Open Access
| Issue |
BIO Web Conf.
Volume 225, 2026
International Colloquium on Youth, Environment, and Sustainability – “Earth System Equity: Integrating Social-Economy and Ecological Solutions within Planetary Boundaries” (ICYES 2025)
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|---|---|---|
| Article Number | 04002 | |
| Number of page(s) | 13 | |
| Section | Renewable and Clean Energy | |
| DOI | https://doi.org/10.1051/bioconf/202622504002 | |
| Published online | 06 March 2026 | |
- D. G. Cendrawati, N. W. Hesty, B. Pranoto, A. Aminuddin, A. H. Kuncoro, and A. Fudholi, “Short-Term Wind Energy Resource Prediction Using Weather Research Forecasting Model for a Location in Indonesia,” Int. J. Technol., vol. 14, p. 584, (2023), doi: https://doi.org/10.14716/ijtech.v14i3.5803. [CrossRef] [Google Scholar]
- V. Gomes, D. Carvalho, and S. Gouveia, “On the Correction of GFS Wind Speed Forecasts in Portugal Using LSTM Networks,” (2026), pp. 321–332. doi: https://doi.org/10.1007/978-3-031-99568-2_26. [Google Scholar]
- C. Pang, T. Song, H. Sun, X. Li, and D. Xu, “A deep learning method for bias correction of wind field in the South China Sea,” Front. Mar. Sci., vol. 11, (2025), doi: https://doi.org/10.3389/fmars.2024.1429057. [Google Scholar]
- X. Sun et al., “Short-Term Wind Speed Forecasts over the Pearl River Estuary: Numerical Model Evaluation and Deterministic Post-Processing,” J. Trop. Meteorol., vol. 30, no. 4, pp. 390–404, Dec. (2024), doi: 10.3724/j.1006-8775.2024.035. [Google Scholar]
- S. R. Fithri, N. W. Hesty, R. P. Wijayanto, and B. Pranoto, “Enhancing wind energy prediction accuracy with a hybrid Weibull distribution and ANN model : a case study across ten locations in Java Island , Indonesia,” vol. 41, no. 1, pp. 180–190, (2026), doi: 10.11591/ijeecs.v41.i1.pp180-190. [Google Scholar]
- M. K. Sallam Ma’aitah, J. B. Idoko, A. Alwhelat, K. Smart, and Z. Alwaeli, “Evaluation of Hyperparameter Optimization Techniques in Deep Learning Considering Accuracy, Runtime, and Computational Efficiency Metrics,” J. Soft Comput. Data Min., vol. 6, (2025), doi: https://doi.org/10.30880/jscdm.2025.06.01.013. [Google Scholar]
- N. T. H. Thu, P. N. Van, N. V. N. Nam, P. H. Minh, and P. Q. Bao, “Forecasting Wind Speed Using A Hybrid Model Of Convolutional Neural Network And Long- Short Term Memory With Boruta Algorithm-Based Feature Selection,” J. Appl. Sci. Eng., vol. 26, pp. 1055–1062, (2023), doi: https://doi.org/10.6180/jase.202308_26(8).0001. [Google Scholar]
- A. Neagoe, E.-I. Tică, L.-I. Vuță, O. Nedelcu, G.-E. Dumitran, and B. Popa, “Hybrid LSTM-ARIMA Model for Improving Multi-Step Inflow Forecasting in a Reservoir,” Water, vol. 17, p. 3051, (2025), doi: https://doi.org/10.3390/w17213051. [Google Scholar]
- G. Çınarer, “Hybrid Deep Learning and Stacking Ensemble Model for Time Series- Based Global Temperature Forecasting,” Electronics, vol. 14, p. 3213, (2025), doi: https://doi.org/10.3390/electronics14163213. [Google Scholar]
- I. A. Kachalla, C. Ghiaus, A. Ademuwagun, O. B. Odeyinde, and M. Baseer, “Data- driven hybrid SARIMAX-MLP framework for energy consumption prediction in residential micro-grid,” Results Eng., vol. 26, p. 105336, (2025), doi: https://doi.org/10.1016/j.rineng.2025.105336. [Google Scholar]
- A. F. Gonzalez-Mora, E. Foulon, and A. N. Rousseau, “A climate-informed statistical framework to indirectly estimate trends in future seasonal high flows in snow-dominated watersheds using short-term climate variability indices,” J. Hydrol., vol. 664, p. 134441, (2026), doi: https://doi.org/10.1016/j.jhydrol.2025.134441. [Google Scholar]
- D. Xu, Q. Zhang, Y. Ding, and D. Zhang, “Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting,” Environ. Sci. Pollut. Res., vol. 29, no. 3, pp. 4128–4144, Jan. (2022), doi: https://doi.org/10.1007/s11356-021-15325-z. [Google Scholar]
- V. Bali, A. Kumar, and S. Gangwar, “A Novel Approach for Wind Speed Forecasting Using LSTM-ARIMA Deep Learning Models,” Int. J. Agric. Environ. Inf. Syst., vol. 11, no. 3, pp. 13–30, Jul. (2020), doi: https://doi.org/10.4018/IJAEIS.2020070102. [Google Scholar]
- P. Valsaraj, D. Alex Thumba, and K. Satheesh Kumar, “Spatio-temporal independent applicability of one time trained machine learning wind forecast models: a promising case study from the wind energy perspective,” Int. J. Sustain. Energy, vol. 41, pp. 1164–1182, (2022), doi: https://doi.org/10.1080/14786451.2022.2032060. [Google Scholar]
- J. Kim, H. Kim, H. Kim, D. Lee, and S. Yoon, “A comprehensive survey of deep learning for time series forecasting: architectural diversity and open challenges,” Artif. Intell. Rev., vol. 58, p. 216, (2025), doi: https://doi.org/10.1007/s10462-025-11223-9. [Google Scholar]
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