building energy data analytics: past, present, future · data analytics_june 2017_reddy 3 building...

T. Agami Reddy, Ph.D., PEEmail: [email protected]

Website: www.auroenergy.com

Workshop on

Big Data in Building Operations

Carleton University, Ottawa, Canada

June 27-28, 2017

1

Building Energy Data Analytics:

Past, Present, Future

Data Analytics_June 2017_Reddy

mailto:[email protected]

http://www.auroenergy.com/

Outline

Objectives– Provide panorama or overview of applications

– Identify specific aspects of some of the application categories

and provide status report

• Building Energy Data Analysis and Modeling Methods

• Brief Overview of Big Data and Relevant Applications

• Building Energy Data Analytics:- Building design

- Building operations:

+ Auditing

+ M&V

+ Day-to-day O&M (Demand response, CC, condition monitoring/FDD, forecasting and supervisory control)

Data Analytics_June 2017_Reddy 2


Building Energy Data Analysis and Modeling Methods

Statistical Theory/Concepts

Classical Parametric

Classical Non-Parametric

Resampling

Data Mining and Machine Learning

Non-Parametric (data driven)

Big Data

Transparent algebraic modelsOpaque models with no clear analytical expression and output processed internally

Inferring Population Behavior from Samples Classification/ Clustering/Association(need large samples)

Data Analytics is the algorithmic implementation of these methods

• Three basic elements

- Huge amounts of heterogeneous multi-source data

- Datafication, storage, retrieval, and processing/analysis

- Big data mind set: unique and novel ways of how to tap data so as to unlock value in terms of useful and actionable knowledge

• Uses social media, open source and govt data,....

• Size of the data compensates for more noisy non-curated data

• Provides general non-causal trends and probabilities which were unanticipated

• Value of domain expertise matters less in identifying trends

• Whole suite of new tools, procedures and software for data capture, datafication, storing in databases, retrieval/quering, processing,...)


V. Mayer-Schonberger and K. Cukier, “Big Data”, John Murray, 2013

Then Now


- Is this simply a flashy leitmotif of the knowledge economy? Internet of Things (IoT)

- Increases dangers of false learning beliefs and unjustified confidence

- Does it provide the degree of quantification necessary for relevant and actionable

implementation?

Relevant Big Data Applications

Building Operations

Learning energy

consumption in residences

(DM using ANN

ensembles and adaptive)

Electric Utilities

DatamineSMI (cluster

customers for rate plan

modification)

Smart Grid Operations

City Level

Routine City

Operations

Extreme Events Mgmt

Development Planning for Aspirational

Goals (carbon neutrality,

sustainable cities)


Bldg Energy Data Analytics for Decision Support Systems

Bldg Design

Individual BldgCluster of Bldgs

(campus, community, city)

Bldg Operations

Past

• Heuristic trial and error

• Design of experiments and regression

• Computer science techniques (expert systems, ontology-based ...)

Present

• Inclusion of daylighting, passive strategies

• Design parameter sensitivity and range of variability

• Simulation based tools -Monte Carlo, optimiz. (GA)

• Enhanced visualization

Future

• Specialized simulation environments

• Sophisticated ML tools for processing batch simulations

• Interactive design assistants

• Dynamic filters for credibility checks on simulations

1/4


Individual Building Operations

Auditing

BenchmarkingWalk-thru

auditInvestment grade audit

M&VDay-to-Day

O&M

Past

• Benchmarking using EUI

• Audit done using utility bills, spot measurements

• Engg calculations or calibrated simulation

• Heuristic expert systems ECM (FEDS program)

Present

• Asset score (ASHRAE)

• EUI+ regression (EPA-PM)

• Interval data + spot meas.

