load profiling tool to support smart grid operation scenariosload profiling tool to support smart...

26-27 September 2012 | Porto – Portugal Lyngby, Copenhagen, Denmark, October 06 - 09, 2013

THE 4TH EUROPEAN INNOVATIVE SMART GRID TECHNOLOGIES

Load Profiling Tool to Support Smart Grid Operation Scenarios

Sérgio Ramos, Isabel Praça, Zita Vale, Tiago M. Sousa, Vera Faria

GECAD – Knowledge Engineering and Decision Support Research Group

Polytechnic of Porto

Portugal

[email protected]


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Presentation outline

Introduction / objectives

Developed methodology

Developed tool

Algorithms

Case study

Scenario

Results

Conclusions


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Use of Data Mining (DM) techniques to characterize high voltage HV electrical consumers, based on Knowledge Discovery process applied to Databases (KDD)

Clustering analysis to identify typical load profiles of HV electricity consumers

Implementation of a classification model to classify new consumers

Development of an Automatic Data Treatment Application


• K-means algorithm

• Average-link

• Complete-link

• Ward’s-link

• Normalized Cut algorithm

• 12 Measurement validity indices

Sérgio Ramos, João Duarte, João Soares, Zita Vale,

Fernando J. Duarte, "Typical Load Profiles in the Smart Grid Context – A Clustering Methods Comparison",

IEEE Power and Energy Society General Meeting

2012, San Diego CA, USA, July 22 - 26, 2012.

• K-Means algorithm

• Normalized Cut algorithm

• Pairwise Constrained K-Means

• Metric Pairwise Constrained K-Means


Sérgio Ramos, João M. Duarte, F. Jorge Duarte, Zita

Vale, Pedro Faria, “A Data Mining Framework for Electric Load Profiling”, IEEE PES Conference on

Innovative Smart Grid Technologies (ISGT Latin

America 2013), São Paulo, Brazil, April 2013.

K-Means algorithm

New approach:

• K-Means algorithm (algorithm that always obtained the best quality

partition)


Development of an Automatic Data

Treatment Application (ADTA)

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Previous work / new approach

• K-means

•Two-Step

• Self-organizing map – SOM


Sérgio Ramos, Zita Vale, "Data Mining Techniques to Support the Classification of MV Electricity Customers", 2008 IEEE PES General Meeting, Pittsburgh, Pennsylvania, USA, July2 0-24, 2008. 0

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0.3

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0.5

0.6

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0.9

MIA CDI

Two-Step

K-Means

SOM


Following the KDD process:

In all previous work the data pre-processing step always have

been manually performed

The new approach intends to performed the data pre-processing

step automatically

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Proposed methodology - tool

Data Acquisition Data Pre-

processing Data Mining Application

Obtained Knowledge

Decisions


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New Pre-processing approach without users’ interference:

Load the files concerning customers electricity consumption from

the data base

Check if there are missing consumption values

Estimate missing values in the data (ANN)

Choice of the time horizon that is intended to obtain for the

typical load profiles (TLP)

• Spring, Summer, Autumn or Winter

• Working days, weekends, Saturday, Monday, National Holidays,

…


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New Pre-processing approach:

Output Data

Automatic Data Treatment Application

(ADTA)

Input Data Cleaned data

Formatted data Daily representative load profile

Spring data

Summer data

Winter data

Autumn data

Working days

Weekends days

Saturday

National holidays

Mondays


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Proposed methodology – algorithms

Clustering Process:

Starting from the ADTA output data:

The representative load diagrams are assembled in clusters

• K-means algorithm – (KM)

– Clustering algorithm that produced the best quality partition among several

clustering algorithms in previous work (Normalized Cut algorithm, Pairwise Constrained K-

Means, Metric Pairwise Constrained K-Means, Average-link, Complete-link, Ward’s-link,…)


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Proposed methodology – algorithms

Clustering Process:

The choice of the best numbers of clusters were based on the evaluation of 8

validity indices

Normalized Hubert Statistic – (NH)

