Abstract
A novel feature-averaging technique of meteorological input data from recent past hours assists wind power generation forecasts from individual wind farms by capturing recent changes in conditions. Seven machine learning models and one deep learning model are configured to evaluate that data averaging technique using published 13,140 hourly data records for each of seven wind farms over an 18 months period. The datasets involve just four historically forecasted weather variables averaged in 12-hourly intervals over the previous 48 h for each hourly power record. This generates 16 time-related input variables for each hourly record. The convolutional neural network (CNN) performs best in training and testing on a supervised learning basis. However, the Adaboost (ADA) model is most accurate for semi-supervised forecasting (RMSE averages 0.177). The ADA achieves forecasting accuracy of RMSE = 0.154 on a t + 1 to t + 30 h ahead basis, outperforming on average, a seasonal autoregressive integrated moving average (SARIMA) model (RMSE = 0.183), trained with the univariate historical hourly power generated. The SARIMA model proved most accurate in forecasting t + 1 to t + 4 h ahead, whereas the ADA model provided more accurate forecasts for most of the hours in the t + 5 to t + 30 interval spread over a 12-month period. By adding additional variables the accuracy of the ADA model could potentially be further improved. Nevertheless, the results presented highlight the effectiveness of applying the proposed time-averaged feature selection technique.
Similar content being viewed by others
References
Pinson, P.: Wind energy: forecasting challenges for its operational management. Stat. Sci. 28, 564–585 (2013). https://doi.org/10.1214/13-STS445
Pardalos, P.M., Rebennack, S., Pereira, M.V.F., Iliadis, N.A., Pappu, V. (eds.): Handbook of Wind Power Systems. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41080-2
Mal, S., Singh, R.B., Huggel, C.: Climate Change, Extreme Events and Disaster Risk Reduction: Towards Sustainable Development Goals. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-319-56469-2
Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (eds.): Handbook of Power Systems I, Energy Systems. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-02493-1
Rebennack, S., Pardalos, P.M., Pereira, M.V.F., Iliadis, N.A. (eds.): Handbook of Power Systems II, Energy Systems. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12686-4
Brown, B.G., Katz, R.W., Murphy, A.H.: Time series models to simulate and forecast wind speed and wind power. J. Clim.Appl. Meteorol. 23(8), 1184–1195 (1984)
Sanchez, I.: Short-term prediction of wind energy production. Int. J. Forecast. 22(1), 43–56 (2006)
Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M., Draxl, C.: The state-of-the-art in short-term prediction of wind power: a literature overview, 2nd edn. Technical report ANEMOS.plus (2011). https://doi.org/10.13140/RG.2.1.2581.4485
Wang, X., Guo, P., Huang, X.: A review of wind power forecasting models. Energy Procedia 12, 770–778 (2011)
Jung, J., Broadwater, R.P.: Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev. 31, 762–777 (2014)
Hanafi, S., Liu, X., Lin, Z., Lotfian, S.: A critical review of wind power forecasting methods—past, present and future. Energies 13, 3764 (2020). https://doi.org/10.3390/en13153764
Lange, M., Focken, U.: Physical Approach to Short-Term Wind Power Prediction. Springer, Berlin (2006).. (ISBN-10 3-540-25662-8S)
Ezzat, A.A., Jun, M., Ding, Y.: Spatio-temporal short-term wind forecast: a calibrated regime-switching method. Ann. Appl. Stat. 13(3), 1484–1510 (2019). https://doi.org/10.1214/19-AOAS1243
Vladislavleva, E., Friedrich, T., Neumann, F., Wagner, M.: Predicting the energy output of wind farms based on weather data: important variables and their correlation. Renew. Energy 50, 236–243 (2013). https://doi.org/10.1016/j.renene.2012.06.036
