CASE STUDY
Turning Energy into a Managed Asset
One-Year Energy Optimization Journey for a Power-Intensive MSME
The Challenge
Electricity was the single largest operating expense for the plant, with persistent issues:
Frequent contracted demand overruns leading to penalties
Poor power factor during partial loading and night shifts
Rising electricity bills with no clear explanation
Suspected harmonics-related heating in panels and transformers
No visibility into:
Energy consumption by process or equipment
Peak demand contributors
Power quality trends
Management wanted to control energy costs without reducing production.
Why BayaSense
BayaSense was chosen as the energy intelligence platform because it offered:
High-resolution energy and power quality monitoring
Edge analytics for demand and power factor tracking
Continuous harmonics measurement and trending
Role-based dashboards for operations and management
Ability to move from visibility to corrective action
Most importantly, BayaSense enabled a data-driven, year-long optimization program.
Business Impact
₹72 lakh annual energy cost savings
₹18 lakh avoided demand penalties
Reduced electrical failures and downtime
Improved transformer and equipment life
Payback period: < 6 months
Solution Overview
One-Year Optimization Journey
Monitoring Scope:
Main incomer and feeder-level energy meters
Injection moulding machine feeders
Utilities: chillers, compressors, cooling systems
Parameters Captured:
kWh, kVA, kW by equipment and utility
Maximum demand vs contracted demand
Power factor (instantaneous & average)
Harmonic distortion (THD-V, THD-I)
Voltage imbalance and loading trends
Dashboards:
Energy by usage and cost center
Demand & penalty risk dashboard
Power factor performance
Harmonics & power quality trends
Phase 1: Visibility & Baseline (Months 1–2)
Established energy baseline by machine and utility
Identified peak demand drivers
Quantified PF and harmonic distortion patterns
Phase 2: Contracted Load Adherence (Months 3–5)
Load staggering across moulding machines
Rescheduled chiller and compressor operation
Demand threshold alerts via BayaSense
Result: Zero demand penalties from Month 4 onward
Phase 3: Power Factor Maximization (Months 6–8)
Optimized capacitor bank switching logic
Identified low-PF machines during idle and part load
Corrected wiring and loading imbalances
Result: Average PF improved from 0.88 → 0.99
Phase 4: Harmonics Management (Months 9–12)
Identified high THD contributors (VFD-driven machines)
Installed tuned harmonic filters on critical feeders
Continuous THD monitoring to validate improvements
Result:
THD-I reduced from 22–28% → <10%
Transformer and panel heating reduced
Key Paramaters
Monthly energy cost
Demand penalties
Average power factor
THD-I (critical feeders)
Energy cost per kg
Before BayaSense
₹38–40 lakh
Frequent
0.88
22–28%
High variability
After BayaSense
₹31–33 lakh
Zero
0.99
<10%
Stable & reduced
Beyond Cost Savings: The Energy Roadmap
With BayaSense as the foundation, the MSME is now positioned to:
Track energy per SKU and mould
Support ISO 50001 energy management systems
Integrate renewable energy sources
Strengthen ESG and customer audits
Enable AI-driven energy optimization
Client Snapshot
Industry: Plastic Processing
Products: Injection-moulded automotive & appliance components
Company Size: MSME
Plant Load Profile:
- Injection moulding machines (80–450 ton)
- Centralized chillers & cooling towers
- Air compressors and material handling systems
Connected Load: ~900 kVA
Contracted Demand: 600 kVA
Operating Model: 3 shifts, 6 days/week
Key Takeaway
You cannot reduce what you do not measure—nor what you do not understand.
BayaSense helped this power-intensive MSME transform energy from an uncontrollable expense into a managed, optimized resource—with sustained savings year after year..