AI - Driven Home Energy Management System for Profit and Grid Stability - Deep Reinforcement Learning and Predictive Models for Minimizing Peak Demand While Balancing Battery Degradation in a Dynamic Environment

Abstract

Abstract This thesis presents the development and implementation of an AI-driven home energy management system designed to optimize residential battery storage in response to Sweden’s new power-based electricity tariffs, which introduce capacity fees based on avarage monthly power peaks starting January 2027. The system integrates three components: (1) multi-modal forecasting models for electricity prices, solar production, and household demand. (2) a Recurrent Proximal Policy Optimization (RPPO) reinforcement learning agent for real-time battery control and (3) automated orchestration via Prefect with Home Assistant integration. The forecasting stack (XGBoost and temporal convolutional networks (TCN)) achieves competitive accuracy, and the RL agent, trained on a custom reward balancing cost, solar utilization, and safety, learns price arbitrage and solar aware charging strategies. Field deployment on a 22 kWh battery with a 20 kW dual-orientation PV array demonstrates integration with real hardware and shows preliminary economic benefits under simulated seasonal conditions. The agent maintains 100% safety compliance (zero charge/discharge violations during final deployment) while achieving high grid independence. Although additional computational time for full training convergence and hyperparameter tuning remains as future work, these preliminary results underscore the strong potential of AI-driven residential energy management for cost savings and grid support.