Dunn index – (D)

Davies-Bouldin index – (DB)

SD validity index – (SD)

Silhouette statistic – (S)

Index I – (I)

XB cluster validity index – (XB)


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Case Study - scenario

Data description:

Real sample of 185 High Voltage Consumers

Collect period of data:

September 2010 – August 2011

96 values obtained per day:

( ) ( ) ( )1 ,... , 1... , 1...

m m m

norm norm hnorm

Vector Load Curve

x x x m M h H


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Case study - results

Traditionally, during the preprocessing data step:

All records belonging to customer’s files are checked (manually)

Filling gaps in data – ANN / Linear Regression

Power consumption Normalization – [0-1] (to be compared among them)

( ) ( )./m m

maxix x


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Load profiling results – K-means; k=4 clusters:

Summer work days Summer weekend days

Winter work days Winter weekend days



To classify new HV consumers in one of the obtained clusters

Classification algorithm – Decision Tree (C5.0)

Load Shape indices – Derived from the daily load diagrams Information about:

The daily load curve shape

The consumption pattern of each consumer

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av,day

1

max,day

Pf

P min,day

2

max,day

Pf

P

av,night

3

av,day

P1f

3 P

av,lunch

4

av,day

P1f

8 P

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

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

Time (h)

Po

wer

(p

.u.)

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Example of a rule set derived from the classification model:

Spring – working days

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If f2 ≤ 0,394

Else if f3 ≤ 0,516

and f1 ≤ 0,674 then cluster 4

and f1 > 0,674 then cluster 2

Else if f3 > 0,516 then cluster 3

Else then cluster 1

Classification results

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Conclusions

Implemented methodology for the characterization of

electricity High Voltage (HV) consumers based on real data

base from Portuguese Utility

Main Contribution:

Development of an Automatic Data Treatment Application –

ADTA – to analyze the input data through all the steps of the

pre-processing:

Data Cleaning

Estimating missing values

Data transformation

Data reduction volume


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Conclusions

Typical load profiles of HV consumers have been achieved

based on data mining techniques

The proposed method considers the performance comparison

of 4 clustering algorithms, based on previous authors work [1]

8 validity indices have been used to support the choice of the

best partition [1-3]:

Clustering algorithm – K-Means

Number of clusters – k=4 clusters


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Conclusions

A classification model that will enable the determination of the

class to which a new consumer should belong was

implemented and tested with very good accuracy

A classification rule set is proposed based on load shape

indices derived from the typical load curves

26-27 September 2012 | Porto – Portugal Lyngby, Copenhagen, Denmark, October 06 - 09, 2013


This work is supported by FEDER Funds through the “Programa Operacional Factores de Competitividade – COMPETE” program and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the projects FCOMP-01-0124-FEDER: PEst-OE/EEI/UI0760/2011, PTDC/EEA-EEL/099832/2008, PTDC/SEN-ENR/099844/2008, and PTDC/SEN-ENR/122174/2010.

Load Profiling Tool to Support Smart Grid Operation Scenarios

Sérgio Ramos, Isabel Praça, Zita Vale, Tiago M. Sousa, Vera Faria

[email protected]


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Previous results

Sérgio Ramos, João M. Duarte, F. Jorge Duarte, Zita Vale, Pedro Faria,

“A Data Mining Framework for Electric Load Profiling”, 2013 IEEE PES

Conference on Innovative Smart Grid Technologies (ISGT Latin America

2013), São Paulo, Brazil, April 15-17, 2013

Sérgio Ramos, João Duarte, João Soares, Zita Vale, Fernando J.

Duarte, "Typical Load Profiles in the Smart Grid Context – A Clustering

Methods Comparison", IEEE Power and Energy Society General

Meeting 2012, San Diego CA, USA, July 22 - 26, 2012

Sérgio Ramos & Zita Vale, "Data Mining Techniques to Support the

Classification of MV Electricity Customers", 2008 IEEE PES General

Meeting, Pittsburgh, Pennsylvania, USA, July2 0-24, 2008

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