Forecasting wind speed with recurrent neural networks. (English) Zbl 1253.86008

Summary: This research presents a comparative analysis of the wind speed forecasting accuracy of univariate and multivariate ARIMA models with their recurrent neural network counterparts. The analysis utilizes contemporaneous wind speed time histories taken from the same tower location at five different heights above ground level. A unique aspect of the study is the exploitation of information contained in the wind histories for the various heights when producing forecasts of wind speed for the various heights. The findings indicate that multivariate models perform better than univariate models and that the recurrent neural network models outperform the ARIMA models. The results have important implications for a variety of engineering applications and business related operations.


86A32 Geostatistics
62M20 Inference from stochastic processes and prediction
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62M45 Neural nets and related approaches to inference from stochastic processes
86A10 Meteorology and atmospheric physics
Full Text: DOI


[1] Al-Ahmari, A., Prediction and optimisation models for turning operations, International journal of production research, 46, 15, 4061-4081, (2008) · Zbl 1140.90372
[2] Barbounis, T.; Theocharis, J.; Alexiadis, M.; Dokopoulos, P., Long-term wind speed and power forecasting using local recurrent neural network models, IEEE transactions on energy conversion, 21, 1, 273-284, (2006)
[3] Barbounis, T.; Theocharis, J., A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation, Neurocomputing, 70, (2007), 1525-1452
[4] Bhattacharyya, P.; Sengupta, D.; Mukhopadhyay, S.; Chattopadhyay, A., On-line tool condition monitoring in face milling using current and power signals, International journal of production research, 46, 4, 1187-1201, (2008) · Zbl 1160.90367
[5] Brown, B.; Katz, R.; Murphy, A., Time series models to simulate and forecast wind speed and wind power, Journal of applied meteorology, 23, 8, 1184-1195, (1984)
[6] Buizza, R.; Houtekamer, P.; Toth, Z.; Pellerin, G.; Wei, M.; Zhu, Y., A comparison of the ECMWF, MSC and NCEP global ensemble prediction systems, Monthly weather review, 133, 5, 1076-1097, (2005)
[7] Considine, T.; Jablonowski, C.; Posner, B.; Bishop, C., The value of hurricane forecasts to oil and gas producers in the gulf of Mexico, Journal of applied meteorology, 43, 9, 1270-1281, (2004)
[8] Damousis, I.; Alexiadis, M.; Theocharis, J.; Dokopoulos, P., A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation, IEEE transactions on energy conversion, 19, 2, 352-361, (2004)
[9] Das, P.; Datta, S., Exploring the non-linearity in empirical modelling of a steel system using statistical and neural network models, International journal of production research, 45, 3, 699-717, (2007) · Zbl 1128.90318
[10] ()
[11] Ellingwood, B.; Tekie, P., Wind load statistics for probability based structural design, Journal of structural engineering, 125, 4, 453-463, (1998)
[12] Ewing, B.; Kruse, J.; Schroeder, J.; Smith, D., Time series analysis of wind speed using VAR and impulse response technique, Journal of wind engineering and industrial aerodynamics, 95, 3, 209-219, (2007)
[13] Ewing, B.; Kruse, J.; Schroeder, J., Time series analysis of wind speed with time-varying turbulence, Environmetrics, 17, 2, 119-127, (2006)
[14] Ewing, B.; Kruse, J.; Thompson, M., Analysis of time-varying turbulence in geographically-dispersed wind energy markets, Energy sources, part B: economics, planning and policy, 3, 4, 340-347, (2008)
[15] Giebel, G., Brownsword, R., Kariniotakis, G., 2003. The state-of-the-art in short-term prediction of wind power: A literature overview. Deliverable Report D1.1. Project ANEMOS.
[16] Gneiting, T.; Larson, K.; Westrick, K.; Genton, M.; Aldrich, E., Calibrated probabilistic forecasting at the stateline wind energy center: the regime-switching space-time method, Journal of the American statistical association, 101, 475, 968-979, (2006) · Zbl 1120.62341
[17] Gneiting, T.; Raftery, A., Weather forecasting with ensemble methods, Science, 310, 5746, 248-249, (2005)
[18] Grimit, E.; Mass, C., Initial results of a mesoscale short-range ensemble forecasting system over the Pacific northwest, Weather and forecasting, 17, 2, 192-205, (2002)
[19] Hahn, H.; Meyer-Nieberg, S.; Pickl, S., Electric load forecasting methods: tools for decision making, European journal of operational research, 199, 3, 902-907, (2009) · Zbl 1176.90291
[20] Haykin, S., Neural networks: A comprehensive foundation, (1998), Prentice-Hall Englewood Cliffs · Zbl 0828.68103
[21] Herzmann, D.; Wolt, J.; Arritt, R., Representativity of a mesoscale network for weather-related factors governing pollen dispersal, International journal of biometerology, 52, 7, 617-624, (2008)
[22] Holmes, J., Wind loading of structures, (2007), Taylor & Francis New York
[23] Horonjeff, R.; McKelvey, F.; Sproule, W.; Young, S., Planning and design of airports, (2010), McGraw-Hill New York
[24] Huang, S.; Chiu, N.; Chen, L., Integration of the grey relational analysis with genetic algorithm for software effort estimation, European journal of operational research, 188, 3, 898-909, (2008) · Zbl 1144.90367
[25] Huang, Z.; Chalabi, Z., Use of time-series analysis to model and forecast wind speed, Journal of wind engineering and industrial aerodynamics, 56, 2-3, 311-322, (1995)
