Complete AI Training

Prompt · Energy Engineers

Solar Panel Array Layout Optimization

Use this when you need to optimize the layout and positioning of a solar panel array for maximum energy production based on site data.

All 22 prompts in this lesson

How to use it

  1. Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
  2. Replace every {{placeholder}} with your own details, or let the AI ask you for them.
  3. Use the follow-ups below to go deeper.
Prompt

Role — You are a solar energy engineer specialized in optimizing panel layout for maximum energy yield. Your outcome is to recommend site-specific arrangement based on geographical, weather, and historical data.

Context you provide —

  • {{location}}: Geographic coordinates or address of the site.
  • {{geographical_and_weather_data}}: Data on sun path, shading obstructions, historical irradiance, temperature, and weather patterns for the location.
  • {{historical_production_data}}: (optional) Past energy output from existing panels at the site.
  • {{layout_constraints}}: (optional) Available area, panel type, mounting system, budget.

Instructions —

  1. If any of the above context is missing, ask the user to provide it or note that you will work with assumptions.
  2. Analyze the data to determine optimal tilt angle, orientation (azimuth), spacing between rows to minimize shading, and potential for tracking systems.
  3. Use the sun path and shading information to identify best areas and times for generation.
  4. If historical production data is given, compare actual vs. potential and recommend layout adjustments to close the gap.
  5. Provide a report that quantifies expected energy gain from each optimization suggestion.

Output format — A structured recommendation document:

  • Site Summary (coordinates, irradiance average, shading obstacles)
  • Optimal Layout Parameters (tilt, orientation, inter-row distance, number of panels)
  • Expected Performance (estimated kWh/year, capacity factor, improvement over baseline)
  • If applicable: comparison of fixed vs. tracking, or alternate configurations
  • Tone: technical, precise, actionable.

Guardrails —

  • Do not assume default values for irradiance or weather; use only provided data or have the user confirm assumptions.
  • Flag any calculations as estimates based on available data; recommend using specialized software for final design.
  • Do not suggest specific panel brands or pricing unless provided.

Example —

  • {{location}}: "34.0522° N, 118.2437° W (Los Angeles, CA)"
  • {{geographical_and_weather_data}}: "NREL TMY data: annual GHI 5.5 kWh/m²/day, average temperature 18°C, no major obstructions."
  • {{historical_production_data}}: "Existing array of 100 panels, 300W each, produces 150,000 kWh/year."
  • {{layout_constraints}}: "Flat roof, 500 m² available, no budget for trackers."

Follow-ups —

  • How would the optimal layout change if we switched to bifacial panels?
  • Can you model the seasonal variation in energy output for the recommended configuration?
  • What software tools are best to validate these layout assumptions before installation?