Battery-to-Revenue Intelligence is a new capability designed to extend the platform’s optimization workflows to battery energy storage assets. The announcement follows the Company’s recently completed validation of DeepSolar Predict using real-world European market data, as PRF continues to advance the solution toward planned commercial launch.

Battery-to-Revenue Intelligence is designed to help renewable energy operators move beyond battery monitoring by connecting storage availability, operational constraints and market conditions into decision-support workflows for revenue optimization. The capability is intended to support operators as they evaluate when to store, dispatch or preserve battery capacity based on market opportunities, production forecasts and changing grid conditions.

As renewable penetration increases, operators are no longer managing only production — they are managing financial exposure, imbalance risk, curtailment risk and storage flexibility in increasingly dynamic power markets. PRF believes battery energy storage systems will play a growing role in this transition, particularly as asset owners seek to improve market participation and maximize the value of renewable generation and storage assets. According to the International Energy Agency (IEA), global battery storage capacity additions reached approximately 108 GW in 2025, up roughly 40% year-over-year, and the International Renewable Energy Agency projects installed battery storage capacity to grow nearly ninefold from 2023 levels to 782 GW by 2030.

DeepSolar Predict is being developed as an end-to-end decision-support solution that connects the full renewable energy value chain — from weather intelligence and production forecasting to storage optimization, market participation and revenue-focused recommendations. By bringing these capabilities together in a single platform, PRF aims to help operators move from fragmented operational tools toward a unified environment for commercial decision-making.

The new capability is designed to evaluate battery operating conditions, including state of charge, availability, charging and discharging behavior, operational readiness and other constraints, and to incorporate these insights into the platform’s revenue optimization workflows.

"Battery storage is becoming a critical component of renewable energy revenue optimization," said Efi Cohen-Arazi, Chief Executive Officer of PRF Technologies. "We believe the next step is not simply monitoring batteries, but helping operators understand how battery flexibility can support higher-value market decisions. DeepSolar Predict is being developed as an end-to-end solution that connects forecasting, asset intelligence, storage optimization and market decision support with the goal of helping customers move from reactive operations to proactive, data-driven revenue optimization."

As part of its product roadmap, PRF also plans to deliver a "What-if Battery Scenario" capability designed to help asset owners evaluate the potential revenue impact of adding or operating battery storage within a renewable energy portfolio. Based on ongoing discussions with prospective customers, the Company believes scenario-based storage analysis is increasingly relevant for renewable energy operators evaluating how batteries may contribute to improved market participation, reduced exposure and stronger revenue performance.

PRF believes Battery-to-Revenue Intelligence represents another step in the evolution of DeepSolar Predict from asset analytics and forecasting into AI-driven revenue optimization. The Company also expects these capabilities to serve as a core component of GridFeed™, PRF’s planned commercial platform for renewable energy trading and market participation, built on the DeepSolar Predict AI engine, which the Company intends to introduce more fully in the coming weeks.