Neural Network modelling for prediction of energy in hybrid renewable energy systems

J. Femila Roseline; D. Dhanya; Saravana Selvan; M. Yuvaraj; P. Duraipandy; S. Sandeep Kumar; A. Rajendra Prasad; Ravishankar Sathyamurthy; V. Mohanavel

Energy Reports (Nov 2022)

Neural Network modelling for prediction of energy in hybrid renewable energy systems

J. Femila Roseline,
D. Dhanya,
Saravana Selvan,
M. Yuvaraj,
P. Duraipandy,
S. Sandeep Kumar,
A. Rajendra Prasad,
Ravishankar Sathyamurthy,
V. Mohanavel

Affiliations

J. Femila Roseline: Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 600124, Tamil Nadu, India
D. Dhanya: Department of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Tamil Nadu 629171, India
Saravana Selvan: Faculty of Engineering and Computer Technology, AIMST University, Bedong, Kedah, Malaysia
M. Yuvaraj: Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu 639113, India
P. Duraipandy: Department of Electrical and Electronics Engineering, J B Institute Of Engineering And Technology, Hyderabad, Telangana 500075, India
S. Sandeep Kumar: Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, India
A. Rajendra Prasad: Department of Mechanical Engineering, Sri Sairam Engineering College, Chennai 600044, Tamil Nadu, India; Corresponding author.
Ravishankar Sathyamurthy: Department of Mechanical Engineering, KPR Institute of Engineering and Technology, Coimbatore, Arasur 641407, Tamil Nadu, India; Department of Mechanical Engineering, University Centre for Research & Development, Chandigarh University, Gharuan, Mohali, Punjab, India
V. Mohanavel: Centre for Materials Engineering and Regenerative Medicine, Bharath Institute of Higher Education and Research, Chennai 600073, Tamil Nadu, India; Department of Mechanical Engineering, School of Technology, Glocal University, Delhi-Yamunotri Marg, Uttar Pradesh, 247121, India

Journal volume & issue: Vol. 8
pp. 999 – 1008

Abstract

Read online

When it comes to the expansion of the renewable energy business in today technological age, the ability to predict power and energy output based on shifting weather patterns is crucial. It is possible to support and even improve an economy and quality of life by using renewable energy sources rather than traditional fossil fuels, rather than by using fossil fuels at all. Because global warming and climate change are posing serious challenges to our planet, the findings of this study may be valuable in the development of smart grids that can properly predict future weather conditions. In this study, we develop an artificial neural network (ANN) model to estimate the energy generated at PV and the energy from the hybrid PV and wind energy systems considering several weather factors. The modelling is conducted to potentially predict the energy generation. The results shows that the proposed classifier is efficient in terms of reduced mean squared error with increased accuracy than other methods.

Published in Energy Reports

ISSN: 2352-4847 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: http://www.journals.elsevier.com/energy-reports/

About the journal

Abstract

Keywords