Lledo, L., Torralba, V., Soret, A., Ramon, J., Doblas-Reyes, F.J.: Seasonal forecasts of wind power generation. Renew. Energy 143, 91–100 (2019). https://doi.org/10.1016/j.renene.2019.04.135
Lima, J.M., Guetter, A.K., Freitas, S.R., Panetta, J., de Mattos, J.G.Z.: A meteorological–statistic model for short-term wind power forecasting. J. Control Autom. Electr. Syst. 28, 679–691 (2017). https://doi.org/10.1007/s40313-017-0329-8
Liu, H., Chen, C., Lv, X., Wu, X., Liu, M.: Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods. Energy Convers. Manag. 195(1), 328–345 (2019). https://doi.org/10.1016/j.enconman.2019.05.020
Zhang, Y., Wang, J., Wang, X.: Review on probabilistic forecasting of wind power generation. Renew. Sustain. Energy Rev. 32, 255–270 (2014)
Henze, J., Siefert, M., Bremicker-Trübelhorn, S., Asanalieva, N.: Probabilistic upscaling and aggregation of wind power forecasts. Energy Sustain. Soc. 10, 15 (2020). https://doi.org/10.1186/s13705-020-00247-4
Wang, J., Yang, W., Du, P., Niu, T.: A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm. Energy Convers. Manag. 163, 134–150 (2018)
Liu, J., Wang, X., Lu, Y.: A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system. Renew. Energy 103, 620–629 (2017)
Wang, J., Yang, W., Du, P., Li, Y.: Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system. Energy 148, 59–78 (2018). https://doi.org/10.1016/j.energy.2018.01.112
Tu, C.S., Hong, C.H., Huang, H.S., Chen, C.H.: Short term wind power prediction based on data regression and enhanced support vector machine. Energies 13, 6319 (2020). https://doi.org/10.3390/en13236319
Erdem, E., Shi, J.: ARMA based approaches for forecasting the tuple of wind speed and direction. Appl. Energy 88, 1405–1414 (2011)
Dowell, J., Pinson, P.: Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Trans. Smart Grid 7, 763–770 (2016)
Yan, J., Li, K., Bai, E., Deng, J., Foley, A.: Hybrid probabilistic wind power forecasting using temporally local Gaussian process. IEEE Trans. Sustain. Energy 7, 87–95 (2016)
Cadenas, E., Rivera, W., Campos-Amexcua, R., Heard, C.: Wind speed prediction using a univariate ARIMA model and a multivariate NARX model. Energies (2016). https://doi.org/10.3390/en9020109
Eldali, F.A.A., Hansen, T., Suryanarayanan, S., Chong, E.K.P.: Employing ARIMA models to improve wind power forecasts: a case study in ERCOT. In: North American Power Symposium (IEEE) 18–20 September, Denver, Colorado, USA (2016). https://doi.org/10.1109/NAPS.2016.7747861
Garcıa, J.L.T., Caldero, E.C., Avalos, G.G., Heras, E.R., Tshikala, A.M.: Forecast of daily output energy of wind turbine using sARIMA and non-linear-autoregressive models. Adv. Mech. Eng. (2019). https://doi.org/10.1177/1687814018813464
Yuan, X., Tan, Q., Lei, X., Yuan, Y., Wu, X.: Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine. Energy 129, 122–137 (2017). https://doi.org/10.1016/j.energy.2017.04.094
Foley, A.M., Leahy, P.G., Marvuglia, A., McKeogh, E.J.: Current methods and advances in forecasting of wind power generation. Renew. Energy 37, 1–8 (2012)
Kusiak, A., Zheng, H., Song, Z.: Short-term prediction of wind farm power: a data mining approach. IEEE Trans. Energy Convers. 24(1), 125–136 (2009)
Treiber, N.A., Heinermann, J., Kramer, O.: Aggregation of features for wind energy prediction with support vector regression and nearest neighbors. In: European Conference on Machine Learning, DARE Workshop (Conference Paper) (2013)
Kumar, P.S., Lopez, D.: Forecasting of wind speed using feature selection and neural networks. Int. J. Renew. Energy Res. 6(3), 833–837 (2016)
Martín-Vázquez, R., Aler, R., Galván, I.M.: A study on feature selection methods for wind energy prediction advances in computational intelligence. Lect. Notes Comput. Sci. 10305, 698–707 (2017). https://doi.org/10.1007/978-3-319-59153-7_60
Silva, L.: A feature engineering approach to wind power forecasting: GEFCom 2012. Int. J. Forecast. 30(2), 395–401 (2014). https://doi.org/10.1016/j.ijforecast.2013.07.007