• Screening parameters of inverse change point models

• Improved calibration simulation

Future (remote, peer groups)

• Leverage SMI data using sophisticated ML tools

• Creation of databases for bldg load prototypes- DM

• Simulation based ECM (USDOE Asset Score Management tool

2/4


Building Operations

Auditing M&V

Indiv. BldgUtility Bills

Indiv. BldgMonitoring

Cluster of Bldgs

Day-to-Day O&M

Past

• Utility bills (VBDD- PRISM, MMT)

• Interval data: Change Point or calibrated simulation

• Disaggregated data: ECM isolation or equipment calibration

Present

• Hourly or sub-hourly Change Point models or ANN models

• Robust modeling: cross-validation methods

• Improved calibrated simulation using end-use data

Future (baseline modeling)

• Non-parametric methods

• More sophisticated ML tech.

• DM methods (random forest)

• Short-term monitoring

• Portfolio analy. (group bldgs)

• Improve Change Point modeling and error metrics

3/4

The intent of this research is to identify simple modeling techniques to determine the best time to begin in-situ monitoring of building energy use, and to determine the least amount of data required for generating acceptable long term predictions.

SCATTER PLOTS TIME SERIES PLOTS

WBE

CHW

WBE

CHW

HW HW

9

Change point behavior creates problems for short-term monitoring

Singh, Reddy and Abushakra (2014). Predicting Annual Energy Use in Buildings Using

Short-Term Monitoring: The Dry Bulb Temperature Analysis (DBTA) Method, ASHRAE

Trans., January.

How can length of monitoring be reduced to save on M&V costs?



Subbarao, Etingov and Reddy (2014) , An Actuarial Approach to Retrofit Savings in Buildings. ASHRAE Trans.

Suitable for risk analysis by financial institutions for large investment decisions

Portfolio Analysis to Determine ECM Savings in Groups of Buildings


Building Operations

AuditingDay-to-Day

O&M

Individual BldgMonitoring

Residences Small/medium commercial

Large Commercial

Cluster of Bldgs

Integrated Energy Systems

M&V

Inverse Statistical Models

Data Mining and Machine Learning

Calibrated Simulation

Demand Side Mgmt.

Demand Response

Condition Monitoring

AFDDE CommissionSupervisory

Control

Forecasting, Control and

Dispatch

4/4


Calibration of Detailed BldgSimulation Programs

Individual Bldg

Urban BldgEnergy Modeling

(UBEM)

Each and every bldgEmpirical modeling of

city blocks

Archtype or prototype bldg.

model (with and w/o canopy correction)

Weather research and forecasting

(WRF) mesoscale models (1 km x 1 km)

Past

• Heuristic trial and error

• Simulation search methods (MC)-parameter sensitivity

• Simulation optim(GA)

Present/Future

• Machine Learning Optimization tools (Autotune, GA)

• Automated capture of bldg. imagery (GIS, satellite, airborne, land-based, cartographic) and HVAC prototypes (databases)

• Add-ons to EnergyPlus- canyon/canopy correction ( impact of neighboring blds on radiation, airflow and temperature distributions)

• Coupling WRF grids with proper specs of buildings roads/open spaces of as grid macro inputs


- Fei Zhao (2012). Agent-based modelling of commercial building stocks for

energy policy and demand response analysis, Ph.D dissertation, Georgia Tech.

- Julia Sokol (2015). Deriving archtype templates for urban building energy models

based on measured monthly energy use, Ph.D dissertation, MIT

14

Difficulties during Day-to-Day O&M

•Building energy systems and equipment routinely perform

below expectations

•Building operators have limited time and expertise

•Unnecessary or improper manual overrides in response to

occupant complaints

•Buildings are dynamic entities which need to be retuned

constantly

•Limited number of sensors and manpower for smaller buildings

•Too many sensors in large buildings with EMCS

- Sensor info may overwhelm operator

•Degradation goes unnoticed for extended periods


Day-to-Day Operations

Condition MonitoringDefinition: the design and use of sensing equipment and analysis methods to monitor and report on current status and to detect a significant change due to improper operation or equipment degradation attributable to a fault (soft or hard)

Data Analytics_June 2017_Reddy15

Condition Monitoring

Report on current status

Improper Operation

Whole Building

Specific Equipment

Equip/System Perform. Degradation (FDDE)

Fault Detection (FD)

Fault Diagnosis

Evaluation/ Prognosis

EEOs Identification Detect Isolate Commission Replace

Day-to-Day Operations

Condition Monitoring• Advantages: reduces energy use, prolong equipment life and

reduce cost associated with service and maintenance

• Involves monitoring, and identification of improper building start/stop, equipment hard/soft faults, occupant behavior

• Suite of sensing methods (visual, thermal, electrical, vibrational, optical, tribology...)

