Short-Term Wind Speed Forecast by Convolutional LSTM with Ensemble Empirical Mode Decomposition

Rakesh Yadav G., Ramu V.

Abstract


The shift from fossil‑based to clean‑energy infrastructure has escalated the significance of wind power. Expanding the power system to accommodate wind generation remains challenging due to the natural fluctuations and uncertainty of wind speeds. Effective short‑term prediction of wind speeds is key to maintain system reliability, efficient power scheduling, and dependable wind‑energy integration. This work introduces a hybrid EEMD‑ConvLSTM framework that hybridize Ensemble Empirical Mode Decomposition for signal decomposition and a Convolutional LSTM for spatiotemporal feature learning. Using EEMD, the raw time‑dependent and structurally stochastic and highly variable wind‑speed measurements are separated into a collection of intrinsic oscillatory components each obtaining meaningful time‑varying sequence of the underlying dynamics. These functions are subsequently fed as input to the ConvLSTM network, which models both spatial and temporal dependencies to improve forecasting. The model’s accuracy is examined using site‑specific wind‑speed data sourced from a power plant facility in Telangana, India. The forecasting accuracy is reflected in the MAE (0.0983 m/s), RMSE (0.1025 m/s), and MAPE (1.5472%) obtained during testing. The method performs better and runs faster than EEMD‑LSTM and other conventional models, making it suitable for instantaneous utilization in smart‑grid environments and renewable energy platforms

Keywords


Convolutional long short-term memory (ConvLSTM); Renewable energy integration; Short-term wind power prediction; Spatiotemporal deep learning; Time-series decomposition

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References


Global Wind Energy Council (GWEC). 2021. Global Wind Report: Annual Market Update 2021. Global Wind Energy Council Report; 2021.

Huang, Z., and Jiriwibhakorn, S. (2025). Advanced Short-Term wind power Forecasting based on adaptive Neuro-Fuzzy Inference System and artificial neural network. International Review of Electrical Engineering, 20(2), 126.

Behgouy, P. and Ugurenver, A., 2025. Hybrid Microgrid Power Management via a CNN–LSTM Centralized Controller Tuned with Imperialist Competitive Algorithm. Mathematics, 13(24), 4030.

Wang J., Qin S., Zhou Q., and Jiang H. 2015. Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China. Renewable Energy 76(1): 91–101.

Liu J., Wang X., and Lu Y. 2017. A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro‑fuzzy inference system. Renew. Energy 103(1): 620–629.

Adewuyi, O.B., Folly, K.A., Oyedokun, D.T. and Ogunwole, E.I., 2022. Power system voltage stability margin estimation using adaptive neuro-fuzzy Inference system enhanced with particle swarm optimization. Sustainability, 14(22), 15448.

Okumus I. and Dinler A. 2016. Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conversion and Management 123(1): 362–371.

Mehr, M.M., Farzin, H. and Mashhour, E., 2025. Short-Term Load Forecasting Using Multilayer Neural Networks: A Residential Complex Case Study. In Proceedings of 2025 Fifth National and the First International Conference on Applied Research in Electrical Engineering (AREE). Ahvaz, Iran, Islamic Republic of, pp. 1-6. IEEE.

Boubaker, H. and Bannour, N., 2023. Coupling the empirical wavelet and the neural network methods in order to forecast electricity price. Journal of Risk and Financial Management, 16(4), 246.

Yu C., Li Y., and Zhang M. 2017. Comparative study on three new hybrid models using Elman neural network and empirical mode decomposition based technologies improved by singular spectrum analysis for hour-ahead wind speed forecasting. Energy Conv.and Management 147(1): 75–85.

Doucoure B., Agbossou K., and Cardenas A. 2016. Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data. Renewable Energy 92(1): 202–211.

Ai, X., Li, S. and Xu, H., 2023. Wind speed prediction model using ensemble empirical mode decomposition, least squares support vector machine and long short-term memory. Frontiers in Energy Research, 10, 1043867.

Yuzgec, U., Dokur, E. and Balci, M., 2024. A novel hybrid model based on empirical mode decomposition and echo state network for wind power forecasting. Energy, 300, 131546.

Chen, N., Sun, H., Zhang, Q. and Li, S., 2022. A short-term wind speed forecasting model based on EMD/CEEMD and ARIMA-SVM algorithms. Applied Sciences, 12(12), 6085.

Gu, W., Xing, H., Yang, G., Shi, Y. and Liu, T., 2024. Artificial-Intelligence-Based model for early strong wind warnings for high-speed railway system. Electronics, 13(23), 4582.

Zhao, L.F., Siahpour, S., Haeri Yazdi, M.R., Ayati, M. and Zhao, T.Y., 2022. Intelligent Monitoring System Based on Noise‐Assisted Multivariate Empirical Mode Decomposition Feature Extraction and Neural Networks. Computational intelligence and neuroscience, 2022(1), 2698498.

Jiang Y. and Huang G. 2017. Short-term wind speed prediction: Hybrid of ensemble empirical mode decomposition, feature selection and error correction. Energy Conversion and Management 144(1): 340–350.

Jiang, T., & Liu, Y. (2023). A short-term wind power prediction approach based on ensemble empirical mode decomposition and improved long short-term memory. Computers and Electrical Engineering, 110, 108830.

Wang S., Zhang N., Wu L., and Wang Y. 2016. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA–BP neural network method. Renewable Energy 94(1): 629–636.

Tahir, A.S., Abdulazeez, A.M. and Ali, I.A., 2024. Wind speed forecasting based on secondary decomposition and LSTM. International Journal of Communication Networks and Information Security, 16(3), 1-15.

Van Jaarsveldt, C., Peters, G.W., Ames, M. and Chantler, M., 2023. Tutorial on empirical mode decomposition: Basis decomposition and frequency adaptive graduation in non-stationary time series. IEEE Access, 11, 94442-94478.

Sun, X., Nassif, R., Richard, C. and Wang, H., 2024. Noise-Assisted Graph Multivariate Empirical Mode Decomposition with Non-Uniform Projections. IEEE Transactions on Circuits and Systems I: Regular Papers, 72(4), 1707-1717.

National Institute of Wind Energy (NIWE), India. Ministry of New and Renewable Energy. Available at: niwe.res.in. Accessed on 20 June 2017.




DOI: https://doi.org/10.64289/iej.26.0206.4431901