Marugán, A.P., Márquez, F.P.G., Perez, I.M.P., Ruiz-Hernández, D.A.: Survey of artificial neural network in wind energy systems. Appl. Energy 228, 1822–1836 (2018). https://doi.org/10.1016/j.apenergy.2018.07.084
Nazir, M.S., Alturise, F., Alshmrany, S., Nazir, H.M.J., Bilal, M., Abdalla, A.N., Sanjeevikumar, P., Ali, Z.M.: Wind generation forecasting methods and proliferation of artificial neural network: a review of five years research trend. Sustainability 12, 3778 (2020). https://doi.org/10.3390/su12093778
Mohandes, M.A., Rehmann, S., Halawani, T.O.: A neural networks approach for wind speed prediction. Renew. Energy 13(3), 345–354 (1998)
Catalao, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: An artificial neural network approach for short-term wind power forecasting in Portugal. In: IEEE, 15th International Conference on Intelligent System Applications to Power Systems 8109 (2009). https://doi.org/10.1109/ISAP.2009.5352853
Han, S., Liu, Y., Yan, J.: Neural network ensemble method study for wind power prediction. In: IEEE Asia Pacific Power and Energy Engineering Conference (APPEEC) (2011). https://doi.org/10.1109/APPEEC.2011.5748787
Peng, H., Liu, F., Yang, X.: A hybrid strategy of short term wind power prediction. Renew. Energ. 50, 590–595 (2013). https://doi.org/10.1016/j.renene.2012.07.022
Pelletier, F., Masson, C., Tahan, A.: Wind turbine power curve modelling using artificial neural network. Renew. Energy 89, 207–214 (2016). https://doi.org/10.1016/j.renene.2015.11.065
Rani, R.H.J., Victoire, T.A.A.: Training radial basis function networks for wind speed prediction using PSO enhanced differential search optimizer. PLoS ONE 13(5), e0196871 (2018). https://doi.org/10.1371/journal.pone.0196871
Mishra, S.P., Dash, P.K.: Short-term prediction of wind power using a hybrid pseudo-inverse Legendre neural network and adaptive firefly algorithm. Neural Comput. Appl. 31, 2243–2268 (2019). https://doi.org/10.1007/s00521-017-3185-3
Zameer, A., Arshad, J., Khan, A., Raja, M.A.Z.: Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers. Manag. 134, 361–372 (2017). https://doi.org/10.1016/j.enconman.2016.12.032
Shahid, F., Khan, A., Zameer, A., Arshad, J., Safdar, K.: Wind power prediction using a three stage genetic ensemble and auxiliary predictor. Appl. Soft Comput. J. 90, 106151 (2020). https://doi.org/10.1016/j.asoc.2020.106151
Zhang, J., Yan, J., Infield, D., Liu, Y., Lien, F.: Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl. Energy 241, 229–244 (2019). https://doi.org/10.1016/j.apenergy.2019.03.044
Shahid, F., Zameer, A., Mehmood, A., Raja, M.A.Z.: A novel wavenets long short term memory paradigm for wind power prediction. Appl. Energy 269, 115098 (2020). https://doi.org/10.1016/j.apenergy.2020.115098
Shahid, F., Zameer, A., Muneeb, M.: A novel genetic LSTM model for wind power forecast. Energy 223, 120069 (2021). https://doi.org/10.1016/j.energy.2021.120069
Manobel, B., Sehnke, F., Lazzús, J.A., Salfate, I., Felder, M., Montecinos, S.: Wind turbine power curve modeling based on Gaussian processes and artificial neural networks. Renew. Energy 125, 1015–1020 (2018). https://doi.org/10.1016/j.renene.2018.02.081
Heinermann, J., Kramer, O.: Precise wind power prediction with SVM ensemble regression. In: Artificial Neural Networks and Machine Learning—ICANN. Springer, Switzerland, pp. 797–804 (2014)
Jiang, P., Wang, Y., Wang, J.: Short-term wind speed forecasting using a hybrid model. Energy 119, 561–577 (2017). https://doi.org/10.1016/j.energy.2016.10.040
Taieb, S.B., Hyndman, R.J.: A gradient boosting approach to the Kaggle load forecasting competition. Int. J. Forecast. 30(2), 382–394 (2014). https://doi.org/10.1016/j.ijforecast.2013.07.005
Lahouar, A., Hadj Slama, J.B.: Hour-ahead wind power forecast based on random forests. Renew. Energy 109, 529–541 (2017). https://doi.org/10.1016/j.renene.2017.03.064
Mangalova, E., Agafonov, E.: Wind power forecasting using the k-nearest neighbors algorithm. Int. J. Forecast. 30(2), 402–406 (2014). https://doi.org/10.1016/j.ijforecast.2013.07.008
