Abstract
Humankind can survive to the peril of its life only if it accords with nature and its resources. Yet, we tend to degrade nature and use its resources till its phase of depletion. We have relied on natural resources, and wind energy is one of those. People have been trying to predict the nature of the wind, but predicting its existence isn’t an easy task. Certainly, one needs to take a lot of factors into consideration. As Albert Einstein quoted “When the number of factors coming into play in a phenomenological complex is too large, scientific method in most cases fails us. One needs to only think of the weather, in which case prediction even for a few days ahead is impossible.” [1], yet we tend to build supercomputers and generate algorithms that can give us accurate results on the weather. We have also tried to replicate the same. We analyzed the wind data obtained from the Copernicus Climate Change Service (C3S), which is a body under the European Union. We further analyzed the data by imputing various time series and neural network algorithm which leads us to foresee the upcoming wind speed in the European Union region. The outcomes from the statistical model could be used by wind farmers, industries, or researchers for their benefits and research.
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Sai Anand, M., Ramalakshmi, R. (2022). Time Series Analysis to Forecast Wind Speed. In: Peter, J.D., Fernandes, S.L., Alavi, A.H. (eds) Disruptive Technologies for Big Data and Cloud Applications. Lecture Notes in Electrical Engineering, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-19-2177-3_38
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DOI: https://doi.org/10.1007/978-981-19-2177-3_38
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