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.