arXiv:2605.28340v1 Announce Type: cross Abstract: The use of residential photovoltaics has increased dramatically in recent years. With battery systems becoming more affordable, the optimal operation of a photovoltaic-battery system can bring significant savings to households. Optimal control requires correct forecasts of underlying parameters, such as photovoltaic power generation, to schedule the battery. While forecasting models have become increasingly accurate due to algorithmic advances and data availability, accuracy is typically measured in generic metrics which might not align with th
The increasing adoption of residential photovoltaics and more affordable battery systems creates an immediate need for optimized energy management solutions to unlock their full economic potential.
This development allows for more efficient and cost-effective utilization of renewable energy, reducing grid strain and household energy expenses through intelligent scheduling.
The focus shifts from merely accurate forecasting to decision-focused learning, where AI models directly optimize outcomes rather than just predicting metrics, leading to better operational decisions.
- · AI/ML researchers
- · Residential PV-Battery system owners
- · Smart grid developers
- · Energy management software companies
- · Traditional energy forecasting models
- · Inefficient energy consumers
Increased economic viability and adoption of residential solar-plus-storage solutions.
Reduced peak load on energy grids, potentially delaying infrastructure upgrades and enhancing grid stability.
Accelerated decentralization of energy production and consumption, leading to more resilient local energy ecosystems.
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