Measure interventions that improve output, uptime, and efficiency.
Energy and utilities teams operate complex systems where small optimizations compound at scale. Decision Process helps you run controlled experiments on maintenance schedules, demand response programs, and equipment configurations — and quantify the effect before you commit.
Experiment Templates
Ready-to-run experiments
Demand Response Incentive Test
“Time-of-use pricing incentives reduce peak demand more than flat rate plans.”
Conditions
- Flat rate plan (control)
- Time-of-use pricing
Metrics
- Energy Output (MWh)
- Efficiency Rate (%)
- Cost per kWh (USD)
Turbine Maintenance Interval Optimization
“90-day preventive maintenance intervals outperform 60-day for output and uptime.”
Conditions
- 60-day PM (control)
- 90-day PM
- Condition-based
Metrics
- Downtime Hours (hrs/month)
- Energy Output (MWh)
- Maintenance Cost (USD/yr)
Solar Panel Orientation A/B
“East-west bifacial panel orientation produces higher daily yield vs. south-facing.”
Conditions
- South-facing (control)
- East-west bifacial
Metrics
- Energy Output (MWh)
- Capacity Factor (%)
- Cost per kWh (USD)
Worked Example
Preventive maintenance intervals across 18 wind turbines
A wind farm operator randomly assigns turbines to one of three PM schedules: 60-day (current standard), 90-day extended, or condition-based triggered by sensor thresholds. 6 turbines per arm, measured over 9 months.
Results: downtime_hours / month
60-day PM (control)
mean: 14.2 hrs/mo
95% CI: 11.4–17.0
90-day PM
mean: 13.8 hrs/mo
95% CI: 11.0–16.6
P(better) = 61%
Condition-based PM
mean: 9.1 hrs/mo
95% CI: 7.0–11.2
P(better) = 97%
Condition-based maintenance reduces downtime by −5.1 hrs/month (d = −0.79, large effect) at 97% posterior probability vs. the 60-day schedule. The 90-day schedule shows no meaningful improvement — interval extension alone doesn't drive the gain. Recommendation: pilot condition-based PM across the full fleet.
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