Wind speed
The MATLAB data file “wind_speed_2015_2016_10minutely.mat” was obtained based on the original wind speed data downloaded from "Iowa Environmental Mesonet: AWOS Network Database" at the localities of Le Mars, Orange City, and Sheldon in Iowa, USA, recorded from 2015 to 2016. The raw data set has varying resolution, ranging from 5 to 10 min per sample. A fraction of missing measurements were filled in by interpolation. The resulting data-set was then re-sampled at a fixed rate of 10 min per sample, resulting in 105120 data points for each location.
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Four groups of wind speed series
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This dataset is in support of my research paper 'Analysis of Power Generation And Turbine Characteristics of 2.2 kW Residential Wind Generators'.
Related Claim : Novel ß Wind Turbine Urban Residential Controller and Novel ß Wind Vibration Octa Axis Harvesting System in Patent 'Novel ß 10-Axis Grid Compatible Multi-Controller'
Preprint :
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This is the data Archive for Zhang, et al., “A Geophysical Model Function for S-band Reflectometry of Ocean Surface Winds in Tropical Cyclones,” accepted by Geophysical Research Letters. This data set was generated from twelve (12) days of airborne S-band (2.3 GHz) reflectometry data collected during the 2014 hurricane season between 2 July 2014 and 17 September 2014. Cross-correlations between the direct and reflected S-band signals, commonly referred to as the “waveform” or delay-Doppler map (DDM) are provided with corresponding aircraft time and position data.
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With the advent of huge data availability and cheap processing power to derive insights from data ,applications of Big Data and Machine Learning are gaining popularity in every industry. Now, Predicting about future is no more an alternate but a necessity to increase efficiency with accurate output. Forecasting for a shorter duration (Nowcasting) fits perfectly in this space to estimate the final production for better planning and control.
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