24-hours wind speed forecasting and wind power generation in La Serena (Chile). Issue 6 (December 2018)

• Record Type: Journal Article
• Authors: Salfate, Ignacio; López-Caraballo, Carlos H; Sabín-Sanjulián, Carolina; Lazzús, Juan A; Vega, Pedro; Cuturrufo, Fernando; Marín, Julio
• Is Part Of: Wind engineering. Volume 42:Issue 6(2018)
• Page Start: 607
• Page End: 623
• Publication Date: 2018-12
• Subjects: Wind speed forecasting -- artificial neural network -- wind power estimation -- renewable energy -- semi-arid zone; 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: 10.1177/0309524X18780381
• Languages: English
• ISSNs: 0309-524X

Abstract

This article presents 24-h wind speed forecasting for the city of La Serena in Chile and a methodology to explore forecasting effects on the production of wind turbine power. To that end, we used meteorological data from a weather station located in the southern zone of the hyper-arid Atacama Desert. In this area, energy resources are economically and environmentally important, and wind speed forecasting plays a vital role in the management and marketing processes of wind potential via wind farms. To contribute to the development of this energy, we propose carrying out the short-term prediction of 12 and 24 h ahead (identified as Ws ( t + 12) and Ws ( t + 24), respectively) using an artificial neural network with backpropagation approach. Hourly time series of wind speed, temperature, and relative humidity (from 2003 to 2006) were considered to characterize the artificial neural network in the training phase, while we used data from the year 2007 to check the efficiency of our prediction. For artificial neural network Ws ( t + 12) and Ws ( t + 24) models, we obtained similar performance of wind speed prediction with root mean square error of around 0.7 m s −1 and with maximum and minimum residuals of +4 and ‒4 m s −1, respectively. Based on the results, we gain a reliable tool to characterize wind speed properties in the range of 1 day within 20% of uncertainty. Moreover, this tool becomes useful to study the effects of our artificial neural network Ws ( t + 12) and Ws ( t + 24) models on the generation of wind energy from a wind power turbine parametrization.