Hybrid forecasting and optimization framework for residential photovoltaic-battery systems: Integrating data-driven prediction with multi-strategy scenario analysis

With the advancement of energy transition, residential photovoltaic (PV) systems face intermittency challenges that impact grid stability. While battery integration enhances resilience, existing approaches exhibit critical gaps: (1) underdeveloped hybrid modeling frameworks balancing physical interpretability and data-driven accuracy; (2) reinforcement learning (RL) strategies prioritizing economic gains over grid stability, risking localized fluctuations; and (3) performance evaluations lacking systematic assessment across varying PV-battery capacities. To bridge these gaps, this study proposes a hybrid framework combining physical energy flow constraints with XGBoost-based machine learning for robust forecasting. Two optimization strategies, proximal policy optimization (PPO) and rule-based control (RBC), are developed for charge-discharge scheduling, explicitly incorporating grid stability metrics. Multi-scenario analysis evaluates performance under varying capacities and initial states of charge (SOC). Results demonstrate the hybrid model’s superiority over physics-based benchmarks, significantly improving prediction accuracy, with R² increasing from 0.70 to 0.95 for SOC and from 0.83 to 0.98 for grid power. Both PPO and RBC enhance efficiency and stability versus baseline: the energy self-sufficiency rate rises from 10.6% to 79.3% (PPO) and 82.4% (RBC), while grid power fluctuations decrease from 2.6 kWh to 1.66 kWh (PPO) and 1.38 kWh (RBC). Crucially, RBC achieves higher stability and interpretability near boundaries, whereas PPO excels in long-term optimization but exhibits boundary-condition sensitivity. Results further reveal that PV-battery capacity and initial SOC influence strategy performance. This study establishes a structured technical pathway encompassing hybrid forecasting model development, stability-oriented optimization design, and scenario-based performance evaluation, providing an integrated solution to enhance grid resilience and energy autonomy in residential PV-battery systems.