International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Short-Term Hybrid Wind Power Forecasting Approach using BiLSTM_EMD and the Avrami Curve

Joseph N. MathengeMSc Student, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, KenyaJohn N. NderuProfessor, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, KenyaDavid K. MurageProfessor, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya

Vol 7 No 10 (2023): Volume 7, Issue 10, October 2023 | Pages: 376-392

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 02-11-2023

doi Logo doi.org/10.47001/IRJIET/2023.710051

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Abstract

In recent times, the world has inclined towards using renewable energy sources since they are emission-free, occur freely in nature, and unlike fossil fuels, cannot be depleted. Wind power is one such renewable energy source that has attracted a lot of research and interest in the power industry. With the growing quantities of wind power generation incorporated into power systems, grid reliability is at risk since wind power is highly intermittent. Wind power forecasts facilitate incorporation of wind in a grid’s power mix more efficiently and reduce the quantity of power reserves allocated to cater to the intermittency of wind. This makes adopting more wind power resources into the grid more economical. In this paper, a novel approach to wind power forecasting is developed using Bidirectional Long Short-Term Memory Neural Networks (BiLSTM) hybridized with Empirical Mode Decomposition (EMD) then enhanced with a wind power curve layer derived from the Avrami Equation. The developed model was tested on an online-based dataset and compared with the traditional LSTM and other hybrid LSTM-data decomposition models. Using the developed BiLSTM + EMD enhanced with an Avrami Power Curve layer, wind power prediction improved by at least 50% compared to hybrid BiLSTM-data decomposition models. Modelling and coding were performed in MATLAB R2019a.  

Keywords

Wind Power Forecasting, Bi-directional Long Short Term Memory Network, Empirical Mode Decomposition, The Avrami Equation


Citation of this Article

Joseph N. Mathenge, John N. Nderu, David K. Murage, “A Short-Term Hybrid Wind Power Forecasting Approach using BiLSTM_EMD and the Avrami Curve” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 7, Issue 10, pp 376-392, October 2023. Article DOI https://doi.org/10.47001/IRJIET/2023.710051

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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