A Short-Term Wind Power Forecasting Approach using ANFIS
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.
Country : Kenya
1 Joseph N. Mathenge2 David K. Murage3 John N. Nderu
MSc Student, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Professor, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
Professor, Department of Electrical and Electronic Engineering, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
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