• Suite of analysis methods- generally requires higher level of modeling sophistication than needed for M&V

• Model residual behavior acquires greater importance

• Need refined and local error metrics superior to global MBE, RMSE or CV

Data Analytics_June 2017_Reddy16

Plot of WBE[MJ]

0 200 400 600 800

predicted

0

200

400

600

800

ob

se

rve

dPhoenix- Medium Office Building- Weekdays- TMY3

Outlier Plot with Sigma Limits

Sample mean = 0.000115598, std. deviation = 44.6723

0 2 4 6(X 1000)Row number

-200

-100

0

100

200

WB

E_

Re

s_

TM

Y

-4

-3

-2

-1

0

1

2

3

4


Sample mean = -12.0185, std. deviation = 50.9113


-200

-100

0

100

200

WB

E_

Re

s_

20

13

-4

-3

-2

-1

0

1

2

3

4

CP-MLR Model Residuals for both Training and Prediction are patterned due to Thermostat Set-point Change

COOLING schedule:Until 5 am = 26.7 C5 am - 6 am = 25.6 C6 am - 7 am = 25 C7 am - 10 PM = 24 C10 PM - midnight = 26.7 C

HEATING schedule:Until 5 am = 15.6 C5 am - 6 am = 17.8 C6 am - 7 am = 20 C7 am - 10 PM = 21 C10 PM - midnight = 15.6 C

Tdb, Tdp,Tdb

+, Tdp+

Elight, Eplug




-200

-150

-100

-50

0

50

100

150

200

Re

s_

TM

Y_

AN

N

-4-3-2-101234




-200

-150

-100

-50

0

50

100

150

200

Re

s_

20

13

_A

NN

-4-3-2-101234




-200

-100

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200

WB

E_

Re

s_

TM

Y

-4

-3

-2

-1

0

1

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-200

-100

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100

200

WB

E_

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20

13

-4

-3

-2

-1

0

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4

CP-MLR Models

ANN_MLP-BP (using CP terms)

X-bar Chart for WBE_Res_TMY

0 50 100 150 200 250 300

Subgroup

-26

-16

-6

4

14

24

34

X-b

ar

CTR = 0.00UCL = 24.41

LCL = -24.41

Range Chart for WBE_Res_TMY

0 50 100 150 200 250 300

Subgroup

0

50

100

150

200

250

300

Ran

ge

CTR = 155.24

UCL = 240.39

LCL = 70.10

CP-MLR Models

ANN_MLP- BP (using CP terms)X-bar Chart for Res_TMY_ANN

0 50 100 150 200 250 300

Subgroup

-26

-16

-6

4

14

24

34

X-b

ar

CTR = -0.36UCL = 13.88

LCL = -14.60

Range Chart for Res_TMY_ANN

0 50 100 150 200 250 300

Subgroup

0

50

100

150

200

250

300

Ran

ge

CTR = 90.59

UCL = 140.27

LCL = 40.90

Constant Deviation

Base load Deviation

Late Shutdown

Early Startup

Example 1: Condition Monitoring

Automatic Identification of Actionable Energy Efficiency Opportunities (EEOs) from Interval Data for Small-Medium Buildings

Types of EEOs Studied

Howard, Reddy and Runger (2016) Automated Data Mining Methods for Identifying Energy Efficiency Opportunities Using Whole-Building Electricity, ASHRAE Trans., January

