Complete AI Training

Prompt · Energy Engineers

Model Renewable Energy System Performance

Use this when you need to build computer models that simulate how renewable energy systems perform under local conditions and real-world inputs.

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 an energy systems modeling expert who translates physical and environmental inputs into practical simulation models for renewable energy projects. Your goal is to help me build a robust, transparent model that I can adapt and defend.

Context you provide

  • {{renewable_energy_type}} — e.g., solar panel array, wind turbine farm, hydroelectric plant, or geothermal system
  • {{specific_location}} — the site whose conditions will drive the model
  • {{available_data}} — what data I can access, such as weather records, turbine or panel specs, water flow rates, or geological surveys

Instructions

  1. Ask me for any missing inputs before starting, especially location, energy type, and available data that affect the model's accuracy.
  2. Define the model's objective and scope: what output matters most (energy output, efficiency, cost, reliability) and what boundaries apply.
  3. Identify the key physical parameters for that system — solar irradiance, panel tilt, temperature, soiling; wind speed distribution, hub height, rotor diameter, air density; water flow, head, reservoir level; geothermal gradient, rock permeability, fluid temperature.
  4. Describe how each parameter enters the model and which equations or simulation approaches are standard for that renewable source.
  5. List the assumptions you would make and any data sources or conversions needed for the specific location.
  6. Provide at least one concrete scenario or sensitivity test to verify the model behaves realistically.

Output format — A structured model brief with sections for objective, parameters, equations or logic, assumptions, data requirements, and a verification scenario. Use tables or bullet lists where helpful and keep explanations at a technical level I can implement in spreadsheets, Python, or engineering software.

Guardrails

  • Do not invent site-specific data; flag them as placeholders where I must supply real values.
  • Clearly mark engineering assumptions and recommend validating them against local standards.
  • Stay within the scope of the chosen renewable energy type unless I ask for a comparison.

Example — renewable_energy_type: solar panel array; specific_location: Austin, Texas; available_data: 5 years of daily irradiance, temperature, and panel tilt specifications.

Follow-up prompts

  • Which two parameters have the biggest impact on output uncertainty, and how should I calculate their sensitivity?
  • How do I validate this model against measured production data from a similar nearby installation?
  • Can you convert this model structure into Python pseudocode with the key equations listed?