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All Journal International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) International Journal of Accounting and Management Information Systems
Kusumanto, Raden
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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SYNERGY OF GREEN ENERGY AND SMART TECHNOLOGY: APPLICATION OF RECURRENT NEURAL NETWORKS IN SOLAR-POWERED AGRICULTURE Maulidina, Elfira; Dewi, Tresna; Kusumanto, Raden
International Journal of Mechanics, Energy Engineering and Applied Science (IJMEAS) Vol. 3 No. 2 (2025): IJMEAS - May
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijmeas.v3i2.406

Abstract

In an effort to improve energy efficiency and sustainability in the agricultural sector, smart technology has been integrated into the greenhouse system. The research utilizes the Recurrent Neural Network (RNN) algorithm to forecast values of irradiance on a time principal. The RNN algorithm is chosen for its ability to handle time-series data and predict patterns based on historical data. By using the RNN algorithm, the system can predict real-time needs and then use this information to optimally distribute power from solar power plants. Additionally, this system is equipped with Internet of Things (IoT)-based monitoring capabilities, allowing remote monitoring and control of the research object. Connected IoT sensors collect real-time environmental data and send it to the data server for analysis. The data is also used to update the model of RNN, making supply prediction more accurate over time. The implementation results show increased energy efficiency and reduced operational costs in Green House management. By leveraging AI and IoT technology, model evaluation is conducted using RMSE, MSE, MAE, and R-squared (R²) metrics as important indicators of model accuracy. The evaluation results indicate that this model can provide accurate predictions of irradiance patterns, with low RMSE and MAE values and R² approaching one, signifying excellent implementation in capturing data dynamics.
IoT monitoring for PV system optimization in hospital environment application Andi, Kemas; Kusumanto, Raden; Yusi, Syahirman
International Journal of Accounting and Management Information Systems Vol. 2 No. 2 (2024): August
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/ijamis.v2i2.3310

Abstract

Purpose: This study aims to implement an Internet of Things (IoT)-based monitoring system to optimize photovoltaic (PV) system performance and ensure uninterrupted power supply for critical medical equipment, specifically ventilators and monitors, in the ICU room of RSI Siti Khadijah Palembang. Methodology/approach: The system uses on-grid solar panels connected to an Arduino-based IoT monitoring platform. The IoT system receives inputs from current and voltage sensors, enabling real-time supervision of solar panel output, battery status, and inverter performance. Automatic Transfer Switch (ATS) technology ensures seamless switching between PV and utility sources during periods of insufficient solar energy. Results/findings: The installed system demonstrates stable daily performance, with peak energy production reaching up to 400 Wp under optimal conditions. Real-time data from the IoT dashboard enables informed decision-making to maintain power supply continuity in the ICU. Voltage levels remained within a safe range for ICU operations throughout the test period. Conclusion: IoT integration enhances the reliability of hospital-based PV systems. The system proved effective in maintaining continuous energy delivery to life-saving equipment, reducing reliance on conventional UPS systems. Limitations: The current system lacks MPPT (Maximum Power Point Tracking) and thermal regulation, which could further optimize energy conversion efficiency under varying weather conditions. Contribution: This study provides a replicable model of IoT-enhanced PV deployment in hospital settings, offering valuable insights for renewable energy applications in critical infrastructure across tropical developing countries.
Co-Authors Andi, Kemas Maulidina, Elfira Tresna Dewi Yusi, Syahirman