AIoT Driven Smart Solar System for Real Time Predictive Sustainable Energy Management
DOI:
https://doi.org/10.33050/italic.v4i1.968Keywords:
AIoT, Smart Solar System, Renewable Energy, Predictive Analytics, Machine LearningAbstract
The rapid expansion of solar photovoltaic (PV) technologies has increased the demand for intelligent, adaptive, and data-driven energy management systems. However, conventional and IoT only solar infrastructures still face limitations, including inefficient energy distribution, delayed fault detection, and an inability to respond dynamically to fluctuating environmental conditions. This study proposes an AIoT-based Smart Solar System that integrates IoT-enabled sensing modules with artificial intelligence for real-time monitoring, predictive analytics, and autonomous control. The system employs a distributed architecture consisting of edge devices, cloud analytics, and machine learning models particularly Long Short-Term Memory (LSTM) networks and regression-based predictors to enhance forecasting accuracy and operational responsiveness. The objective of this research is to improve power utilization, predictive reliability, and maintenance efficiency within solar energy systems. Experimental results demonstrate a 22.8% increase in power utilization, a 17% reduction in maintenance downtime, and a forecasting accuracy of 95.2% (R2 = 0.952). These findings indicate that AIoT integration significantly enhances energy intelligence, system reliability, and sustainability. Overall, the proposed architecture establishes a scalable foundation for next generation renewable energy systems capable of self learning, adaptive optimization, and real-time decision making.
References
M. N. Singh, N. ISBM COE, K. Srivastava, C. Khemkar, and N. ISBM COE, “Ai & iot for smart energy management in green systems,” J Eng Technol Manag, vol. 77, pp. 519–547, 2025.
Q. Aini, D. Manongga, U. Rahardja, I. Sembiring, and Y.-M. Li, “Understanding behavioral intention to use of air quality monitoring solutions with emphasis on technology readiness,” International Journal of Human–Computer Interaction, pp. 1–21, 2024.
M. Choiri, E. S. Pramudito, F. Sutisna, and R. S. Sean, “Business artificial intelligence for enhancing sustainable decision intelligence,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 106–116, 2025.
F. A. Alaba, U. Sani, E. G. Dada, and B. H. Mohammed, “Aiot-enabled smart grids: advancing energy efficiency and renewable energy integration,” in Artificial Intelligence of Things for Achieving Sustainable Development Goals. Cham: Springer Nature Switzerland, 2024, pp. 59–79.
A. Safari, M. Daneshvar, and A. Anvari-Moghaddam, “Energy intelligence: A systematic review of artificial intelligence for energy management,” Applied Sciences, vol. 14, no. 23, p. 11112, 2024.
Q. Wei, H. Sun, J. Fan, G. Li, and Z. Zhou, “Aiot based visual anomaly detection in photovoltaic sequence data via sequence learning,” Energies, vol. 17, no. 21, p. 5369, 2024.
K. Stecuła, R. Wolniak, and W. W. Grebski, “Ai driven urban energy solutions from individuals to society: A review,” Energies, vol. 16, no. 24, p. 7988, 2023.
Government of Indonesia, “Peraturan presiden republik indonesia no. 22 tahun 2017 tentang rencana umum energi nasional (ruen),” 2017, presidential Regulation of the Republic of Indonesia.
F. S. Alharithi and A. A. Alzahrani, “Enhancing environmental sustainability with federated lstm models for ai-driven optimization,” Alexandria Engineering Journal, vol. 108, pp. 640–653, 2024.
A. K. Salama and M. M. Abdellatif, “Aiot-based smart home energy management system,” in Proceedings of the 2022 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT). IEEE, 2022, pp. 177–181.
U. Rahardja, Q. Aini, A. S. Bist, S. Maulana, and S. Millah, “Examining the interplay of technology readiness and behavioural intentions in health detection safe entry station,” JDM (Jurnal Dinamika Manajemen), vol. 15, no. 1, pp. 125–143, 2024.
