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Statistical Analysis and Forecasting of Wind Speed

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dc.contributor.author Pasari, Sumanta
dc.date.accessioned 2023-08-14T06:55:37Z
dc.date.available 2023-08-14T06:55:37Z
dc.date.issued 2022
dc.identifier.uri https://ieeexplore.ieee.org/document/9798358
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/11370
dc.description.abstract Energy plays a vital role in urbanization and industrialization. Wind energy is highly valuable and accurate forecasts can help determine the best locations to set up windmills. Using a dataset comprising wind speeds from 15 years (2000–2014) within two locations of Rajasthan, namely Jaipur and Jaisalmer, we present a detailed statistical analysis including distribution analysis and forecasting using Moving Average (MA), Auto-Regressive (AR), Auto-Regressive Moving Average (ARMA), Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We show empirically why SARIMA is the best model and why the former four models are inadequate when it comes to forecasting wind speeds. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Mathematics en_US
dc.subject Wind Speed Forecasting en_US
dc.subject ARMA en_US
dc.subject ARIMA en_US
dc.subject SARIMA en_US
dc.title Statistical Analysis and Forecasting of Wind Speed en_US
dc.type Article en_US


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