Open AccessPrediction of the Speed and Wind Direction Using Machine Learning
Author(s) -
Balachandra Pattanaik,
S. Manikandan,
S. Peniel Pauldoss,
S. Gobinath
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1964/4/042064
Subject(s) - wind power , wind speed , computer science , ant colony optimization algorithms , power (physics) , energy (signal processing) , tracking (education) , simulation , artificial intelligence , automotive engineering , meteorology , environmental science , engineering , electrical engineering , mathematics , statistics , psychology , pedagogy , physics , quantum mechanics
The wind is a free energy source; however, its high unpredictability is a significant integration problem of large wind power plant into an energy system. In a wind conversion system, the wind speeds are a vital power-generated tracking, regulation, schedules and dispatch and satisfy consumer requirements. This paper proposes using the machine learning (ML) based ant colony optimization (ACO) method for the wind speed prediction. A correlation among predicted and real data from climate models showed strong consensus. The significance of the current research depends on its ability to forecast wind speeds, a crucial precursor to performing successful incorporation of wind power.