Introduction
This guide will help engineering teams consistently estimate energy and cost savings from faults detected by PEAK. By quantifying savings, teams can prioritise issues, justify actions, and better communicate impact to stakeholders.
Why Calculate Savings?
Quantifying fault impact helps to:
Assist with alert prioritisation by expected value or urgency
Support business cases and return-on-investment (ROI) decision making
Enhance customer conversations and reporting
Demonstrate the tangible value of the FDD platform
Types of Savings
Energy Consumption: Reduction in electricity or gas use.
Demand (kW): Lower peak energy demand, which may reduce demand charges.
Maintenance: Avoided service callouts, fewer breakdowns, extended equipment life.
Energy Consumption
Use site asset register data, recorded trend operational data and psychrometrics to calculate excess energy use.
Basic Formulas:
Thermal energy wastage (e.g.valve leakage)
Energy (kWh) = (Flow Rate × ΔT × Specific Heat × Density × Hours) / System COP
Cost Saving = Energy kWh × $/kWh
Unnecessary motor operation (e.g.overnight operation)
Energy (kWh) = kW x t(hrs)
Cost = kWh x $/kWh
Required Inputs
Mass flow rate = either water or air
ΔT = temperature difference before and after
Specific heat = 1.213kg/kJ°C (air) | 4.187kg/kJ°C (water)
Density = 1.2kg/m3 (air) 1000kg/m3 (water)
System Coefficient of Performance = Whole system COP (~4 for chilled eater system)
Time = duration of fault (hrs)
Energy Cost = $/kWh (elec) or $/MJ (gas)
Example: Stuck cooling valve on a VAV box
Airflow: 0.5 m³/s
ΔT: 10°C
Hours/day: 12
Days/month: 20
System COP: 4
Approx. Savings:
Energy = (0.5 × 10 × 1.2 × 1.2 × 240) / 4 = ~108 kWh/month
Cost = 430kWh × $0.25/kWh = $27/month x 12 = $324 annual
Example: Early morning operation of AHU
Identified alert fan operating before required start time. AHu start time observed at 3am, occupied hours do not begin until 6am.
Fan motor size: 5.5kW
Hours/day: 3 (i.e. only include operation outside of scheduled hours)
Days/month: 20 (5 days per week, 4 weeks per month)
Approx. Savings:
Energy = 5.5 × 3 × x 20 = ~330 kWh/month
Cost = 330kWh × $0.25/kWh = $83/month x 12 = $996 annual
Tracking savings via PEAK
Once savings are calculated they can be added to PEAK Actions through entering the calculated savings amount into the Cost Savings modal for each action.
These can then be viewed for multiple actions including total sum identified.
Measurement & Verification with PEAK Meters
Savings from resolved FDD faults and identified Energy Conservations Measures (ECM’s) can be verified using PEAK’s integrated utility meters by comparing actual energy consumption before and after the fault resolution. PEAK meters provide high-resolution electricity, gas, or thermal data, allowing engineers to:
Baseline pre-fault energy use during similar operating conditions (e.g., weather, occupancy)
Track post-fix energy trends over days or weeks
Confirm measurable reduction in energy use that aligns with expected savings
This metered data provides tangible evidence to support the fault’s or ECM’s impact and validate savings beyond modeled estimates.
Step 1: Identify the Fault and Resolution Date
Use the PEAK Alerts and PEAK Charts:
Confirm the fault type (e.g., simultaneous heating and cooling)
Note the date the fault was resolved or manually fixed
Ensure the system returned to normal operating conditions
Step 2: Define Baseline and Post-Fix Periods
Choose comparison periods with similar:
Outdoor air temperature (OAT)
Equipment schedules or occupancy
Typical comparison windows:
Baseline: 2–4 weeks prior to fix
Post-fix: 2–4 weeks after fix
🔍 Tip: For seasonal ECM’s, compare to the same time last year if necessary.
Step 3: Access Relevant PEAK Meter Data
Navigate to the Meters module in PEAK
Select the relevant energy meter (e.g., electricity for AHU, gas for boilers)
Set resolution to hourly or daily
Apply filters to display baseline and post-fix periods side by side
📸 Suggested Screenshot:
PEAK meter trend showing pre- and post-fix daily energy usage
Annotate fault resolution date
Step 4: Compare Energy Consumption
Visually identify the drop (or stabilisation) in energy after the fix
Use built-in export tools or chart comparisons to calculate:
Total kWh/therms before vs after
Average daily usage change
✏️ Example: 3,200 kWh/month pre-fix → 2,200 kWh/month post-fix = 1,000 kWh/month savings
Recommendations:
Validate platform assumptions (e.g., hours, load profiles) using PEAK Charts
Adjust airflow, motor sizes estimates based on real site information, e.g. asset registers
Always use discretion for extreme or non-routine faults
Ensure you’re using actual operating schedules and not assumed 24/7 runtime.
Adjust $/therm, $/MJ or $/kWh based on the site utility bill or regional average.