Machine Learning-Based Energy Management in Solar-Powered Smart Grids: Integrating Demand Forecasting and Battery Storage Optimization

Abstract

Solar-powered smart grids must coordinate variable photovoltaic generation, changing electricity demand, and constrained storage resources. Machine learning can improve the representation of demand and generation patterns, while optimization can translate forecasts into battery charging and discharging decisions. However, predictive accuracy alone does not establish reliable or economical energy management. This narrative review integrates established literature on probabilistic load forecasting, solar prediction, machine learning, microgrid control, battery degradation, and prescriptive analytics. It proposes a framework linking data provenance, forecast development, uncertainty assessment, physically feasible scheduling, and operational monitoring. Particular attention is given to chronological validation, correlated load and solar errors, state-of-charge limits, conversion losses, battery aging, and the distinction between scheduling and electrical protection. An illustrative graph compares grid imports before and after battery dispatch in a synthetic daily optimization example with explicitly stated assumptions. The calculation uses known profiles and therefore does not demonstrate realized forecasting benefits or field savings. A comparative table summarizes complementary analytical approaches and their main limitations. The analysis argues that probabilistic forecasts, appropriate benchmarks, and realistic closed-loop evaluation are necessary to establish operational value. Storage schedules should reflect reserve requirements and battery condition rather than prioritize short-term electricity costs alone. Future empirical studies should compare complete forecasting and control systems under seasonal variation, communication failures, and changing demand. The proposed framework supports reproducible research and local evaluation of reliable, efficient, and accountable energy management.