Preventive Voltage Drop System in Smart Farming Using Gradient Boosting
Abstract
Solar photovoltaic systems in smart farming frequently suffer from destructive voltage drops caused by weather fluctuations, endangering critical actuators like irrigation pumps and ventilation fans. To address this, this study presents a preventive control system integrating a Gradient Boosting Machine algorithm with real-time load management. The model utilizes weather, technical, and temporal features to forecast solar power generation. Training was conducted on 2,184 hours of historical data from April to June, while evaluation used a 744-hour test dataset from July collected at an 8,000-watt photovoltaic facility in South Sulawesi. Forecasted power output is fed directly into the load controller to prioritize actuator execution and enforce a maximum voltage drop limit below five percent. Experimental evaluation demonstrates superior predictive accuracy, achieving a Root Mean Square Error of 0.211 kilowatts and a Mean Absolute Error of 0.158 kilowatts. The findings confirm that proactive artificial intelligence load scheduling effectively prevents voltage degradation, offering a scalable, software-driven energy management solution for sustainable smart agriculture.
References
Alan, M. B. F., Puspitaningayu, P., Nashrullah, M. A., Suciningtyas, L., & Rahman, M. M. (2025). LSTM-based Prediction of Photovoltaic Voltage with Lightweight Load Scenarios. Journal of Intelligent Systems and Telecommunication, 1(2), 200–210. https://doi.org/10.26740/jistel.v1n2.p200-210
Ananwattanaporn, S., Thongsuk, S., Lertwanitrot, P., Yoomak, S., & Ngamroo, I. (2024). Characteristics of various single wind-power distributed generation placements for voltage drop improvement in a 22 kV distribution system. Sustainability, 16(10), 4295. https://doi.org/10.3390/su16104295
Bai, R., Li, J., Liu, J., Shi, Y., He, S., & Wei, W. (2025). Day-ahead photovoltaic power generation forecasting with the HWGC-WPD-LSTM hybrid model assisted by wavelet packet decomposition and improved similar day method. Engineering Science and Technology, an International Journal, 61, 101889. https://doi.org/10.1016/j.jestch.2024.101889
Danach, K., Aly, W. H. F., & Kanj, H. (2025). Machine learning for smart grid stability: Enhancing reliability in renewable energy integration. European Journal of Pure and Applied Mathematics, 18(2), 5953. https://doi.org/10.29020/nybg.ejpam.v18i2.5953
Di Leo, P., Ciocia, A., Malgaroli, G., & Spertino, F. (2025). Advancements and challenges in photovoltaic power forecasting: A comprehensive review. Energies, 18(8), 2108. https://doi.org/10.3390/en18082108
Fathollahi, A. (2025). Machine learning and artificial intelligence techniques in smart grids stability analysis: A review. Energies, 18(13), 3431. https://doi.org/10.3390/en18133431
Huang, B., Song, K., Zhang, T., Lian, Z., Li, H., Jin, D., & Wang, Y. (2025). Solving power supply stability issues in remote agricultural areas based on an improved sliding-mode active disturbance rejection control method. Agriculture, 15(7), 674. https://doi.org/10.3390/agriculture15070674
Kholila, N., Habsari, K. M., Anindya, C., & Charisma, K. (2024). Optimasi solar tracking system pada PLTS di lahan pertanian menggunakan polynomial regression. Jurnal ELTEK, 22(2), 66–74. https://doi.org/10.33795/eltek.v22i2.512
Kumari, K., Mirzakhani Nafchi, A., Mirzaee, S., & Abdalla, A. (2025). AI-driven future farming: Achieving climate-smart and sustainable agriculture. AgriEngineering, 7(3), 89. https://doi.org/10.3390/agriengineering7030089
Mbendane, B., Abe, B., & Richards, C. (2024). Analysis of supercapacitors in renewable energy systems for managing power fluctuations. International Journal of Energy Research, 2024(1), 8821940. https://doi.org/10.1155/2024/8821940
Olcay, K., Giray Tunca, S., & Arif Ozgur, M. (2024). Forecasting and performance analysis of energy production in solar power plants using Long Short-Term Memory (LSTM) and Random Forest models. IEEE Access, 12, 103299–103312. https://doi.org/10.1109/ACCESS.2024.3431200
Riswandi. (2019). Sistem kontrol vertical garden menggunakan NodeMCU ESP8266 berbasis Android (Unpublished undergraduate thesis). Universitas Hasanuddin, Makassar, Indonesia.
Riswandi, R., Jamiah, R., Mardhatillah, N., & Hamid, H. P. (2021). Klasifikasi penyakit pada citra daun jeruk menggunakan arsitektur MobileNet berbasis mobile platform. Jurnal Fokus Elektroda, 6(4), 212–217. https://doi.org/10.33772/fokus.v6i4.182
Riswandi, R., Niswar, M., Tahir, Z., Zainal, Z., & Wey, C. Y. (2022). Design and implementation of IoT-based aeroponic farming system. In 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) (pp. 308–311). IEEE. https://doi.org/10.1109/CyberneticsCom55287.2022.9865320
Santos, A. A., Pereira, F., da Silva, A. F., Caetano, N., Felgueiras, C., & Machado, J. (2023). Electrification of a remote rural farm with solar energy—Contribution to the development of smart farming. Energies, 16(23), 7706. https://doi.org/10.3390/en16237706
Zikri, F. (2024). Smart urban farming 5.0: Integration of vertical farming automation and solar panels for maximum energy efficiency and productivity in major cities in Indonesia. Agricultural Power Journal, 1(3), 52–62. http://doi.org/10.70076/apj.v1i3.82
