Self-Optimizing Power Converters using Hybrid DRL and LSTM-Driven MPPT For IoT Energy Harvesting
The IoT devices that are energy harvested must have efficient and adaptive power management to ensure that they can work effectively even when the environmental conditions are dynamic and unpredictable. Traditional MPPT methods and fixed-parameter power converters usually exhibit slow convergence, inefficient operation and low flexibility in the presence of varying energy sources. In an attempt to address such constraints, the proposed study will introduce a hybrid Deep Reinforcement Learning (DRL) + predictive LSTM-based MPPT framework to predictive engage in intelligent energy harvesting. The LSTM network captures the time variation of the gathered energy and forecasts the future power with the time, thus it can regulate the MPPT proactively and correctly in the circumstances of non-stationary conditions. At the same time, the DRA succeeds in adapting converter parameters like duty cycle and switching behavior with optimal control policies learned by continuous interaction with the environment. The hybrid architecture is a balance between short-term responsiveness and long-term optimization that will guarantee stable power output and low oscillations around the maximum power point. Massive simulations prove that the selected approach is much more efficient in converting power, convergence rate, and system stability than the conventional and standalone AI-based MPPT strategies. The framework attains statistically 98.60% accuracy, 98.50% precision, 98.70% recall and F1-score of 98.60 with 120 seconds computation time, which ensures reliability and real-time viability. Comparative analysis indicates that the method is superior to base methods in the tracking accuracy, adaptability and efficiency during computations. The structure also automatically responds to changes in load requirements and environmental changes and uncertainties without tuning. The paper validates the suggested solution as a strong, scalable, and energy-saving solution to the next-generation self-powered IoT systems. They can be used in smart sensing, wearable electronics, and remote monitoring to aid sustainable and intelligent operation in dynamically modulated energy harvesting cases.