Articles

IoT-Based 100 WP Solar Power Plant for Ultraviolet Lighting in Dragon Fruit Cultivation: Design and Performance Comparison of Tracking and Non-Tracking Systems

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The use of solar energy for agricultural applications can reduce dependence on conventional electricity while enabling operation in locations where grid access is limited. This study aimed to design and evaluate a 100 Wp photovoltaic system integrated with an Internet of Things (IoT) monitoring and control system for lighting in a dragon fruit plantation. The system employed a 100 Wp monocrystalline solar panel, a solar charge controller, a battery storage unit, an inverter, an ESP32 microcontroller, voltage and current sensing, and Telegram as the remote monitoring interface. Electrical parameters were observed hourly from 09:00 to 17:00 WIB under two panel configurations: solar tracking and fixed non-tracking. Panel voltage, panel current, battery voltage, and calculated panel power were analyzed. For the tracking configuration, the average panel voltage and current were 19.64 V and 4.12 A, respectively, corresponding to an average calculated power of 81.47 W. The maximum calculated power was 100.94 W at 12:00. Under the non-tracking configuration, the average voltage and current were 18.69 V and 3.61 A, with an average calculated power of 68.08 W and a maximum of 91.65 W at 12:00. Thus, the tracking configuration produced approximately 19.7% higher average calculated panel power than the non-tracking configuration. The ESP32–Telegram system successfully transmitted operating information in real time when a stable Internet connection was available. The results demonstrate that combining solar tracking with IoT monitoring can improve the observed electrical performance and facilitate remote management of a small off-grid photovoltaic lighting system.

Exemplary Model of AI-Supported Adaptive Optimization Energy Flow Control in Smart City Microgrids: A Simulation-Based Scenarios

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The paper focuses on the possibilities for developing a model for adaptive control of electricity flows in urban microgrids using AI support into the Internet of Things networks.  The goal is the requirement for smarter, more adaptive and sustainable methods in controlling local energy systems. This is critical for distributed generation and the growing incorporation of renewable energy resources. The study is conceptual in nature and aims to develop an integrated model that combines physical energy infrastructure, IoT-based data acquisition, the analytical capabilities of artificial intelligence, and a logic for adaptive real-time decision-making. It is analyzed the theoretical foundations of adaptive management in microgrids, the design of model development of multilayered architecture, and the interaction between physical and information flows. Particular attention is given to the role of intelligent monitoring devices, forecasting and optimization algorithms, as well as the coordination between local generation, storage, consumption, and exchange with the main grid. The proposed model is analyzed through comparison with traditional, optimization-based, and AI-driven models discussed in the scientific literature, and it is argued that the integration of AI and IoT enables higher adaptability, improved load balancing, more efficient use of local energy resources, and better integration of renewable energy sources in the urban energy environment. The proposed model provides a conceptual framework for the intelligent management of electricity flows in urban microgrids, emphasizing its potential for further development and application in sustainable energy systems.