Overview of Analytical Method

Infer constant & baseload deviation EEOs from clusters

Infer early startup & late shutdown

EEOs

Quantify EEO savings potential

Cluster temperature-

adjusted values

Adjust electricity for temperature

effects

Determine occupied &

unoccupied period

Preprocess Data


Stage 1: Schedule EEO Detection, CO Office


• Letting 𝑦𝑖(𝑡) denote the electricity consumption during hour 𝑡 of day 𝑖, we fit the following model for each day:

ො𝑦𝑖 𝑡= 𝛽0 + 𝛽1𝑡 + 𝛽2 𝑡 − 𝑘𝑖1 + + 𝛽3 𝑡 − 𝑘𝑖2 + + 𝛽4 𝑡 − 𝑘𝑖3 +

+ 𝛽5 𝑡 − 𝑘𝑖4 + + 𝛽6 𝑡 − 𝑘𝑖5 + + 𝛽7 𝑡 − 𝑘𝑖6 +

• Knot points 𝑘𝑖𝑗 in equation above chosen for each day to minimize the

residual sum of squares:

𝑅𝑆𝑆𝑖 =

𝑡=1

24

[𝑦𝑖 𝑡 − ො𝑦𝑖 𝑡 ]2

Six knot spline regression found to be best fordetecting startup and shutdown ofoffice bldgs

Schedule EEO Results: CO Office


5,21 5,23

23


BehaviorNumber of

days

Savings opportunity

percent

Annual savings opportunity

(kWh)

normal 159 0% 0

late shutdown 40 6.67% 23,985

early startup 25 3.41% 7,969

early shutdown 3 -3.51% -1,028

early startup, late shutdown

21 10.27% 20,167

late startup 2 -3.68% -709

other 3 -1.88% -549Data Analytics_June 2017_Reddy 25

Stage 2: Amplitude EEO Detection

• Using the knot points identified in stage 1, we determine the hours in which the building was occupied and unoccupied for each day as follows:– Occupied period: 𝑘𝑖2 < 𝑡 < 𝑘𝑖5– Unoccupied period: 𝑡 < 𝑘𝑖1 or 𝑡 > 𝑘𝑖6

• Let ҧ𝑥𝑖,𝑜 and ത𝑦𝑖,𝑜denote the mean hourly external temperature and the mean hourly electricity consumption during occupied period of day 𝑖– ҧ𝑥𝑖,𝑈 and ത𝑦𝑖,𝑈 denote the same quantities but for unoccupied period of day 𝑖

• We fit the following spline regression models using robust regression, where 𝑘𝑜 and 𝑘𝑈 are chosen to minimize robust residual sum of squares:

– ො𝑦𝑖,𝑜 = 𝛽0,𝑜 + 𝛽1,𝑜 ҧ𝑥𝑖,𝑜 + 𝛽2,𝑜 ҧ𝑥𝑖,𝑜 − 𝑘𝑜 +

– ො𝑦𝑖,𝑈 = 𝛽0,𝑈 + 𝛽1,𝑈 ҧ𝑥𝑖,𝑈 + 𝛽2,𝑈 ҧ𝑥𝑖,𝑈 − 𝑘𝑈 +

• The residuals from these model are then divided by their actual values ത𝑦𝑖,𝑜and ത𝑦𝑖,𝑈, and the resulting values are clustered using DBSCAN


Amplitude Results: NM Office

BehaviorNumber of

days

Savings opportunity

percent

Annual savings opportunity

(kWh)

Cluster 1 amplitude 182 -0.84% -1526


Cluster 3 amplitude 12 13.17% 1971



Example 2: Mining of Monitored Data from Residences

“Mining Hidden Knowledge from Measured Data for Improving

Building Energy Performance”, Zhun Yu, Ph.D thesis, Concordia

University, January 2012

• Developed a classification decision tree methodology for establishing a

predictive model for energy demand in residences (low or high EUI)

