Statistical Analysis and Forecasting of Wind Speed

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/11370

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DC Field	Value	Language
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
Appears in Collections:	Department of Mathematics

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