R. de Andrade Fernandes, C. T. da Costa, R. C. S. Gomes, and N. L. Vilac¸a, “Smartlvenergy: An aiot framework for energy management through distributed processing and sensor actuator integration in legacy low voltage systems,” IEEE Sensors Journal, vol. 24, no. 13, pp. 20 726–20 741, 2024.
A. W. Ng, A. Wu, and E. T. Wut, “Ai optimized solar tracking system for green and intelligent building development in an urban environment,” in Handbook of Sustainability Science in the Future: Policies, Technologies and Education by 2050. Springer, 2022, pp. 1–17.
E. Lodhi, X. Liu, G. Xiong, M. A. Khan, Z. Lodhi, T. Nawaz, and F. Wang, “Smartpv-aiot: An aiot integrated framework for fault diagnosis and remote monitoring in photovoltaic systems,” Energy Conversion and Management: X, p. 101117, 2025.
F. A. Alijoyo, “Ai powered deep learning for sustainable industry 4.0 and internet of things: Enhancing energy management in smart buildings,” Alexandria Engineering Journal, vol. 104, pp. 409–422, 2024.
O. H. Boucif, A. M. Lahouaou, D. E. Boubiche, and H. Toral-Cruz, “Artificial intelligence of things for solar energy monitoring and control,” Applied Sciences, vol. 15, no. 11, p. 6019, 2025.
S. El Himer, M. Ouaissa, M. Ouaissa, and Z. Boulouard, “Artificial intelligence of things for renewable energies systems,” in Artificial Intelligence of Things for Smart Green Energy Management. Cham: Springer International Publishing, 2022, pp. 1–13.
S. Z. Mir, M. K. Majeed, M. H. S. Siddiqui, S. K. Khawer, I. Younus, S. R. Shah, and A. Shahid, “Integrating ai and iot in solar power systems for improved monitoring and predictive maintenance,” Spectrum of Engineering Sciences, vol. 3, no. 4, pp. 971–982, 2025.
D. Sunaryo, E. P. Lestari, S. Puryandani, and H. Hersugondo, “Driving shareholder value through techno-preneurship innovation,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 751–765, 2025.
A. Yueanket, S. Phunklang, S. Kornsing, M. Kupimai, A. Innok, T. Pumpoung, and S. Todnatee, “Aiot-based water management solutions for sustainable sugarcane farming,” in 2024 International Conference on Power, Energy and Innovations (ICPEI). IEEE, 2024, pp. 19–23.
M. R. AT, B. B, S. A. P. RR, R. C. Naidu, R. K. M, P. Ramachandran, and A. M. Siddiqui, “Intelligent energy management across smart grids deploying 6g iot, ai, and blockchain in sustainable smart cities,” IoT, vol. 5, no. 3, pp. 560–591, 2024.
R. Pathak, N. Sharma, and P. Ranka, “Smart greenhouse management system using aiot for sustainable agriculture,” in Explainable AI (XAI) for Sustainable Development. Chapman and Hall/CRC, 2024, pp. 127–143.
T. Suminar, J. Sutarto, Y. Siswanto, and A. D. Cahyani, “Creating a technopreneurship-based life skills education model with methods of project-based learning,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 567–581, 2025.
S. R. Biswal, T. R. Choudhury, S. B. Santra, B. Panda, S. Mishra, and S. Padmanaban, “Simplified prediction based ai iot model for energy management scheme in standalone pv powered greenhouse,” IEEE Journal of Emerging and Selected Topics in Industrial Electronics, 2024.
X. Wen, Q. Shen, W. Zheng, and H. Zhang, “Ai driven solar energy generation and smart grid integration: A holistic approach to enhancing renewable energy efficiency,” Academia Nexus Journal, vol. 3, no. 2, 2024.
S. R. Joshua, A. N. Yeon, S. Park, and K. Kwon, “A hybrid machine learning approach: Analyzing energy potential and designing solar fault detection for an aiot-based solar–hydrogen system in a university setting,” Applied Sciences, vol. 14, no. 18, p. 8573, 2024.
