An AI-Enabled IoT Framework with Adaptive Predictive Energy Optimization for Intelligent DC Solar Microgrid Management
Abstract
The rise of renewable energy systems has driven the need for more intelligent techniques to enhance efficiency, reliability, and sustainability in a DC solar microgrid. This paper presents a framework of adaptive predictive energy optimization (APEO) algorithm-driven intelligent energy management and predictive fault diagnosis of DC solar microgrid using Internet of Things (IoT) technology. The proposed framework brings together the real-time data sensing and processing using IoT, edge computing, weather forecasting and solar power estimation, adaptive battery management, dynamic load priority scheduling, self-learning fault diagnosis, and cloud monitoring in one architecture. The APEO algorithm continuously estimates available PV energy and then optimizes the energy charging and discharging of the battery, identifies the critical loads connected to the DC system, and adjusts the optimization parameters adaptively based on the real-time measured feedback of the working status to utilize as much renewable energy as possible and to obtain as little energy loss as possible. The proposed framework was tested for different solar irradiance, environmental conditions and dynamic load profiles and compared with Conventional MPPT, Rule Based and Fuzzy Logic energy management techniques. The results of the experiments show that the proposed framework achieves 98.1% energy utilization, 98.4% photovoltaic conversion efficiency, 96.8% battery utilization, 99.2% fault detection accuracy, 98.9% prediction accuracy and 99.1% overall system efficiency, with communication latency of 18ms and energy loss of 2.4%. The battery life is also extended by 7.3 years thanks to the adaptive learning, which optimises battery charging and load scheduling. The results obtained validate the proposed APEO framework for next-generation solar DC microgrids with IoT-enabled intelligent renewable energy management systems, which is efficient, scalable, and reliable.