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 Wind Power Forecasting Approach using ANFIS

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

Vol 5 No 5 (2021): Volume 5, Issue 5, May 2021 | Pages: 35-42

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 09-05-2021

doi Logo doi.org/10.47001/IRJIET/2021.505007

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Abstract
In recent times, there has been a growing penetration of wind power in electrical grids globally. On one hand, this has led to the reduction in the cost of unit power while on the other hand brought about the challenge of ensuring grid reliability in cases where the penetration of wind power is high. This is brought about by the high intermittency and limited predictability of wind power hence making it a non-dispatchable resource, unlike conventional power sources. As a result of this, there has been a parallel growth in the research around wind speed and wind power forecasting techniques to ensure that the maximum benefits of wind power are realized at the cheapest cost possible. Neural networks have been at the core of this research owing to their ability to learn, versatility in handling time-series data, and their strength in establishing non-linear relationships between input and output datasets. In this paper, a hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) is enhanced using previous hour wind power and wind speed data to improve its accuracy in short-term wind forecasting. 
Keywords

Wind Power Prediction, Short Term Wind Forecasting, Neural Networks


Citation of this Article

Joseph N. Mathenge, David K. Murage, John N. Nderu, “A Short-Term Wind Power Forecasting Approach using ANFIS” Published in International Research Journal of Innovations in Engineering and Technology - IRJIET, Volume 5, Issue 5, pp 35-42, May 2021. Article DOI https://doi.org/10.47001/IRJIET/2021.505007

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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