• Cluster analysis to identify occupant behavior patterns that could save

energy

• Association rules identified correlations in building operational data which

could lead to energy savings by modifying mechanical ventilation

equipment

• Above three methods combined into a methodology to allow identifying

occupant behavior which needs to be modified and provide feasible

recommendations


29

Types of Maintenance Related to

Equipment Degradation

Reactive Maintenance(done in small bldswith limited staff)

PreventiveMaintenance(done by servicecompanies)

PredictiveMaintenance(results in continuouscommission)


Continuous-Commissioning/Re-Tuning

30

Ene

rgy

Co

nsu

mp

tio

n

Time

Typical commercial building behavior over time

Periodic Re-tuning Ensures Persistence

Continuous Re-tuning Maximizes Persistence

S. Katipamula (2012), Lessons learnt from Building Re-tuning Training,

ASHRAE Conf., Jan



Massieh Najafi (2010). Fault Detection and Diagnosis in Building HVAC Systems,Doctoral dissertation, Univ of California, Berkeley, Fall.

32

Definitions: FDDE

Fault: Abnormal operation

- hard and soft/incipient faults

- types: process, sensor, control

Detection: Signaling occurrence of a faulty condition, situation or operation

Diagnosis: Identifying the root cause of the fault- reasoning in the reverse direction: ascertain cause given effect

Evaluation: Impact of the fault on $ or equipment life

Action: Corrective measures taken --- Commissioning



D.M. Himmelblau (1978),

Fault Detection and Diagnosis

in Chemical and Petrochemical

Processes, Elsevier

Fault Detection

34

COP predicted

COP measured

tttt xy

** *

**

*

***

**

*

*

*

**

*

*

Uncertainty band

*

* ok*

*alarm

Model Based (Analytical) FD Methods



RP1043 Lab Chiller

2.0

2.5

3.0

3.5

4.0

4.5

5.0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Test Number

CO

P

Normal2

CF6

CF12

CF20

CF30

FDD method should be very sensitive:

Differences in COP between normal and faulty states are small as against

the large variation during normal operation

Variation of measured COP with operating conditions for Normal operation and under condenser fouling tests at four severity conditions

3/6

36

Fault Diagnosis Phil Haves, LBNL, 2004


• Diagnosis requires knowledge of how different faults affect operation

• Generally, three methods of diagnosing faults:

- Analyze how differences between predicted and actual performance

vary with operating points, e.eg, using IF/THEN rules

- Compare actual performance with different mathematical models

of faulty performance

- Estimate parameters of an on-line model extended to treat faults

(e.g. low estimated UA value indicates coil fouling)

Heuristic

Fault signatures

pattern recognition

Parameter estimation

D.M. Himmelblau (1978)


Chris Hobbs (2011?) Fault Tree Analysis with Bayesian Belied Networks for Safety-Critical

Software, QNX Software Systems

Appealing Method

38

Evaluating Different FDD MethodsNeed to weight Type I and Type II

errors by cost consequences

(false alarms and missed opportunities)


39

Evaluation Methodology

where

J1 cost penalty due to false positives

J2 cost in increased energy use due to missed detection of faults (or false negatives);

ce cost of electric energy

CFP cost of technician’s time to verify complete system in response to a false alarm

FN,f false negative rate for fault f (or missed opportunity rate)

FP false positive rate (or false alarm rate)

f index for fault type

NF total number of possible faults in system

Pf probability of occurrence of fault type f

P0 probability of occurrence of no-fault (i.e., fractional time of fault-free chiller operation)

Ef extra electric power required to provide necessary cooling as a result of fault type f

ft time period (hours) for which the fault f has gone undetected or un-rectified

FN

1 2 0 P FP e f f N,f f

f=1

min { + +...} min{P .F . c (P . E .F . t )}J J J C


Simple to formulate- hard to implement realistically

40

Past and Current Status of FDD in HVAC

• Heuristic process based FDD systems most common in the past

• Often relies on steady state data

• Largely limited to thermal sensors and analysis techniques

• Lot of papers on rooftop units, chillers and AHU

• Component isolation approach is best if feasible (need more sensors)

• Grey-box models have appeal but not lived up to expectation (such as characteristic parameter approach)

• ANN tend to give better modeling accuracy- but how does one train them for fault-free and faulty states?

