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All Journal Jurnal Rekayasa Sistem Industri Jurnal Riset Keuangan dan Akuntansi (JRKA) Dinamisia: Jurnal Pengabdian Kepada Masyarakat SEIKO : Journal of Management & Business JASa (Jurnal Akuntansi, Audit dan Sistem Informasi Akuntansi) Jurnal Pendidikan Akuntansi dan Keuangan Kurs : Jurnal Akuntansi, Kewirausahaan dan Bisnis Jurnal Ilmiah Edunomika (JIE) International Journal Of Science, Technology & Management (IJSTM) Komitmen : Jurnal Ilmiah Manajemen MOVE: Journal of Community Service and Engagement Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Fleksibel: Jurnal Pengabdian Kepada Masyarakat Journal Economic Excellence Ibnu Sina International Transactions on Artificial Intelligence (ITALIC) Jurnal Pemberdayaan Masyarakat (JUPEMAS)
Rizki Indrawan
Departement Of Accounting, Universitas Kuningan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Transportation Other

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AIoT Driven Smart Solar System for Real Time Predictive Sustainable Energy Management Indrawan, Rizki; Very, Eka Dawn; Tribuana, Dhimas; Nabila, Efa Ayu
International Transactions on Artificial Intelligence Vol. 4 No. 1 (2025): November
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v4i1.968

Abstract

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.
Co-Authors Aam Rachmat Mulyana amanah, nurul Amelia Setiawan Anisa Dwi Wahyuni Anissa Yuniar Larasati Anissa Yuniar Larasati Annisa Yuniar Larasati Cahyo Aji Pangestu Dea Amara Dhimas Tribuana Eka Septriarini Eni Wahyu Setyaningsih Hamfri Djajadikerta Heryanti, Ajeng Nuraeni Hilmii Rosyidatul Ummaht Iman Teguh Lukni Burhanudin Mali Amilusholihah Marisa Wijayanti Marlina Marlina Mawardiana, Doni Muhammad Fauzi Akbar Nabila, Efa Ayu Nurfatimah, Siti Nuke Oktaviani Rita Puspasari Oktaviani Rita Puspitasari Purwanto Purwanto Radite, Farhan Ali Wibowo Romli Romli, Romli Sofia Windarti Suhendar, Dadang Ummaht, Hilmii Rosyidatul Usman Sastradipraja Very, Eka Dawn Vicky Dzaky Cahaya Putra Wiwi Hartika