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Robust wind speed estimation with modified fuzzy regression functions with a noise cluster

机译：Robust wind speed estimation with modified fuzzy regression functions with a noise cluster

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The wind is gaining prominence as a renewable energy source as the climate change burden worsens. One of the main components of wind is wind speed, which has many outlier observations that differ significantly from the other measurements. Robust methods are needed to reliably analyse wind speed data by minimising the impact of outliers. The modified fuzzy regression functions framework (MFRFN) using a noise cluster is shown to be robust to the presence of outliers. The current study employs the MFRFN framework for robust wind speed estimation. It compares the estimation accuracy of the MFRFN framework to deep neural networks (DNNs) and support vector machines (SVMs) for wind speed estimation with an extensive dataset including more than 12,000 on-shore and off-shore locations for each month and at the annual level. Then, the MFRFN framework is implemented to produce wind speed estimates at 50 m, and annual, and monthly robust global wind speed potential maps are created. The best monthly and annual MFRFN models are also validated using out-of-sample ground measurements from 572 locations. Consequently, the MFRFN framework performs better than DNNs and SVMs for wind speed estimation and provides us with a robust estimation method for wind speed potential at a given location over the globe.

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