A novel time series data clustering approach for wind speed forecasting. Issue 4 (August 2022)

• Record Type: Journal Article
• Authors: Asif Kamal, Mh; Gyanchandaniyan, Manasi; Kushwah, Anil Kumar
• Is Part Of: Wind engineering. Volume 46:Issue 4(2022)
• Page Start: 1281
• Page End: 1290
• Publication Date: 2022-08
• Subjects: Wind time series -- ARIMA model -- Naive forecast model -- Holt Winter model -- hybrid model; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X221076976
• Languages: English
• ISSNs: 0309-524X

Abstract

Wind energy plays an essential role in the generation process of sustainable energy, with a bright future. Therefore, predicting wind speed fluctuations and their output power plays a crucial role in electric power generation. The integration of wind power is based on the accuracy of wind speed and power prediction model. In this paper, a clustering algorithm is proposed based on the length of the trendlet components. After spotting the different clusters, one suitable cluster is selected for modeling using the panda's correlation method. This paper uses specific ARIMA, Naive Forecast, and Holt Winter models to forecast the selected cluster. Here three hybrid models, namely, C-ARIMA, C-NAIVE Forecast, and C-Holt-Winter, are proposed for wind speed forecasting. The performances of the proposed models are evaluated using the mean absolute error (MAE) and root mean squared error (RMSE). The experiment outcomes show that the cluster-based forecasting technique (Hybrid models) improved performance compared with un-clustered forecasting techniques.