Scopus
🔓 Açık Erişim YÖKSİS DOI Eşleşti
SJR Q1
An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India
Intelligent Systems with Applications · Kasım 2022
YÖKSİS Kayıtları
An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India
Elsevier BV · 2022 SCOPUS
Doç. Dr. AHMET CEVAHİR ÇINAR →
YÖKSİS Kayıtları — ISSN Eşleşmesi
An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India
2022 ISSN: 2667-3053 SCOPUS
Doç. Dr. AHMET CEVAHİR ÇINAR →
Makale Bilgileri
ISSN26673053
Yayın TarihiKasım 2022
Cilt / Sayfa16
Scopus ID2-s2.0-85140075820
Erişim🔓 Açık Erişim
Özet
The growing population has tremendously increased the daily energy demand all around the world. India is the second-most crowded nation in the world with approximately 1.4 billion people. New and renewable energy is on the agenda of India and in 2021 India possesses the fourth-largest installed capacity of wind power. Accurate prediction of wind speed is vital in wind farm design and operation. In this work, an hourly wind speed prediction with an artificial neural network optimized by a metaheuristics approach is conducted. A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for the prediction of the hourly wind speed. In this study, 38 years of hourly wind data belonging to 5 cities (Ambur, Hosur, Kumbakonam, Nagapattinam, and Pudukottai) were used. These cities have different specific properties such as latitude, longitude, and altitude. The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. In this work, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Evolutionary Strategy (ES), Genetic Algorithm (GA), Grey-Wolf-Optimizer (GWO), Population-Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), Tree-Seed Algorithm (TSA) have been used to optimize the weights of the ANN. GWO outperforms other metaheuristic algorithms in the prediction of wind speed with a FF MLP ANN model, with a success percentage rate of approximately 3% to 10,000%.
Yazarlar (2)
1
Ahmet Cevahir Cinar
2
Narayanan Natarajan
Anahtar Kelimeler
Artificial neural network
Evolutionary algorithms
Metaheuristics
Swarm intelligence
Wind speed prediction
Kurumlar
Dr.Mahalingam College of Engineering & Technology
Pollachi India
Selçuk Üniversitesi
Selçuklu Turkey
Scimago Dergi (ISSN Eşleşmesi)
Intelligent Systems with Applications
Q1
OA
SJR Skoru1,142
H-Index45
YayıncıElsevier B.V.
ÜlkeNetherlands
Artificial Intelligence (Q1)
Computer Science Applications (Q1)
Computer Science (miscellaneous) (Q1)
Computer Vision and Pattern Recognition (Q1)
Signal Processing (Q1)
Metrikler
29
Atıf
2
Yazar
5
Anahtar Kelime