., Akkoyunlu, M. C., Pusat, S., & Özkan, C. (2015). Prediction of Accurate Values for Outliers in Coal Drying Experiments. Arabian Journal For Science And Engineering, 40, 2721-2727.
• [36] Lapedes, A., & Farber, R. (1987). Nonlinear signal processing using neural networks: prediction and system modeling. Tech. Rep., LA-UR-87-2662, Los Alamos National Laboratory, Los Alamaos, New Mexico 87545.
• [37] Li, G., & Shi, J. (2010). On comparing three artificial neural networks for wind speed forecasting. Applied Energy, 87(7), 2313–2320.
• [38] Grassi, G., & Vecchio, P. (2010). Wind energy prediction using a two-hidden layer neural network. Communications in Nonlinear Science and Numerical Simulation, 15(9), 2262-2266.
• [39] Pinson, P., & Kariniotakis, G. (2004). On-line assessment of prediction risk for wind power production forecasts. Wind Energy, 7(2), 119–132.
• [40] El-Fouly, T. H. M., El-Saadany, E. F., & Salama, M. M. A. (2006). In: One day ahead prediction of wind speed using annual trends, IEEE Power Engineering Society General Meeting.
• [41] Song, Y. D. (2000). A new approach for wind speed prediction. Wind Engineering, 24(1), 35-47.
• [42] Foley, A. M., Leahy, P. G., Marvuglia, A., & McKeogh, E. J. (2011). Current methods and advances in forecasting of wind power generation. Renewable Energy, July, 1–8.
• [43] Wang, X., Sideratos, G., Hatziargyriou, N., & Tsoukalas, L. H. (2004). In: Wind speed forecasting for power system operational planning, Proceedings of the 8th International Conference on Probabilistic Methods Applied to Power System. Iowa State University, Ames, Iowa, September 12–16.
• [44] Anaklı, Z. (2009). A Comparison of data mining methods for prediction and classification types of quality problems. MSc Thesis, The Graduate School of Natural and Applied Sciences, Department of Industrial Engineering, Middle East Technical University, Ankara, Turkey.
• [45] Özmen, A., & Weber, G. W. (2014). RMARS: Robustification of multivariate adaptive regression spline under polyhedral uncertainty. Journal of Computational and Applied Mathematics, 259 (Part B), 914-924.
• [46] Alp, Ö. S., Büyükbebeci, E., Çekiç, A. İ., Özkurt, F. Y., Taylan, P., & Weber, G. -W. (2011). CMARS and GAM & CQP—Modern optimization methods applied to international credit default prediction. Journal of Computational and Applied Mathematics, 235(16), 4639-4651.