Best Faculty Award
Ramesh Kumar V
C Byregowda Institute of Technology, India
Ramesh Kumar V is a faculty member affiliated with C Byregowda Institute of Technology, India, whose documented research activity is associated with computer science and computational approaches to electrical and energy-related systems. His indexed publication record includes work on artificial neural networks for electrical load forecasting and converter-based hybrid power systems. The available bibliographic information provides a basis for considering his academic and research contributions in the context of the Best Faculty Award.
Abstract
The academic record of Ramesh Kumar V demonstrates research engagement at the intersection of computer science, artificial neural networks, forecasting, and energy systems. His publications address daily and hourly peak-load forecasting and a bidirectional converter for hybrid power systems. These studies illustrate the application of computational intelligence and power-electronic methods to practical engineering problems. Bibliographic records identify three Scopus-indexed documents, 22 citations, and an h-index of 2 in the supplied profile data.
Keywords
Computer Science; Artificial Neural Networks; Load Forecasting; Energy Systems; Hybrid Power Systems; Power Electronics; Academic Research; Faculty Excellence.
Introduction
Forecasting electricity demand is an important computational problem because reliable predictions can support planning, scheduling, and efficient operation of electrical networks. Ramesh Kumar V’s published studies examine artificial neural-network approaches to peak-load prediction, while his later work considers converter technology within a hybrid power system. The combination of computational modelling and energy applications represents a practical research direction within computer science and engineering.
Research Profile
The supplied Scopus information records Ramesh Kumar V under author ID 57208926798, with three documents, 22 citations, and an h-index of 2. His documented research themes include artificial neural networks, peak-load forecasting, and hybrid power-system conversion. The publication record indicates an applied orientation, connecting computational methods with energy and electrical engineering challenges.
Research Contributions
- Application of artificial neural networks to daily peak-load forecasting.
- Development and evaluation of an artificial neural-network model for hourly peak-load forecasting.
- Research on transformerless bidirectional converter technology for hybrid power systems.
Publications
Transformerless Bidirectional Converter Fed Hybrid Power System (2022), published as a book chapter in Lecture Notes in Electrical Engineering. [1]
Daily Peak Load Forecast Using Artificial Neural Network (2019), published in International Journal of Electrical and Computer Engineering. [2]
Artificial neural network model for hourly peak load forecast (2018), published in International Journal of Energy Economics and Policy, with Scopus record identifier. [3]
Research Impact
The citation record supplied for the researcher indicates measurable scholarly visibility, with 22 citations and an h-index of 2. The publications address applied problems that are relevant to energy-demand prediction and hybrid power-system operation. Such research can contribute to the broader development of data-driven approaches for electrical-system planning and operational analysis.
Award Suitability
The documented publication activity provides evidence relevant to evaluation for a Best Faculty Award, particularly in research-oriented academic contribution. His work demonstrates continuity across computational forecasting and energy-system applications, supported by indexed publications and citation activity. Final award assessment should consider the complete academic record, including teaching quality, institutional service, mentorship, innovation, publications, and independently verified research achievements.
Conclusion
Ramesh Kumar V’s documented profile reflects an applied research focus combining computer science methods with electrical and energy-system problems. His publications on neural-network forecasting and hybrid power conversion provide a coherent basis for academic recognition, subject to comprehensive evaluation of his broader faculty and scholarly record.
External Links
References
- Springer. (2022). Transformerless Bidirectional Converter Fed Hybrid Power System. Lecture Notes in Electrical Engineering. DOI: 10.1007/978-981-16-3690-5_107.
https://doi.org/10.1007/978-981-16-3690-5_107
- International Journal of Electrical and Computer Engineering. (2019). Daily Peak Load Forecast Using Artificial Neural Network. 9(4), 2256–2263. DOI: 10.11591/ijece.v9i4.pp2256-2263.
https://doi.org/10.11591/ijece.v9i4.pp2256-2263
- International Journal of Energy Economics and Policy. (2018). Artificial neural network model for hourly peak load forecast. Scopus record 2-s2.0-85053004165.
Scopus record
- Elsevier. (n.d.). Scopus author details: Ramesh Kumar V, Author ID 57208926798. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57208926798
- ORCID. (n.d.). ORCID record for Ramesh Kumar V.
https://orcid.org/0000-0003-3226-4986
- International Academic Excellence Awards. (n.d.). Academic Excellence Awards.
https://academicexcellenceawards.com/