Dr. Jyotirmayee Naik

About

Dr. Jyotirmayee Naik is an accomplished researcher and academic specializing in renewable energy systems, machine learning, and DC microgrid fault analysis. She earned her Ph.D. in Electrical Engineering from Siksha ‘O’ Anusandhan University, India, where her work focused on developing hybrid signal processing and machine learning models for wind speed and power forecasting. Dr. Naik's research interests include renewable energy forecasting, energy management systems, and fault detection techniques for smart grids and microgrids. She has published extensively in high-impact international journals and actively contributes to the academic community as a reviewer and conference participant. With a strong foundation in teaching and research, she is committed to advancing knowledge in artificial intelligence, Machine learning, and sustainable energy technologies.

Employment

Employment history is unavailable.

Education

• SOA University Doctor of Philosophy (PhD)
  2016 - 2022
• SOA University Masters of Technology
  2014 - 2016
• Biju Patnaik University of Technology Bachelor of Technology
  2008 - 2012

Projects & Funding

Projects & funding information is unavailable.

Publications (30)

• Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
  Energy 2022
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• Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network
  Applied Soft Computing 2022 DOI: 10.1016/j.asoc.2022.109314
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• Fault analysis of photovoltaic based DC microgrid using deep learning randomized neural network
  Applied Soft Computing 2022
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• Short-time wind power prediction using hybrid kernel extreme learning machine
  International Journal of Power Electronics 2022 DOI: 10.1504/IJPELEC.2022.10046564
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• Short-time wind power prediction using hybrid kernel extreme learning machine
  International Journal of Power Electronics 2022
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• Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
  Energy 2022 DOI: 10.1016/j.energy.2021.122585
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• A Fruit Fly Optimization-Based Extreme Learning Machine for Biomedical Data Classification
  Smart Innovation, Systems and Technologies 2021 DOI: 10.1007/978-981-15-5971-6_15
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• An efficient Robust Random Vector Functional Link network for Solar Irradiance, Power and Wind speed prediction
  2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON) 2021
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• An efficient robust random vector functional link network for solar irradiance, power and wind speed prediction
  1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology, ODICON 2021 2021 DOI: 10.1109/ODICON50556.2021.9428977
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• Optimized droop controller based energy management for stand-alone micro-grid using hybrid monarch butterfly and sine-cosine algorithm
  Sustainable Energy Technologies and Assessments 2021 DOI: 10.1016/j.seta.2021.101310
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