IoT-Enabled Cloud-Based Industrial Monitoring and Management Framework

DOI

10.2991/978-94-6239-654-8_18How to use a DOI?

Keywords

Intelligent Monitoring System (IMS); IoT Platform; Cloud Computing; LSTM Ensemble; Deep Learning; Fault Detection; Ensemble Learning

Abstract

The given paper introduces Intelligent Monitoring System (IMS) dedicated to Photovoltaic (PV) plants with the usage of the low-cost hardware and lightweight software that would make its deployment face-free in a variety of PV installations. The system has a platform that is based on the Internet of Things (IoT) platform, which allows the platform to communicate seamlessly, interoperate, and handle data in real-time. An embedded personal cloud server is added to perform effective computation and safe storage of PV system data, and a web-based monitoring interface is given to allow the visualization of several users. IMS enables the use of advanced deep ensemble-based learning in detecting faults and predicting power. The model forecasts PV output at different environmental conditions due to a long short-term memory (LSTM) ensemble model to enable optimal energy production and early detection of malfunctions. Fault diagnosis is done based on features found in Current voltage (I V) characteristics and with the help of an ensemble of Naive Bayes, K Nearest Neighbors and Support Vector Machine models with addition of a feature selection algorithm. An actual PV installation was used to test the system and proved the IMS is scalable, interoperable, and useful in the overall monitoring of PV plants, both data acquisition, performance measurement, fault identification, and predictive analytics.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dasaraju Chandra Mohan
AU  - R. Yogesh Rajkumar
PY  - 2026
DA  - 2026/04/24
TI  - IoT-Enabled Cloud-Based Industrial Monitoring and Management Framework
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 193
EP  - 207
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_18
DO  - 10.2991/978-94-6239-654-8_18
ID  - Mohan2026
ER  -