Slideset for participation in the GE Digital IIoT Predix Challenge

1. Learning and predictive operational interactions
by Predix for end to end energy management
Predix IIoT Challenge - April 2017
Team Name: Actionable Energy
Team Members: Henk Bredell
Lafras Moolman
Leon Myburg

2. The business challenge
 The need: Property owners need to reduce the cost of energy
 The challenges to overcome:
 Owners lack the ability to plan. This is mostly due to a lack of visibility of
actual consumption and reasons for the levels of consumption.
 Owners are also not able to intervene effectively in their consumption
patterns. Interventions imply that the owners actually understand what is
driving their consumption in order to implement improvements
 Consumption is a function of business requirements. Different business
processes vary in terms of their needs for electricity and they also differ in
terms of how they may be prioritised, scheduled or set up.
 These constraints are complex and require consideration in order for
business not to be negatively affected by energy interventions in realtime
 Once measures are implemented, it is hard to track the effect of the
initiatives and to track the improvement gains that have been made

3. Our approach
 Empowered by predictive intelligence
 There are different ways to save costs for energy:
 Shifting consumption to cheaper times
 Reducing the peak demand
 Reducing the extent to which power factor deteriorates (reactive power)
 Reducing wasteful consumption – when it is not needed
 Controls
 Hardwired controls – often rigid leading to discomfort and inefficiencies
 Policies and rules – often ineffective
 Manual scheduling – also rigid, time consuming and irrelevant after some
time
 Realtime sensing, forecasting, business owner interaction and tracking
 Predix will allow for
 realtime analysis, and learning to decide on policies and approach
 and then also help to schedule automatically and notify business people
 Track adherence to the plans and provide feedback to business owners

4. kW
Time
kW
Time
Options for energy savings
Energy
Management
kW
Time

5. Load shifting example
Actual peak time energy shifting results from project in Western Cape (RSA)

6. Peak clipping and load shift example
Actual energy results from project at a mine in South Africa
-
2 000
4 000
6 000
8 000
10 000
12 000
04:00:00 AM 08:00:00 AM 12:00:00 PM 04:00:00 PM 08:00:00 PM 12:00:00 AM
kW
Time
Average Weekday Demand Profile
Baseline Actual

7. Energy consumption reduction example
0
10 000
20 000
30 000
40 000
50 000
60 000 12:00:00AM
02:24:00AM
04:48:00AM
07:12:00AM
09:36:00AM
12:00:00PM
02:24:00PM
04:48:00PM
07:12:00PM
09:36:00PM
12:00:00AM
kW
Time
Weekday Average for March 2011
Baseline Target Average
Actual energy savings results from operations on project at a mine in South Africa

8. How it will work
Profile &
allocate
demand
1. Define
demand
centres
2. Allocate
historical
demand
3. Capture
owners
4. Capture
energy
needs
Real-time
demand
forecasting
1. Track real-
time demand
2. Pattern
recognition
3. forecast 1 day
total demand
4. Identify
demand
centres in
play
Iterative
optimisation
1. Schedule
demand
centres to
optimise
overall
demand
2. Optimise for
peak
reduction
3. Optimise for
PF
4. Remove non
essential
consumption
Notification
and control
1. Notify centre
owners of
activity
timeslots
2. Control
digital hold
or go signals
for equipm.
Benefits
tracking
1. Track
demand
centre
adherence
to schedule.
2. Track total
demand and
demand
shifting
achieved
3. Calculate
benefit from
historical

9. How it will work in Predix
Profile &
allocate
demand
Real-time
demand
forecasting
Iterative
optimisation
Notification
monitoring
and control
Benefits
tracking
Predix
Data
Owners
Organising
Consumption
Data – users,
demand,
constraints
Operations
schedules
Predix
adherence
learning
and benefit
tracking
Predix scheduling
algorithm
Predix predictive analytics
for forecasting
Predix Machine
Real-time data feed and controls
Notifications
Dashboards
Action and
Controls
Learning
and sharing
Optimisation
Sensing