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/10.1088/1742-6596/1964/4/042064Additional details
Identifiers
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