• Grey-box model trained online (with forgetting factor) more sensitive than stationary models but prone to volatility

• High initial costs of sensing equipment and customization software limited widespread use (there are platforms such as VOLTRONwhich may alleviate the latter)


41

Current and Future Status of FDD in HVAC

• Several published papers proposing whole building diagnosticians

• Analytical redundancy and virtual sensor concepts proposed to reduce sensing hardware requirements

• Extension of single fault methods to multiple faults

• Clustering methods (proposed 20 years back) have been expanded into more comprehensive data mining methods

• Fault trees + Bayesian Belief Networks + fuzzy logic concepts also published

• Need to develop robust and automated data cleaning routines

• How best to train models online and update them?

• Development of self correcting sensor networks

• Better analysis methods for evaluation/prognostics (of fault impacts)

• Most important: very few real-time implementation of AFFD tools in the field to demonstrate benefits and reliability


Supervisory Control

• Heuristic

• Model based

• Adaptive

• Forecasting bldg. load, solar, PV output

• Whole building systems

• Cooling plants + thermal storage

• CHP

• Integrated energy systems with battery storage (distributed generation)

• How to explicitly include forecasting errors into scheduling and control strategies?


Prime Mover 1

Solar PV

Air-Handlers

Natural Gas

Boiler 1

Electricity Meter

Building Thermal Loads

Building Electric Loads

Grid

VC Chiller 1

VC Chiller 2

𝑄𝑏𝑜𝑖𝑙𝑒𝑟

𝐹𝑏𝑜𝑖𝑙𝑒𝑟1

𝐹𝑃𝑀1

𝐸𝑃𝑀1

𝑄𝑉𝐶1 𝐸𝑉𝐶1

𝑬𝒃𝒍𝒅𝒈

𝑸𝒄𝒐𝒐𝒍𝒊𝒏𝒈

𝑸𝒉𝒆𝒂𝒕𝒊𝒏𝒈

𝑄𝑉𝐶2 𝐸𝑉𝐶2

𝑬𝑷𝑽

AC Chiller 2𝑄𝐴𝐶2

Decision variablesNon-decision variablesProvided forecasts

