International Journal of Innovative Research in Computer Science and Technology
Year: 2022, Volume: 10, Issue: 4
First page : ( 85) Last page : ( 91)
Online ISSN : 2350-0557.
DOI: 10.55524/ijircst.2022.10.4.10 |
DOI URL: https://doi.org/10.55524/ijircst.2022.10.4.10
Crossref
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Halima Sadia , Krishna Tomar
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.
M. Tech Scholar, Department of Electrical Engineering, RIMT University, Mandi Gobindgarh, Punjab, India
No. of Downloads: 36 | No. of Views: 1033
Svetlana Orlova, Mikhail Tarasov, Anastasia Belova, Alexey Frolov, Tatiana Zykova, Viktor Melnikov, Krzysztof Zalewski.
March 2025 - Vol 13, Issue 2
Nitya Yadav, Pratibha, Ramendra Tripathi, Neha, Vivek Kushawah.
May 2024 - Vol 12, Issue 3
Yuan Anisa, Winda Erika, Fadhillah Azmi.
May 2024 - Vol 12, Issue 3