Development of an Artificial Intelligence-Based Miniature Solar Tracking System for Enhanced Solar Efficiency in Compact and Off-Grid Environments

Authors

  • Shihab Adil Abdul Rahim Al-Barram Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Section 13, 40100 Shah Alam, Selangor, Malaysia
  • Kinn Abass Bakon Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Section 13, 40100 Shah Alam, Selangor, Malaysia
  • Asmaa Mahfod Hezam Al-Hakimi Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Section 13, 40100 Shah Alam, Selangor, Malaysia

Keywords:

Solar tracking system, artificial intelligence, solar efficiency, off-grid power

Abstract

Fixed miniature solar systems lose significant energy because they cannot follow the sun's movement. This study develops an artificial intelligence-based miniature dual-axis tracking prototype with Internet of Things monitoring to maximize energy capture. The methodology employs a physics-informed framework and Random Forest Regression tested across 14,443 data points. Findings show the dual-axis tracking achieves a 37.5 % power increase over fixed systems, with tracking errors minimized to 0.03-0.1 degrees. This results in a total energy gain of up to 65 %, while humidity variations showed no significant impact on performance. This project uniquely combines lightweight machine learning with portable hardware for real-time safety support. It offers a practical renewable energy solution for students and rural off-grid users.

Author Biographies

Kinn Abass Bakon, Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Section 13, 40100 Shah Alam, Selangor, Malaysia

kinn_abbass@msu.edu.my

Asmaa Mahfod Hezam Al-Hakimi, Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Section 13, 40100 Shah Alam, Selangor, Malaysia

asmaa@msu.edu.my

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Published

2026-10-11

How to Cite

Shihab Adil Abdul Rahim Al-Barram, Kinn Abass Bakon, & Asmaa Mahfod Hezam Al-Hakimi. (2026). Development of an Artificial Intelligence-Based Miniature Solar Tracking System for Enhanced Solar Efficiency in Compact and Off-Grid Environments . Future Energy and Environment Letters , 8(1), 24–32. Retrieved from http://karyailham.com.my/index.php/feel/article/view/1330

Issue

Vol. 8 No. 1: September (2026)

Section

Articles

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