Wind Energy Analysis and Forecast using Machine Learning

Halima Sadia; Krishna Tomar

doi:10.55524/ijircst.2022.10.4.10

Abstract

Better prediction tools for future solar and wind power are crucial to reducing the requirement for controlling energy associated with the conventional power facilities. For optimal power grid integrating of highly variable wind power output, a strong forecast is extremely crucial. In this part, we concentration on wind power for the near run projections and conduct a wind unification study in the western United States using data from the National Research Conducted by the university (NREL). Our approach derives functional connections directly from data, unlike physical systems that rely on exceedingly difficult differential calculus. By recasting the prediction problem as a regression problem, we investigate several regression methodologies such as regression models, k-nearest strangers, and regression algorithms. In our testing, we look at projections for specific machines along with power from the wind parks, proving that a classification algorithm for predicting short-term electricity generation is feasible.

Keywords

NREL, Wind Energy, Machine Learning.

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Cites this article as

H. Sadia, K. Tomar, "Wind Energy Analysis and Forecast using Machine Learning", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-10, Issue-4, Page No-85-91, 2022. Available from: https://doi.org/10.55524/ijircst.2022.10.4.10

Corresponding Author

Halima Sadia

M. Tech Scholar, Department of Electrical Engineering, RIMT University, Mandi Gobindgarh, Punjab, India

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