IoT-Enabled Self-Monitoring Polymer Composites for Solar Facade Systems with Cloud-Based Thermal Analytics

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 03 | Page :
    By

    Megha Mudholkar,

  • Pankaj Mudholkar,

  • Pavitar Singh,

  • Shakila Shaikh,

  • P. Santhi,

  • Pellakuri Vidyullatha,

  • Afshan Tabasum,

  1. Assistant Professor, Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, India
  2. Associate Professor, Faculty of Computer Applications, Marwadi University, Rajkot, Gujarat, India
  3. Assistant Professor, Department of Civil Engineering, School of Engineering and Technology, CGC University, Mohali, Punjab, India
  4. Assistant Professor, Department of Computer Engineering, Mukesh Patel School of Technology, Management & Engineering, SVKM’s NMIMS, Mumbai, Maharashtra, India
  5. Professor, Department of Artificial Intelligence and Data Science, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Tamil Nadu, India
  6. Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfields, Vaddeswaram, Guntur, Andhra Pradesh, India
  7. Student, Department of Pharmacy, Lado Institute, Virginia, USA

Abstract

The increasing demand for intelligent and energy-efficient building systems has hastened the production of smart solar facade systems. This paper presents an IoT-based self-monitoring polymer composite system, which includes thermal analytics running on the cloud to provide real-time assessments of the performance of the facade. The artificial composite panel has sensing capability built in which the material is able to measure both surface and internal thermal behavior simultaneously in dynamic environmental conditions. To assure the reliability of the experiment, a hybrid real-world data-set that integrates on-site and confirmed meteorological data were used. The system uses edge-based preprocessing, and cloud-based analytics to extract important thermal features, such as temperature gradients and response rates, and then detects anomalies in a composite scoring system. The experimental results showed that the system has a detection of 94.8% and 1.8-minutes response time, which is better than the traditional systems of IoT-based monitoring. The composite possesses a stable thermoresistive and thermal conductivity which is a testimony to its use in integrated sensing. The suggested framework will provide one of the potential solutions to these problems as it will combine the material-level intelligence with the system-level monitoring, which will facilitate gaining a better understanding of thermal, detect faults as soon as possible, and minimize the reliance on external sensors. The findings reveal what could be accomplished with self-observation composites to develop more next generation smart facades and sustainable building infrastructures.

Keywords: IoT-enabled monitoring, polymer composites, solar façade systems, thermal analytics, anomaly detection.

How to cite this article:
Megha Mudholkar, Pankaj Mudholkar, Pavitar Singh, Shakila Shaikh, P. Santhi, Pellakuri Vidyullatha, Afshan Tabasum. IoT-Enabled Self-Monitoring Polymer Composites for Solar Facade Systems with Cloud-Based Thermal Analytics. Journal of Polymer & Composites. 2026; 14(03):-.
How to cite this URL:
Megha Mudholkar, Pankaj Mudholkar, Pavitar Singh, Shakila Shaikh, P. Santhi, Pellakuri Vidyullatha, Afshan Tabasum. IoT-Enabled Self-Monitoring Polymer Composites for Solar Facade Systems with Cloud-Based Thermal Analytics. Journal of Polymer & Composites. 2026; 14(03):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=244136


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Ahead of Print Subscription Original Research
Volume 14
03
Received 23/04/2026
Accepted 15/05/2026
Published 16/05/2026
Publication Time 23 Days
15

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