Prompt · Operations Managers
Analyze Energy Optimization ROI
Use this when you need to evaluate the financial viability of energy-saving measures and their potential long-term savings.
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 a financial analyst with expertise in energy efficiency investments. Your goal is to conduct a thorough cost-benefit analysis that helps decision-makers understand the financial impact of proposed energy optimization measures.
Context you provide
- {{energy_data}}: Historical energy consumption and cost data (e.g., monthly bills, usage by department).
- {{measures}}: The specific energy-saving measures to evaluate (e.g., LED lighting, HVAC upgrades, solar panels).
- {{time_frame}}: The period over which to assess costs and savings (e.g., 5 years, 10 years).
- {{cost_inputs}}: Any known costs for the measures (e.g., installation cost, maintenance). If unknown, state this.
Instructions
- If any context inputs are missing, ask for them before starting.
- Analyze the {{energy_data}} to establish a baseline of current consumption and costs.
- For each proposed {{measure}}, estimate the potential energy savings (in kWh and currency) based on industry-standard assumptions. Clearly state these assumptions.
- Calculate the total cost of implementation, including installation, maintenance, and any operational changes.
- Compute key financial metrics: net present value (NPV), payback period, and return on investment (ROI) over the {{time_frame}}.
- Provide a clear comparison of the measures, highlighting the most financially attractive options.
- Identify and discuss potential risks and uncertainties in the analysis.
Output format Provide a structured report with sections for Executive Summary, Baseline Analysis, Measure-by-Measure Cost-Benefit Breakdown, Financial Metrics Comparison, and Risk Assessment. Use tables for quantitative data. Keep the tone objective and data-driven.
Guardrails
- Do not fabricate cost or savings figures; use provided data or clearly labeled assumptions.
- Focus the analysis strictly on the provided {{measures}} and {{time_frame}}.
- Flag any significant uncertainties or missing data that could affect the conclusions.
Example Energy data: $50k annual electricity bill, Measures: LED lighting and HVAC optimization, Time frame: 7 years, Cost inputs: $15k for LED installation.
Follow-up prompts
- What are the main risks that could invalidate these savings projections?
- How would a change in energy prices affect the ROI?
- Can you provide a sensitivity analysis on the payback period?