In large distribution networks and distributed energy, wind energy is critical. Grid power balance relies heavily on accurate wind farm predictions. For effectively extraction characteristics using wind time series analysis, this poster offers a time -series data fuzzy c-means grouping technique and also a clusters selection algorithm. Clustering analysis is an effective approach for data processing which is often utilised. Cluster analysis is used to partition large datasets in subgroups based on similarity as differences. A wavelet decomposition is being used to split down wind energy output and to provide the MLNARx with more appropriate inputs. A comparison with well-known estimation methods reveals that the suggested estimation method outperforms them.
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