[26] Hussain, S.; Elbergali, A.; Al-Masri, A.; Shukur, G., Parsimonious modeling, testing and forecasting of long-range dependence in wind speed, Environmetrics, 15, 2, 155-171, (2004)
[27] Institute for Business and Home Safety, 1999. Performance of metal buildings in high winds. Natural Hazard Mitigation Insight: A publication of the Institute of Business and Home Safety, Vol. (9), pp. 1-11.
[28] Jonsson, T.; Pinson, P.; Madsen, H., On the market impact of wind energy forecasts, Energy economics, 32, 2, 313-320, (2010)
[29] Jordan, M., 1986. Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the eighth conference of the cognitive science society.
[30] Kaparthi, S.; Suresh, N., Performance of selecting part-machine grouping technique for data sets of wide ranging sizes and imperfection, Decision sciences, 25, 4, 515-532, (1994)
[31] Kavasseri, R.; Seetharaman, K., Day-ahead wind speed forecasting using f-ARIMA models, Renewable energy, 34, 5, 1388-1393, (2009)
[32] Kestens, E.; Teugels, J., Challenges in modeling stochasticity in wind, Environmetrics, 13, 8, 821-830, (2002)
[33] Khanduri, A.; Morrow, G., Vulnerability of buildings to windstorms and insurance loss estimation, Journal of wind engineering and industrial aerodynamics, 91, 4, 455-467, (2003)
[34] Kim, Y.; Street, N.; Russell, G.; Menczer, F., Customer targeting: A neural network approach guided by genetic algorithms, Management science, 51, 2, 264-276, (2005)
[35] Ku, C.-C.; Lee, K., Diagonal recurrent neural networks for dynamic systems control, IEEE transaction on neural networks, 6, 1, 144-156, (1995)
[36] Kuflik, T.; Boger, Z.; Shoval, P., Filtering search results using an optimal set of terms identified by an artificial neural network, Information processing & management, 42, 2, 469-483, (2006) · Zbl 1077.68634
[37] Kuldeep, K.; Sukanto, B., Artificial neural network vs. linear discriminant analysis in credit ratings forecast: A comparative study of prediction performances, Review of accounting & finance, 5, 3, 216-227, (2006)
[38] Landajo, M.; de Andrés, J., Robust neural modeling for the cross-sectional analysis of accounting information, European journal of operational research, 177, 2, 1232-1252, (2007) · Zbl 1109.62082
[39] Lenard, M.; Alam, P.; Madey, G., The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision, Decision sciences, 26, 2, 209-224, (1995)
[40] Manwell, J.; McGowan, J.; Rogers, A., Wind energy explained: theory, design, and application, (2009), John Wiley & Sons West Sussex
[41] More, A.; Deo, M., Forecasting wind with neural networks, Marine structures, 16, 1, 35-49, (2003)
[42] Palmer, T., The economic value of ensemble forecasts as a tool for risk assessment: from days to decades, Quarterly journal of the royal meteorological society, 128, 581, 747-774, (2002)
[43] Pinson, P.; Nielsen, H.; Møller, J.; Madsen, H.; Kariniotakis, G., Nonparametric probabilistic forecasts of wind power: required properties and evaluation, Wind energy, 10, 6, 497-516, (2007)
[44] Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A., Forecasting and simulating wind speed in corsica by using an autoregressive model, Energy conversion and management, 44, 20, 3177-3196, (2003)
[45] Regnier, E., Doing something about the weather, Omega, 36, 1, 22-32, (2008)
[46] Roulston, M.; Kaplan, D.; Hardenberg, J.; Smith, L., Using medium-range weather forecasts to improve the value of wind energy production, Renewable energy, 28, 4, 585-602, (2003)
[47] Sfetsos, A., A comparison of various forecasting techniques applied to Mean hourly wind speed time series, Renewable energy, 21, 1, 23-35, (2000)
[48] Sfetsos, A., A novel approach for the forecasting of Mean hourly wind speed time series, Renewable energy, 27, 2, 163-174, (2002)
[49] Simiu, E.; Scanlan, R., Wind effects on structures, (1986), Wiley-Interscience New York
[50] Simiu, E.; Scanlan, R., Wind effects on structures, (1996), John Wiley & Sons New York
[51] Thieme, R.; Song, M.; Calanton, R., Artificial neural network decision support systems for new product development project selection, Journal of marketing research, 37, 2, 499-506, (2000)
[52] Twisdale, L., Probability of facility damage from extreme wind effects, Journal of structural engineering, 114, 10, 2190-2208, (1988)
[53] Wang, K.; Chen, J.; Lin, Y., A hybrid knowledge discovery model using decision tree and neural network for selecting dispatching rules of a semiconductor final testing factory, Production planning & control, 16, 7, 665-680, (2005)
[54] Williams, R., Zipser, D., 1988. A learning algorithm for continually running fully recurrent networks. Technical report ICS Report 8805. University of California, San Diego, La Jolla.
[55] Wu, R.; Chen, R.; Chian, S., Design of a product quality control system based on the use of data mining techniques, IIE transactions, 38, 1, 39-51, (2006)
[56] Zhang, W.; Cao, Q.; Schniederjans, M., Neural network earnings per share forecasting models: A comparative analysis of alternative methods, Decision sciences, 35, 2, 205-237, (2004)
[57] Zhou, N., Smith, D., Mehta, K., 2003. Stochastic models for wind, wind-induced pressure, and structural response of a purlin measured in full scale. In: Proceedings of the Eleventh International Conference on Wind Engineering. Texas Tech University Wind Science and Engineering, pp. 821-828.
[58] Zhu, D.; Premkumar, G.; Zhang, X.; Chu, C., Data mining for network intrusion detection: A comparison of alternative methods, Decision sciences, 32, 4, 635-653, (2001)
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