Energiency provides software and data analytics to help industrial companies improve energy efficiency and reduce costs. Their solutions include monitoring energy and fluid usage in real-time, detecting savings opportunities, predictive modeling, and automatic alerts. Example customers include companies in food and beverage, automotive, paper, chemicals, steel, and glass. Through analyzing historical usage data and live streaming data with machine learning algorithms, Energiency has helped customers achieve energy savings of 2-10% and reduce energy costs.

1. energy intelligence for manufacturing
05 07 19

2. 2
Energiency
COMPANY OVERVIEW
Energy
Multi-fluids
electricity,
gas,
utilities
Data
Pure player
Software SaaS
Data analytics
Industry
F&Bev
Automotive
Paper
Chemistry
Steel
…
2013 20 p
Rennes
Lyon
1
* French Research AI Institute
*

3. 3
From data to energy savings potential detection
SOLUTION
Confidential
Data
Energy & fluids
Production & maintenance
Weather…
Day to day value
Monitoring & real time KPI
Automatic alerting & reporting
Predictive baseline & forecast
àTurn available raw data into new energy saving opportunities
2
Analytics
Off-line
historical data
Energy savings potential audit
Data science study
Cloud
live data
KPI computing
ML algorithms

4. 4
Unveil energy savings potential & improve
energy performance
COMPUTING USE CASE
Confidential
3
.
Agro-industry
Challenge
- Improve energy performance control (through monthly gas
consumption ratio)
Solution
- real time (5 mn) gas consumption ratio calculated / type
product
- automatic daily reports including energy performance for
the 11 families of fertilizers produced
Results
à 6% potential savings out of a # € 1 M/y energy billPaper industry
Challenge
- secure an already excellent control of electricity consumption
Solution
- real time (10 mn) electricity consumption ratio / type paper
- new target baseline calculated each year
Results
à # °/oo proven savings out of a # € 50 M/y electricity bill

5. 5
Real time energy performance control
COMPUTING USE CASE
Confidential
4
Chemical Industry
Challenge
- improve boiler house operations performance (300 MW capacity)
Energiency solution
- parametrical alerts : 750 alerting scenarios calculated each 30s
- automatic edition of energy performance shift reports each 8h
Results
- best practices “natural” alignment in 6 months
à 0.4% proven savings out of a # € 100 M/y energy bill

6. 6
AI energy savings potential audit
ML MODELS USE CASE
Data science study
Historical data gathering (18 months)
Descriptive analysis and modelling
On-site workshop
Energy savings potential calculation
Confidential
5
Descriptive analytics
Automotive - Paintshop
- 70 energy savings opportunities identified
à Gaz / Electricity : 5% potential savings on top 12
opportunities with low capex (ROI 0,3 y) = initial target
Correlation table
& ML models
Steel - Rolling mill furnace
- 1600 data flows / 1y / 1s
- Relevant influential
variables identified
- 3 models in place
à 2,4% potential savings
with no capex

7. 7
AI real time over-consumption alerts #1
ML MODELS USE CASES
Confidential
6
Mechanics Industry
Challenge
- Improve energy performance thanks to AI
- Energiency tested on half of the plant, where 30% energy
savings was already achieved with a dedicated team
- Energy bill : 1 M€/y
Solution
- 2 real time models (Elec, CA) feeded with production data (8
channels, 120 references)
Results
àElectricity : 5% proven savings
àCA : 10 % proven savings

8. 8
AI real time over-consumption alerts #2
ML MODELS USE CASE
Confidential
7
Steel Industry
Challenge
- improve rolling mill gas furnace energy performance
Solution
- gas consumption machine learning model
- real time alerts (orange / red)
Results
à 2 % energy savings potential out of a # €5 M/y energy
bill

9. 9
AI forecast models : reduce energy cost
ML MODELS USE CASE
Confidential
8
Paper Industry
Challenge
- Improve day ahead nomination accuracy to
increase electricity bill rebate
Solution
- Machine learning forecast model based on
production planning
Results
à 20% rebate increase
Steel Industry
Challenge
- et up intraday nomination accuracy to reduce
imbalance penalty
Solution
- Machine learning forecast model based on
production real time and forecast data
- Automatic 1h renomination
Results
à Energy costs reduction potential #%

10. 10
Some Energiency references
REFERENCES
Confidentiel
13

11. 11
Energiency activity in Glass sector
REFERENCES
Confidentiel
13
Targets (ongoing proposals)
• St Gobain Sekurit (flat glass)
• Verescence (perfume bottles)

12. CONTACT loic.fargette@energiency.com
+33 (0)6 80 89 08 82
@ energiency
energiency.com