Published July 1, 2021 | Version v1
Journal article

Prediction of the Speed and Wind Direction Using Machine Learning

  • 1. Department of Electrical and Computer Engineering, Bule Hora University, Bule Hora (Ethiopia)
  • 2. Department of Electrical and Electronics Engineering, Meenakshi Sundararajan Engineering College, Chennai, Tamil Nadu (India)
  • 3. Department of Mechanical Engineering, Brilliant Group of Technical Institutions, Hyderabad, Telangana (India)
  • 4. Department of Mechanical Engineering, Kongunadu College of Engineering and Technology, Tholurpatti, Tamil Nadu (India)

Description

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. (paper)

Availability note (English)

Available from http://dx.doi.org/10.1088/1742-6596/1964/4/042064

Additional details

Publishing Information

Journal Title
Journal of Physics. Conference Series (Online)
Journal Volume
1964
Journal Issue
4
Journal Page Range
[5 p.]
ISSN
1742-6596

Conference

Title
1. International Conference on Advances in Computational Science and Engineering
Acronym
ICACSE 2020
Dates
25-26 Dec 2020
Place
Coimbatore, Tamilnadu (India)

INIS

Country of Publication
United Kingdom
Country of Input or Organization
International Atomic Energy Agency (IAEA)
INIS RN
53094167
Subject category
S17: WIND ENERGY; S54: ENVIRONMENTAL SCIENCES;
Resource subtype / Literary indicator
Conference
Descriptors DEI
CLIMATE MODELS; ENERGY SYSTEMS; FREE ENERGY; MACHINE LEARNING; OPTIMIZATION; REGULATIONS; WIND; WIND POWER; WIND POWER PLANTS
Descriptors DEC
ALGORITHMS; ARTIFICIAL INTELLIGENCE; ENERGY; ENERGY SOURCES; LAWS; LEARNING; MATHEMATICAL LOGIC; MATHEMATICAL MODELS; PHYSICAL PROPERTIES; POWER; POWER PLANTS; RENEWABLE ENERGY SOURCES; THERMODYNAMIC PROPERTIES