Ultra Low Energy Management with IoT & Machine Learning Controls
Chiller Plant Optimisation
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IoT Definitions
The Internet of Things (IoT) is the inter-networking of physical devices, (also referred to as "connected devices" and "smart devices"), buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data.
Source : Wikipedia
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Data Analytics in the Energy world Deriving insights from data
Electrical Meter Meter Reading
99867
Interval Data
Time Value Unit
2017-08-01 0600 122 kWh
2017-08-01 0700 120 kWh
2017-08-01 0800 389 kWh
Trend Chart
Start Time 0600
End Time 1900
Duration 13 hrs
Anomalies Correlations Patterns
Statistical Data Outcomes
Predictive Maintenance Energy Optimization Fault Detection
Machine Learning Model
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IoT + Data Analytics Extremely powerful formula for Smart Decision Making
IoT Cloud
Other Relevant
Data
Energy
Occupancy
Temperature
Weather Geospacial
Multiple datasets from
sensor networks
+ = SMART Machine Learning
Model
Continuous Learning
engine to improve
performance over time
through artificial
intelligence
Descriptive
Predictive
Prescriptive
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Case Study Green Mark Platinum Building
Chiller Plant (24Hr Operation)
Cooling Load 250 - 475 RT
CH Plant Efficiency 0.55 – 0.64 ikW/RT
0.58 ikW/RT (Avg)
Total Input Power 135 kW – 300 kW
Setpoints
CH CHWS-T
CHWP DP
CDWP Flow Rate
CT CDWS-T
No new CH Plant hardware, No upgrades to BMS
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Optimization Approach Real Time Machine Learning Model
Predicted Outputs
Optimization Output
Optimization Model
Prediction Model
Real time Readings
BMS Executes and System
settles
1. Machine Learning Model developed
to mimic Chiller Plant operation.
2. Optimization Engine output is sent to
BMS for execution.
3. Real Time Data drives Machine
Learning model.
4. Error rate of ML model is +/- 1%
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LonWorks/BacNet/OPC IP Network
Control Valve
Sensor
DDC
Controller
Control Valve
Sensor
DDC
Controller
Control Valve Flow Meter
Controller
DDC for Chiller
Chiller, AHU, FCU
Power Meter
Power Meter
BMS Application
server
Sensor
Connected Chiller Plants
Simple, Non Disruptive Integration with existing Chiller Plant System (BMS)
RJ45
Power Meter
Power Meter
BMS Application
server KEM
Gateway
Internet
Energetix Cloud Dynamic Chiller Plant
Optimization
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System Architecture Integrate data from different devices onto open IoT Cloud Platform
Edge
(B
uild
ing)
D
ata
Acq
uis
itio
n
Clo
ud
In
fras
tru
ctu
re
Op
en
A
PI
Ap
plic
atio
ns
KEM
Gateway
BMS Sensor Controll
er
Site A
……..
KEM PLATFORM / ENERGETIX DATA LAKE Cloud IoT Platform
Access Control and Security
Data Transform
Standard Taxonomy
Platform Administration
KEM
Gateway
BMS Sensor Controll
er
Site N
Web Services RESTful / JSON
Impala ODBC/JDBC SQL Commands
Web Apps Mobile BI Tools AI
Open APIs allow data
interchange and co-
innovation
Choose from a range of
available Apps, BI
Tools and Data
Analytics applications
or develop specific front
end Apps rapidly,
KEM Platform enables
access controls and
provides data
transformation into
standard taxonomy.
Platform Admin provides
Device and Account
Management and
provisioning.
KEM Gateway enables
plug and play integration
with thousands of different
meters, sensors, BMS
systems, etc to acquire
data quickly and cost
effectively.
DaaS Data as a Service
3G/LTE 3G/LTE
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Findings Chiller
Chiller Efficiency Improvement
Analysis
1. Chillers are set to maintain a preset
temperature setting. Chilled Water
pumps are pressure controlled.
2. During periods when the cooling
requirements are lower (e.g. night
time), power consumption does not
reflect a lower value as the setpoint is
maintained.
3. Across days, the temperature may
vary for the same time period (e.g.
rainy vs sunny days).
With Optimization
1. Variance within the day and
fluctuations across is reduced.
Reduction in the spread in Power
6-8% Savings
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Findings Chilled Water Pump
CHWP Efficiency Improvement
With Optimization
1. CHWP power consumption is lower, but has a wider variance when demand is high.
2. Energy savings is consistently achieved at lower demand levels. At higher demands
levels, the spread of energy consumed is widened due to the constraints set into the
system to maintain cooling load at all times.
3. There is a possibility that certain components in the system may consumed more energy
but overall, the system will be optimized.
Reduction in the overall energy consumption
4-8% Savings
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Summary Ultra Low Energy Chiller Plants
Item Before Optimized Avg Savings %
Indoor Air Temp Settings As Set No change -
Cooling Load 250 – 475 RT No change -
Plant Efficiency 0.58 ikW/RT 0.495 iKW/RT 14.65%
ROI 2 months
Shared Savings Model – Pay only when there is Savings !
IoT Cloud Shared Savings
Machine Learning
Optimization Engine
Customer
Chiller Plant
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