Treiber, N.A., Heinermann, J., Kramer, O.: Wind power prediction with machine learning. In, Lassig, J., Kersting K., Morik K. (eds.) Computational Sustainability, Studies in Computational Intelligence 645, pp. 13–29. Springer, Cham. (2016). https://doi.org/10.1007/978-3-319-31858-5_2
Wood, D.A.: Country-wide German hourly wind power dataset mined to provide insight to predictions and forecasts with optimized data-matching machine learning. Renew. Energy Focus 34, 69–90 (2020). https://doi.org/10.1016/j.ref.2020.06.005
Wood, D.A.: German country-wide renewable power generation from solar plus wind mined with an optimized data matching algorithm utilizing diverse variables. Energy Syst. 11, 1003–1045 (2020). https://doi.org/10.1007/s12667-019-00347-x
Qureshi, A.S., Khan, A., Zameer, A., Usman, A.: Wind power prediction using deep neural network based meta regression and transfer learning. Appl. Soft Comput. 58, 742–755 (2017). https://doi.org/10.1016/j.asoc.2017.05.031
Lin, Z., Liu, X.: Wind power forecasting of an onshore wind turbine based on high-frequency SCADA data and deep learning neural network. Energy 201, 117693 (2020). https://doi.org/10.1016/j.energy.2020.117693
Lin, Z., Liu, X., Collu, M.: Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks. Int. J. Electr. Power Energy Syst 118, 105835 (2020). https://doi.org/10.1016/j.ijepes.2020.105835
Hong, Y.Y., Rioflorido, C.L.P.P.: A hybrid deep learning-based neural network for 24-h ahead wind power forecasting. Appl. Energy 250, 530–539 (2019). https://doi.org/10.1016/j.apenergy.2019.05.044
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting—an approach using AutoEncoder and LSTM neural networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, pp. 002858–002865 (2016)
Meng, A., Chen, S., Ou, Z., Ding, W., Zhou, H., Fan, J., Yin, H.: A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization. Energy 238B, 121795 (2022). https://doi.org/10.1016/j.energy.2021.121795
Özcan, M., Keysan, O., Satır, B.: Optimum bidding strategy for wind and solar power plants in day-ahead electricity market. Energy Syst. 12, 955–987 (2021). https://doi.org/10.1007/s12667-021-00441-z
Yörükoğlu, M., Aydın, S.: Wind turbine selection by using MULTIMOORA method. Energy Syst. 12, 863–876 (2021). https://doi.org/10.1007/s12667-020-00387-8
Christoforou, E., Emiris, I.Z., Florakis, A., Rizou, D., Zaharia, S.: Spatio-temporal deep learning for day-ahead wind speed forecasting relying on WRF predictions. Energy Syst. (2021). https://doi.org/10.1007/s12667-021-00480-6
Omar, O.A.M., Ahmed, H.M., Elbarkouky, R.A.: Wind turbines new criteria optimal site matching under new capacity factor probabilistic approaches. Energy Syst. (2021). https://doi.org/10.1007/s12667-021-00463-7
Mahmoud, M.M., Aly, M.M., Salama, H.S., Abdel-Rahim, A.M.: A combination of an OTC based MPPT and fuzzy logic current control for a wind-driven PMSG under variability of wind speed. Energy Syst. (2021). https://doi.org/10.1007/s12667-021-00468-2
Barreto, G.A., Brasil, I.S., Souza, L.G.M.: Revisiting the modeling of wind turbine power curves using neural networks and fuzzy models: an application-oriented evaluation. Energy Syst. (2021). https://doi.org/10.1007/s12667-021-00449-5
SciKit Learn: Supervised and unsupervised machine learning models in Python. https://scikit-learn.org/stable/ (2021). Accessed 2 Dec 2021
TensorFlow: Deep learning Keras models in Python. https://www.tensorflow.org/guide/keras/sequential_model (2021). Accessed 2 Dec 2021
GridSearchCV: SciKit Learn search function to find optimum parameter values for an estimator. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html (2021). Accessed 6 Dec 2021
BayesSearchCV: Bayesian optimization of hyperparameters by SciKit Learn function. https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html (2021). Accessed 6 Dec 2021
