Idea
A solar radiation forecasting platform with integrated battery scheduling that helps industrial and commercial users optimize renewable energy investments.
Research Paper
Core Innovation
This paper introduces SunCastNet, a data-driven model delivering high-resolution, 10-minute interval solar radiation forecasts up to 7 days ahead. It uniquely integrates these forecasts with reinforcement learning-based battery scheduling to reduce operational regret significantly compared to traditional robust decision making. This combination enables more economically viable solar-battery investments in high-emitting industrial sectors.
Market Size (TAM)
$10–20B TAM, $2–10B SAM; assumption: global industrial and commercial energy sectors adopting solar and battery storage driven by decarbonization goals.
Potential Customers & Pain Points
- Industrial and Commercial Energy Users Facing Solar Investment Decisions
- Renewable Energy Project Developers Needing Accurate Forecasts
- Energy Storage Operators Seeking Optimal Battery Scheduling
Business Model
Subscription-based SaaS platform offering solar forecasting APIs and battery scheduling tools with tiered pricing for industrial and commercial clients.
Competitive Landscape
- Tomorrow.io
- SolarEdge
- IBM Weather Company
Implementation Challenges
- Data integration complexity across regions
- Adoption resistance from traditional energy operators
- Accuracy and reliability under diverse weather conditions
Validation Strategy
- Pilot deployments with select industrial partners to measure economic impact
- Backtesting investment returns using historical data
- Iterative model refinement based on real-world forecast performance
Research Paper Overview
Data-driven solar forecasting enables near-optimal economic decisions
Summary
SunCastNet is a lightweight data-driven forecasting system providing high-resolution solar radiation predictions up to 7 days ahead. Coupled with reinforcement learning for battery scheduling, it significantly reduces operational regret and improves economic viability of solar-battery investments across multiple industrial sectors. This approach demonstrates that accurate, long-horizon solar forecasts can drive measurable economic gains and accelerate renewable energy deployment.