Optimizing distributed solar energy economics: A machine learning classification approach to storage system management

The integration of solar energy systems with battery storage presents complex economic optimization challenges in distributed energy networks, where traditional approaches fail to address the interdependencies between generation variability, storage decisions, and real-time cost minimization. Entransy dissipation theory reveals that optimal energy management requires simultaneous consideration of energy quantity and thermodynamic quality preservation, necessitating sophisticated optimization frameworks that balance technical performance with economic viability. This research develops a novel K-Nearest Neighbors (KNN) classification framework integrated with comprehensive economic evaluation methods to simultaneously optimize photovoltaic generation forecasting, battery storage management, and operational cost reduction. The methodology employs multi-dimensional feature classification using 18 months of operational data from 12 distributed solar installations across China, incorporating economic-weighted distance metrics and real-time cost-benefit analysis. Results demonstrate that KNN-based optimization achieves a 38.9% reduction in operational cost, a 23.4% improvement in forecasting accuracy, and a 89.4% increase in storage efficiency compared to conventional methods. Economic analysis reveals computational energy consumption represents only 3.2% of operational savings, generating net energy value ratios of 582% with cost-benefit ratios of 4.73. Statistical validation confirms significance (p < 0.001) across all metrics with payback periods of 16.2 months. The framework offers actionable policy recommendations for emerging markets, establishing economic thresholds (318.85 RMB/kWh) that facilitate informed investment decisions and promote accelerated renewable energy adoption through enhanced economic viability.