Xiao, L., Dong, Y., Dong, Y.: An improved combination approach based on Adaboost algorithm for wind speed time series forecasting. Energy Convers. Manag. 160, 273–288 (2018). https://doi.org/10.1016/j.enconman.2018.01.038
Khan, M., Liu, T., Ullah, F.: A new hybrid approach to forecast wind power for large scale wind turbine data using deep learning with TensorFlow framework and principal component analysis. Energies 12, 2229 (2019). https://doi.org/10.3390/en12122229
Han, L., Qiao, Y., Li, M., Shi, L.: Wind power ramp event forecasting based on feature extraction and deep learning. Energies 13, 6449 (2020). https://doi.org/10.3390/en13236449
Chaudhary, A., Sharma, A., Kumar, A., Dikshit, K., Kumar, N.: Short term wind power forecasting using machine learning techniques. J. Stat. Manag. Syst. 20(1), 145–156 (2020). https://doi.org/10.1080/09720510.2020.1721632
Acikgoz, H., Yildiz, C., Sekkeli, M.: An extreme learning machine based very short-term wind power forecasting method for complex terrain. Energy Sources Part A Recov. Util. Environ. Effects 42(22), 2715–2730 (2020). https://doi.org/10.1080/15567036.2020.1755390
Yesilbudak, M., Sagiroglu, S., Colak, I.: A new approach to very short term wind speed prediction using k-nearest neighbor classification. Energy Convers. Manag. 69, 77–86 (2013). https://doi.org/10.1016/j.enconman.2013.01.033
Lv, X., Cheng, X., Shuang, Y., Tang, Y.M.: Short-term power load forecasting based on balanced KNN. Mater. Sci. Eng. 322, 072058 (2018). https://doi.org/10.1088/1757-899X/322/7/072058
Bilal, B., Ndongo, M., Adjallah, K.H., Sava, A., Kebe, C.M.F., Ndiaye, P.A., Sambou, V.: Wind turbine power output prediction model design based on artificial neural networks and climatic spatiotemporal data. In: Proceedings of the IEEE International Conference on Industrial Technology Lyon, France 20–22, pp. 1085–1092 (2018)
Vassallo, D., Krishnamurthy, R., Sherman, T., Fernando, H.J.S.: Analysis of random forest modeling strategies for multi-step wind speed forecasting. Energies 13, 5488 (2020). https://doi.org/10.3390/en13205488
Stetco, A., Dinmohammadi, E., Zhao, X., Robu, V., Flynn, D., Barnes, M., Keane, J., Nenadic, G.: Machine learning methods for wind turbine condition monitoring: a review. Renew. Energy 133, 620–635 (2019). https://doi.org/10.1016/j.renene.2018.10.047
SciKit Learn: K-fold cross-validator function to split dataset into k consecutive folds between training and testing sets. https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html (2021). Accessed 6 Dec 2021
Box, G.E.P., Jenkins, G.M.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)
Box, G.E.P., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control, 5th edn, Wiley, Hoboken, New Jersey U.S.A (2015). (ISBN: 978-1-118-67502-1)
Cadenas, E., Rivera, W.: Wind speed forecasting in the South Coast of Oaxaca, México. Renew. Energy 32(12), 2116–2128 (2007). https://doi.org/10.1016/j.renene.2006.10.005
Statsmodels: Statistical models in Python for timeseries analysis. https://www.statsmodels.org/stable/tsa.html (2021). Accessed 6 Dec 2021
Wang, S., Feng, J., Liu, G.: Application of seasonal time series model in the precipitation forecast. Math. Comput. Model. 58, 677–686 (2013). https://doi.org/10.1016/j.mcm.2011.10.034
Hong, T., Pinson, P., Fan, S.: Global energy forecasting competition 2012. Int. J. Forecast. 30(2), 357–363 (2014)
Data Source: Global energy forecasting competition. https://www.kaggle.com/c/GEF2012-wind-forecasting/data (2012). Accessed 7 Dec 2021
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author confirms that he has no conflicts of interest and has received no external or institutional funding associated with the research content of this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wood, D.A. Feature averaging of historical meteorological data with machine and deep learning assist wind farm power performance analysis and forecasts. Energy Syst 14, 1023–1049 (2023). https://doi.org/10.1007/s12667-022-00502-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12667-022-00502-x