Chiller Plant Optimisation Ultra Low Energy Management

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


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


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


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


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%


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


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


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


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


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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Chiller Plant Optimisation Ultra Low Energy Management