Abstract
In this work, a novel bidirectional model based on the recurrent neural network known as Gated Recurrent Unit (GRU) is proposed for the imputation of not available (NA) values in daily wind speed time series. The proposal model consists of two sequential GRU sub-models of 4-layers each, and for experimentation data from 3 years (2018–2020) is used, the first sub-model is trained with data from 2018 and the second sub-model with 2020 data, in both cases 2019 data is predicted, also, for second sub-model it's necessary the flipped 2020 data. Likewise, data augmentation is applied to improve the precision of the NA estimations. The results achieved show that the bidirectional proposal model achieves very good results, outperforming benchmark models such as Local Average of Nearest Neighbors (LANN), Autoregressive Integrated Moving Average (ARIMA) and Gated Recurrent Unit (GRU) without data augmentation. Likewise, comparing the results with other related works, it’s observed that proposal model surpasses most of them, making it an excellent alternative for wind speed time series imputation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ellaban, O., Abu-Rub, H., Blaadjerg, F.: Renewable energy resources: current status, future prospects and their enabling technology. Renew. Sustain. Energy Rev. 39, 748–764 (2014)
Flores, A., Tito, H., Centty, D.: Recurrent neural networks for meteorological time series imputation. Int. J. Adv. Comput. Sci. Appl. 11(3), 482–487 (2020)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arxiv.org, pp. 1–15 (2014)
Flores, A., Tito-Chura, H., Apaza-Alanoca, H.: Data Augmentation for short-term time series prediction with deep learning. In: Arai, K. (ed.) Intelligent Computing. LNNS, vol. 284, pp. 492–506. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-80126-7_36
Shukur, O.B., Lee, M.H.: Imputation of missing values in daily wind speed data using hybrid AR-ANN method. Mod. Appl. Sci. 9(11), 1–11 (2015)
Wesonga, R.: On multivariate imputation and forecasting of decadal wind speed missing data. Springer Plus, 4(12), 1–8 (2015)
Yi, X., Zheng, J., Li, T.: ST-MVL: filling missing values in geo-sensory time series data. In: International Joint Conference on Artificial Intelligence, New York, USA (2016)
Chen, X., Wang, B., Yu, M., Jin, J, Xu, W.: The interpolation of missing wind speed data based on optimized LSSVM model. In: IEEE 8th International Power Electronics and Motion Control Conference, Hefei, China (2016)
Zong-Xia, X., Xiao-Fei, S.: Imputation of missing wind speed data based on Low-Rank matrix approximation. In: IEEE International Conference on Power and Renewable Energy (ICPRE), Chengdu (2017)
Sánchez, C.N., Enriquez-Zárate, J., Velázquez, R., Graff, M., Sassi, S.: Analysis of wind missing data for wind farms in Isthmus of Tehuantepec. In: IEEE International Autum Meeting on Power, Electronics and Computing, Ixtapa, Mexico (2018)
Zapata-Sierra, A.J., Cama-Pinto, A., Montoya, F.G., Alcayde, A., Manzano-Agugliano, F.: Wind missing data arrangement using wavelet based techniques for getting maximun likelihood. Energ. Convers. Manage. 185, 552–561 (2019)
Afrifa-Yamoah, E., Mueller, U.A., Taylor, S.M., Fisher, A.J.: Missing data imputation of high-resolution temporal climate time series data. Meteorol. Appl. 27, 1–18 (2020)
Chang, C., Lee, S.: Novel imputation for time series data. In: International Conference on Machine Learning and Cybernetics, Guangzhou, China (2015)
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: BRITS: Bidirectional recurrent imputation for time series. arxiv.org, pp. 1–12 (2018)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Sci. Rep. 8(6085), 1–12 (2018)
Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. 35(8), 1798–1828 (2013)
Rashid, K.M., Louis, J.: Time-series data augmentation and deep learning for construction equipment activity recognition. Adv. Eng. Inform. 42, 1–12 (2019)
Iwana, B.K., Uchida, S.: Time series data augmentation for neural networks by time warping with a discriminative teacher. arxiv.org (2020)
Rashid, K.M., Louis, J.: Window-warping: a time series data augmentation of IMU data for construction equipment activity identification. In: 36th International Symposium on Automation and Robotics in Construction, Banff, Canada (2019)
Flores, A., Tito-Chura, E., Yana-Mamani, V.: Wind speed time series prediction with deep learning and data augmentation. In: IntelliSys 2021, Amsterdam, Netherlands (2021)
Flores, A., Tito, H., Silva, C.: Local average of nearest neighbors: univariate time series imputation. Int. J. Adv. Comput. Sci. Appl. 10(8), 45–50 (2019)
Hyndman, R., Athanasopoulos, G.: Forecasting: Principles and Practice, Melbourne, Australia, Otexts (2018)
Flores, A., Tito, H., Centty, D.: Improving gated recurrent unit predictions with univariate time series imputation techniques. Int. J. Adv. Comput. Sci. Appl. 10(12), 708–714 (2019)
Moritz, S., Bartz-Beielstein, T.: imputeTS: time series missing value imputation in R. R J. 9(1), 207–218 (2017)
Flores, A., Tito, H., Centty, D.: Comparison of hybrid recurrent neural networks for univariate time series forecasting. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol. 1250, pp 375–387. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55180-3_28.
Zheng, J., Chen, X., Yu, K., Gan, L., Wang, Y., Wang, K.: Short-term power load forecasting of residential community based on GRU neural network. In: International Conference on Power System Technology, Guangzhou, China (2018)
Mao, Y., Jian, M.: Data completing of missing wind power data based on adaptative BP neural network. In: IEEE International Conference on Probabilistic Methods Applied to Power Systems, Beijing, China (2016)
Lin, Q., Wang, J.: Vertically correlated echelon model for the interrpolation of missing wind speed data. IEEE Trans. Sustain. Energ. 5(3), 804–812 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Flores, A., Tito-Chura, H., Yana-Mamani, V. (2022). Wind Speed Time Series Imputation with a Bidirectional Gated Recurrent Unit (GRU) Model. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 2. FTC 2021. Lecture Notes in Networks and Systems, vol 359. Springer, Cham. https://doi.org/10.1007/978-3-030-89880-9_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-89880-9_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89879-3
Online ISBN: 978-3-030-89880-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)