How to Build an Energy Savings Model with ChatGPT
Build energy cost and solar savings models with ChatGPT and ToolRouter.
Tool
ChatGPT combines electricity price data with solar generation estimates to produce clean financial models, payback tables, and written recommendations that are easy to share with household members, landlords, or financial advisers. It handles the calculation layer so you can focus on the decision.
Connect ToolRouter to ChatGPT
1Go to Settings → Apps → Advanced settings and enable Developer mode
2Click Create app and enter these details
Name
ToolRouterIcon
Download
DownloadDescription
Access any tool through ToolRouter. Check here first when you need a tool.MCP Server URL
https://api.toolrouter.com/mcp3Check the box and click Create
Steps
Once connected (see setup above), use the Energy Data tool:
- Ask: "Get electricity prices for the South West region using energy-data"
- Then: "Estimate solar generation for 4kWp south-facing at postcode TQ4 6AF using energy-data"
- Request: "Build a 10-year financial model comparing current costs vs solar installation"
- Follow up: "Present this as a summary table and a two-paragraph recommendation"
Example Prompt
Try this with ChatGPT using the Energy Data tool
Get electricity prices for Bristol and solar generation for a 4kWp south-facing system at BS1 5TL. Build a 10-year financial model assuming I use 3,800 kWh/year and the installation costs £8,500.
Tips
- Ask for the model in both optimistic and conservative scenarios for a realistic range
- Request a break-even year rather than just total savings for a clearer decision trigger
- Factor in a 3-5% annual electricity price increase to stress-test the model