O. J. Alshawabkeh, M. A. Nisirat, and B. R. Qawasmeh, “The use of aiot-integrated approaches to detect accumulated dust on solar panels utilizing color recognition based techniques,” Renewable Energy, vol. 256, p. 124155, 2026.
A. Aprillia, C. Kuswoyo, A. Kristiawan, R. A. Sunarjo, and R. A. Te Awhina, “Cyberpreneurship research trends and insights from 1999 to 2023,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 390–403, 2024.
S. R. Joshua, S. Park, and K. Kwon, “Knowledge based modeling approach: A schematic design of artificial intelligence of things for hydrogen energy system,” in 2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC). IEEE, 2024, pp. 0235–0241.
D. Y. Kristiyanto, H. D. Purnomo, G. P. Cesna, and N. Ani, “The strategic role of orange technology in cultivating innovation and well-being,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 27–37, 2025.
R. de Andrade Fernandes, C. T. da Costa, R. C. S. Gomes, and N. L. Vilac¸a, “Smartlvenergy: An aiot framework for energy management through distributed processing and sensor actuator integration in legacy low voltage systems,” IEEE Sensors Journal, vol. 24, no. 13, pp. 20 726–20 741, 2024.
B. A. Jnr, “Decentralized aiot based intelligence for sustainable energy prosumption in local energy communities: A citizen centric prosumer approach,” Cities, vol. 152, p. 105198, 2024.
C. Lukita, T. Handra, F. P. Oganda, and M. Laurens, “Data-driven innovation for circular digital economy in sustainable urban development,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 7, no. 1, pp. 97–105, 2025.
A. Anubha, D. Narang, and A. Raj, “Aiot-enabled smart homes solution to security, comfort, and energy efficiency,” in Merging Artificial Intelligence With the Internet of Things. IGI Global ScientificPublishing, 2025, pp. 239–268.
Government of Indonesia, “Presidential regulation of the republic of indonesia no. 112 of 2022 on the acceleration of renewable energy development for electricity supply,” Jakarta, Indonesia, 2022, regulates acceleration, pricing mechanisms, and incentives for renewable energy-based electricity generation.
S. R. Ahmed, T. A. Taha, S. M. Karim, P. Shah, A. S. T. Hussain, N. Itankar, and O. K. Ahmed, “Solar street lighting revolution: A sustainable approach enabled by aiot and smart systems,” in International Conference on Forthcoming Networks and Sustainability in the AIoT Era. Cham: Springer Nature Switzerland, 2024, pp. 378–390.
Q. Wei, H. Sun, J. Fan, G. Li, and Z. Zhou, “Aiot-based visual anomaly detection in photovoltaic sequence data via sequence learning,” Energies, vol. 17, no. 21, p. 5369, 2024.
A. A. Akinsemolu and H. Onyeaka, “The role of green education in achieving the sustainable development goals: A review,” Renewable and Sustainable Energy Reviews, vol. 210, p. 115239, 2025.
H. Zhou, Q. Liu, K. Yan, and Y. Du, “Deep learning enhanced solar energy forecasting with ai driven iot,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, p. 9249387, 2021.
F. A. Alaba, U. Sani, E. G. Dada, and B. H. Mohammed, “Aiot enabled smart grids: Advancing energy efficiency and renewable energy integration,” in Artificial Intelligence of Things for Achieving Sustainable Development Goals. Cham: Springer Nature Switzerland, 2024, pp. 59–79.
S. R. Ahmed, A. S. T. Hussain, D. A. Majeed, Y. S. Jghef, J. F. Tawfeq, T. A. Taha, and O. K. Ahmed, “Machine learning for sustainable power systems: Aiot optimized smart grid inverter systems with solar photovoltaics,” in International Conference on Forthcoming Networks and Sustainability in the AIoT Era, p. 368.
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Copyright (c) 2025 Rizki Indrawan, Eka Dawn Very, Dhimas Tribuana, Efa Ayu Nabila
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