CT-D

AC Chiller 1

CT-VC1 CT-VC2

𝑄𝐴𝐶1

CT-AC1 CT-AC2

Boiler 2

Prime Mover 2𝐹𝑃𝑀2

𝑄𝑃𝑀𝑄𝑃𝑀1

𝑄𝑃𝑀2

𝐸𝑃𝑀

𝐸𝑃𝑀2

𝐹𝑏𝑜𝑖𝑙𝑒𝑟2

𝑇𝑃𝑀

𝑚𝑃𝑀

𝑄𝑐𝑜𝑜𝑙𝑖𝑛𝑔

𝑄ℎ𝑒𝑎𝑡𝑖𝑛𝑔

𝑇𝑉𝐶𝑐ℎ𝑜1

𝑚𝑉𝐶𝑐ℎ𝑜1

𝑇𝑉𝐶𝑐ℎ𝑜2

𝑚𝑉𝐶𝑐ℎ𝑜2

𝑇𝑉𝐶𝑐𝑑𝑖1𝑇𝑉𝐶𝑐𝑑𝑖2𝑚𝑉𝐶𝑐𝑑𝑖1

𝑚𝑉𝐶𝑐𝑑𝑖2

𝑚𝑉𝐶𝑐𝑑𝑖

𝑇𝑉𝐶𝑐𝑑𝑖

𝑇𝐴𝐶𝑐ℎ𝑜1

𝑇𝐴𝐶𝑐ℎ𝑜2

𝑚𝐴𝐶𝑐ℎ𝑜1

𝑚𝐴𝐶𝑐ℎ𝑜2

𝑄𝐴𝐶

𝑇𝐴𝐶𝑐ℎ𝑜

𝑚𝐴𝐶𝑐ℎ𝑜

𝑇𝐻𝑅𝑈𝑜𝐷

𝑇𝑃𝑀𝑜𝐷

𝑇𝑃𝑀1

𝑚𝑃𝑀1

𝑇𝑃𝑀2

𝑚𝑃𝑀2

𝑇𝑃𝑀𝑜𝐻

𝑄𝑏𝑜𝑖𝑙𝑒𝑟1

𝑄𝑏𝑜𝑖𝑙𝑒𝑟2

𝑇𝑏𝑜𝑖𝑙𝑒𝑟2

𝑚𝑏𝑜𝑖𝑙𝑒𝑟2

𝑇𝑏𝑜𝑖𝑙𝑒𝑟1

𝑚𝑏𝑜𝑖𝑙𝑒𝑟1

𝑇𝑏𝑜𝑖𝑙𝑒𝑟

𝑚𝑏𝑜𝑖𝑙𝑒𝑟

𝑇𝐴𝐶𝑐𝑑𝑖1𝑇𝐴𝐶𝑐𝑑𝑖2𝑚𝐴𝐶𝑐𝑑𝑖1

𝑚𝐴𝐶𝑐𝑑𝑖2

𝑚𝐴𝐶𝑐𝑑𝑖

𝑇𝐴𝐶𝑐𝑑𝑖

𝑇𝑃𝑀𝑜𝐶

𝑇𝐻𝑅𝑈𝑜𝐻

𝑄𝑉𝐶𝑇𝑉𝐶𝑐ℎ𝑜𝑚𝑉𝐶𝑐ℎ𝑜

𝑇𝐴𝐶𝑜𝑢𝑡

𝐸𝑉𝐶

𝐻𝑋ℎ

𝐻𝑋𝑐

𝐻𝑋𝑑

𝐹𝑃𝑀

𝐹𝑏𝑜𝑖𝑙𝑒𝑟

𝑄𝐴𝐶𝑖𝑛2𝑄𝐴𝐶𝑖𝑛1

𝑄𝑃𝑀,𝐶

𝑇𝑃𝑀,𝐶

𝑚𝑃𝑀,𝐶

𝑄𝑃𝑀,𝐻

𝑇𝑃𝑀,𝐻

𝑚𝑃𝑀,𝐻

𝑄𝑃𝑀,𝐷

𝑇𝑃𝑀,𝐷

𝑚𝑃𝑀,𝐷

𝑄𝐻𝑅𝑈,𝐻

𝑇𝐻𝑅𝑈,𝐻

𝑚𝐻𝑅𝑈,𝐻

𝑄𝐻𝑅𝑈,𝐷

𝑇𝐻𝑅𝑈,𝐷

𝑚𝐻𝑅𝑈,𝐷

𝑄𝐴𝐶𝑖𝑛𝑇𝐴𝐶𝑖𝑛𝑚𝐴𝐶𝑖𝑛

𝐸𝑔𝑟𝑖𝑑𝑏𝑢𝑦𝐸𝑔𝑟𝑖𝑑𝑠𝑒𝑙𝑙


Parting Thoughts 1/2

1) How best to fulfill role of educators and academic research

- distill/assimilate past research and incorporate into curriculum (too many topics to cover and not enough course slots)

- better systemize and codify knowledge

- cross “t”s and dot “I”s (too many misconceptions/errors in current practice)

- balance between research and practice

- refrain from proposing esoteric methods with limited practicality

- theoretical development should not get too much ahead of practice

- always, always be critical of your own work!

(even if you do not express your thoughts aloud)


Parting Thoughts 2/2

2) Knowledge transfer of past work to current crop of R&D

engineers is getting harder

- too many journal and conference papers, hard to

absorb, repetitive, difficult to sort mediocre from good

3) What is the role of the domain expert in the age of data

mining?

4) Everything must be made as simple as possible, but not

simpler. -Einstein-


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