Executive Summary

Many rooftop PV and battery systems produce 10–25% less energy than modeled at sale, leaving homeowners, installers, and financiers with lower returns and higher call-backs; this problem is driven by mis-sized systems, optimistic runtime estimates, per-roof shading/pitch errors, and a lack of closed-loop validation. The people who pay for the fix are residential owners (15M installs annually), installers/EPCs that face warranty costs and lost deals, and lenders/aggregators that underwrite expected cashflows. You could build an AI-driven sizing, runtime and cost planner that fuses high-resolution satellite/LiDAR mapping, local weather and irradiance models, and live telemetry from smart inverters and home energy monitors to produce probabilistic energy and runtime forecasts, per-install recommendations (PV + storage sizing), and a sell-sheet/API for installers and financing partners. The product would emphasize automated per-roof shading/pitch modeling, Monte Carlo runtime outputs (percentile energy and outage duration estimates), and continuous model refinement from post-install telemetry. This market is attractive now: roughly $45B of lifetime spend exists across 15M residential installs at ~$3,000 of design, software-enabled services and consulting per install, and trends — increasing rooftop PV + batteries, proliferation of inverter and home telemetry, and affordable precision mapping — materially improve both data availability and the value of better forecasts. Market score and revenue potential estimates (90/100 and 78/100 respectively) reflect a sizable addressable base and a realistic path to recurring revenue. To stand out you must combine superior per-rooftop geometric accuracy, closed-loop learning from real-world telemetry, and workflows that plug directly into installer quoting and lender underwriting; this yields tangible KPIs such as reduced underperformance claims and better financed yields. The challenges are non-trivial — acquiring diverse telemetry and permitting data, managing fragmented installer sales cycles, and proving predictive accuracy at scale — but strengths include software gross margins, recurring SaaS/transaction revenue, and measurable ROI that can justify an enterprise-priced product.

Market Opportunity

Why your solar setup underperforms — AI sizing, runtime & cost planner targets a $45.0B = 15M annually addressable residential installs x $3,000 average lifetime spend on design, software-enabled services, and consulting total addressable market with medium saturation and a year-over-year growth rate of 15-25% = residential solar + battery adoption CAGR in developed markets.

Key trends driving demand: distributed-energy-resources -- higher rooftop PV + batteries increases need for correct sizing and runtime prediction; telemetry-proliferation -- smart inverters and home energy monitors provide data to validate models and close feedback loops; precision-mapping -- affordable high-res satellite/LiDAR enables per-rooftop shading and pitch modeling; DIY and comparison shopping -- consumers expect transparent ROI and runtime estimates before purchase.

Key competitors include Aurora Solar, HelioScope (Folsom Labs), PVsyst, EnergySage, Workarounds & adjacent solutions (PVWatts, spreadsheets, YouTube guides).

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Why your solar setup underperforms — AI sizing, runtime & cost planner

Executive Summary

Market Validation

Market Opportunity

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