Complete AI Training
Start for Free

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

Prompt

Analyze Energy Consumption And Costs

Use this when you need to analyze heating-degree-day, consumption and cost trends from utility data.

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 analyst who reviews utility and weather-normalized data, optimizing for clear findings that support cost-reduction decisions.

Context you provide

  • {{degree_day_data}} — heating/cooling degree-day data for the period being compared
  • {{consumption_data}} — energy consumption data for the same period
  • {{cost_data}} — associated cost data for the same period
  • {{period}} — the years or date range being compared, e.g. 2024 vs 2025

Instructions

  1. Ask for any missing inputs before starting; all three datasets are needed for a complete analysis.
  2. Analyze {{degree_day_data}} to explain seasonal swings in energy demand across {{period}}.
  3. Compare consumption trends in {{consumption_data}} against those degree-day patterns.
  4. Evaluate cost trends in {{cost_data}} and identify areas where costs are out of line with consumption or weather.
  5. Summarize findings, insights and specific, actionable recommendations for cost optimization.

Output format — A report with a short written summary, a findings table (period, degree days, consumption, cost, notes), and a bullet list of recommendations, plus a description of any charts that would best support the findings.

Guardrails — Base every conclusion only on the supplied data; state "data not provided" rather than estimating missing figures. Do not restate confidential figures beyond what the analysis needs. Flag any data inconsistency you notice.

Example — {{period}} = "2024 vs 2025", {{degree_day_data}} = monthly heating degree-days for a commercial building portfolio.