Designing a solar system accurately is critical—too small and customer expectations won't be met, too large and payback period suffers. This workflow walks through every step from initial site assessment through final system design, using AI tools to optimize sizing, component selection, and financial projections.
Collect property details: address, roof size/orientation/material, shading obstacles, utility rate structure, customer goals (energy offset %, specific load reduction, grid export). Request utility bills from past 12 months for load analysis.
Examine 12 months of utility bills to understand usage patterns, seasonal variation, demand charges, and rate structure. Identify peak usage times and patterns. Understanding load shapes is critical for proper sizing.
Use satellite imagery analysis to evaluate roof solar potential. Aurora Solar or Google Project Sunroof automatically analyze satellite data to measure available roof area, orientation, and shade impacts throughout the year.
Create 3D shade model accounting for trees, buildings, and terrain surrounding the property. AI tools map shade patterns throughout the year to identify optimal array placement and predict seasonal production variations.
Run solar irradiance simulation using site data (latitude, altitude, orientation, shading, weather patterns). AI models incorporate typical meteorological year (TMY) data to forecast expected annual and monthly production.
Based on load analysis and production forecast, determine ideal system size. Consider customer's goals: 100% energy offset (increasingly unrealistic), specific percentage offset (70-80% typical), or specific monthly production target.
Choose inverter, modules, mounting, and balance-of-system components based on system size, roof type, and customer priorities (price, efficiency, warranty). AI tools recommend optimal component combinations.
Model complete financial picture: installed cost, available incentives (federal ITC, state rebates, utility incentives), financing options (cash, loan, lease, PPA), and payback period. AI tools automate most calculations.
Ensure design meets NEC code requirements, local jurisdictional codes, and utility interconnection rules. AI-assisted checklists help ensure nothing is missed.
Create proposal package including system design, production forecast, financial analysis, warranty details, and contract. AI-powered tools generate professional documents automatically.
Create engineering documents for permitting and installation: single-line diagrams, electrical calculations, structural analysis, equipment datasheets, racking details, and interconnection diagrams.
Present proposal and design to customer. Obtain signed contract including design specifics, warranties, performance guarantees, financing terms, and timeline.
Submit design documents to local jurisdiction for building permit and to utility for interconnection approval. Follow up on requests for additional information.
Once permits and interconnection approval are obtained, finalize all installation details with crew: roof access, electrical details, timeline, customer communication plan.
✗ Underestimating shade impact—most production shortfalls result from shade underestimation. Take time with this step.
✗ Ignoring interconnection requirements early—some utilities have special hardware, export limits, or documentation requirements. Verify before design is final.
✗ Oversizing systems to maximize incentives—this leads to excess generation, poor payback, and customer disappointment.
✗ Using generic weather data instead of site-specific data—templates and tools often use average data; use actual TMY data for your location.
✗ Insufficient code compliance review—missed requirements lead to permit rejections and installation delays.
Properly designed systems typically exceed production forecasts by 2-5%, achieve customer energy offset goals, and result in payback periods within projected timelines. Most customer satisfaction issues trace back to design mistakes rather than installation quality. Detailed, accurate design upfront saves significant time and money downstream.
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