AI-DRIVEN SOLAR POWER OPTIMIZATION AND PRIORITY-BASED LOAD CONTROL SYSTEM WITH VOICE ASSISTANCE

Abstract

The escalating global demand for clean energy has underscored the necessity for intelligent management frameworks that can simultaneously optimize photovoltaic generation, battery storage, and load consumption in dynamic environments. Conventional solar photovoltaic systems operate predominantly in passive modes; they harvest, store, and distribute energy without predictive foresight or autonomous adaptation to supply variability. Consequently, these systems frequently exhibit inefficient power utilization, unplanned battery deep-discharge events, and limited user interactivity. This paper proposes an integrated Smart Solar Energy Management System that synergistically combines Internet of Things (IoT) sensing, hybrid deep learning-based forecasting, priority-driven load scheduling, and natural language voice assistance. An ESP32 microcontroller serves as the embedded edge node, orchestrating real-time acquisition of voltage, current, and environmental data through calibrated analog and digital sensors. The acquired telemetry is transmitted to a cloud-native dashboard for remote visualization and historical logging. At the analytics core, a hybrid neural architecture fusing Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) layers is trained on multivariate time-series data to forecast short-term solar generation trajectories. The predictive intelligence is subsequently fused with a three-tier priority load-control algorithm that dynamically connects or disconnects appliances based on battery state-of-charge thresholds and anticipated energy availability. Furthermore, a voice-assistance module is integrated to enable hands-free inquiry and actuation, thereby improving accessibility and user experience. Extensive field evaluation over a continuous 30-day deployment demonstrates that the proposed hybrid predictor achieves a root-mean-square error of 0.11 kWh and a coefficient of determination of 0.95, outperforming standalone LSTM, GRU, and classical multilayer perceptron baselines. The intelligent load-management strategy reduced deep-discharge incidents by approximately 75% and improved overall energy utilization efficiency by 22%. The voice interface attained 95% recognition accuracy with an average end-to-end latency below 1.5 seconds. These results confirm that the confluence of IoT, hybrid deep learning, and voice-driven automation provides a robust, scalable paradigm for next-generation residential solar energy systems.