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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68410
Volume:
10 |
10 |
Oct 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electrical Electronics Engineering
K.L.N College of engineering
Electrical Electronics Engineering
K.L.N College of engineering
Electrical Electronics Engineering
K.L.N College of engineering
Electrical Electronics Engineering
K.L.N College of engineering
Solar photovoltaic (PV) systems with battery storage are spreading quickly, yet many installations still rely on handheld meters and local displays that give no remote access, no historical record and little battery information. This paper presents a sensor-based Internet of Things (IoT) platform for real-time monitoring and cloud analytics of a solar PV generation and battery energy storage system. The architecture senses PV voltage and current, battery voltage, charging current and temperature, ambient temperature, humidity and solar irradiance, and estimates battery state of charge (SoC) by Coulomb counting. Conditioned data are sent over Wi-Fi or a GSM link to the ThingSpeak cloud, where live dashboards and exportable records build a historical dataset. A prototype based on a NodeMCU ESP8266, a DHT22 sensor, a light sensor, voltage and current sensing modules and a 16×2 LCD for local display was tested outdoors. Every reading was uploaded to ThingSpeak, temperature stayed between about 31.9 °C and 32.5 °C, humidity between 61 % and 66 %, and the LCD showed the panel voltage and current at the site. The platform offers a low-cost, expandable base for fault detection, forecasting and battery analytics using machine learning.
KEYWORDS
A sensor-based IoT platform for real-time monitoring and cloud analytics of a solar PV and battery storage system was designed and a prototype was built and tested outdoors. The prototype measured voltage, current, temperature and humidity, showed voltage and current on a local LCD, uploaded every reading to ThingSpeak and produced live charts and an exportable dataset, replacing manual recording with remote, continuous monitoring. Future work includes adding battery current and temperature channels, completing SoC estimation by Coulomb counting, integrating the STM32 controller and GSM link, calibrating all sensors, and collecting a longer dataset. This dataset can then be used to train ML and DL models for power and irradiance forecasting, panel and sensor fault detection, and battery SoC, SoH and remaining useful life estimation.
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Publication Date
10/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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