AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India

Year : 2026 | Volume : 14 | Special Issue 02 | Page : 234 242
    By

    Kusum Tharani,

  • K. Sudha,

  • Surinder Kaur,

  • Manish Talwar,

  • Neeraj Kumar,

  1. Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  2. Associate Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  3. Associate Professor, Department of Information Technology, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  4. Assistant Professor, Department of Instrumentation and Control Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India
  5. Associate Professor, Department of Electrical and Electronics Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract

Lightning strikes are a major climate-related threat to India, resulting in severe human injuries as well as regular damages to the power transmission network and renewable energy infrastructure. This research aims to introduce the concept of an AI-based lightning strike prediction and mitigation system with the integration of polymers for making climate-resilient energy infrastructure. Multidata are collected based on satellite images, climate variables, as well as surface-based sensing modules, and are further processed with machine learning algorithms such as the Random Forest approach, Convolutional Neural Network models, and Long Short-Term Memory network models for accurate spatial-temporal predictions of lightning strikes. One of the primary contributions of this research work is the adaptation of advanced polymer and composite materials. Conductive polymers and polymer nanocomposites are proposed to be identified and analyzed for developing lightweight lightning sensors and protectors. The increased electronic conductivity and dielectric strength of these materials enable their effective use in smart grid applications. The development of AI-based predictive systems and the adaptation of advanced polymer and composite technologies and materials can improve lightning warning systems and create smart and reliable energy systems in India. The development of advanced AI-based predictive systems provides an increased degree of accuracy and reliability. The increased accuracy of AI-based systems reduces the reliance on manual calculations.

Keywords: Lightning prediction, artificial intelligence, machine learning, conductive polymers, smart materials, smart grid protection.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
Kusum Tharani, K. Sudha, Surinder Kaur, Manish Talwar, Neeraj Kumar. AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India. Journal of Polymer & Composites. 2026; 14(02):234-242.
How to cite this URL:
Kusum Tharani, K. Sudha, Surinder Kaur, Manish Talwar, Neeraj Kumar. AI-Driven Lightning Strike Prediction Using Polymer-Integrated Sensor Platforms for Climate-Resilient Energy Systems in India. Journal of Polymer & Composites. 2026; 14(02):234-242. Available from: https://journals.stmjournals.com/jopc/article=2026/view=239665


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Special Issue Subscription Original Research
Volume 14
Special Issue 02
Received 23/01/2026
Accepted 13/02/2026
Published 03/04/2026
Publication Time 70 Days
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