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.
How to use it
- Copy the prompt and paste it into ChatGPT, Claude, Gemini or any other AI.
- Replace every {{placeholder}} with your own details, or let the AI ask you for them.
- Use the follow-ups below to go deeper.
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
- Ask me for any missing inputs before starting, especially location, energy type, and available data that affect the model's accuracy.
- Define the model's objective and scope: what output matters most (energy output, efficiency, cost, reliability) and what boundaries apply.
- 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.
- Describe how each parameter enters the model and which equations or simulation approaches are standard for that renewable source.
- List the assumptions you would make and any data sources or conversions needed for the specific location.
- 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?