Predictive analytics for energy efficiency and energy retailing:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Bamberg
University of Bamberg Press
2019
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| Schriftenreihe: | Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg
Band 36 |
| Schlagworte: | |
| Online-Zugang: | Volltext Inhaltsverzeichnis |
| Beschreibung: | xxvi, 251 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9783863096687 |
Internformat
MARC
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| 100 | 1 | |a Hopf, Konstantin |e Verfasser |0 (DE-588)1191118517 |4 aut | |
| 245 | 1 | 0 | |a Predictive analytics for energy efficiency and energy retailing |c Konstantin Hopf |
| 264 | 1 | |a Bamberg |b University of Bamberg Press |c 2019 | |
| 300 | |a xxvi, 251 Seiten |b Illustrationen, Diagramme |c 24 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 856 | 4 | 1 | |u https://fis.uni-bamberg.de/handle/uniba/45617 |x Verlag |z kostenfrei |3 Volltext |
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| adam_text |
CONTENTS
TABLE
OF
CONTENTS
I
ABSTRACT
XVII
KURZZUSAMMENFASSUNG
XXI
ACKNOWLEDGEMENTS
XXV
1
INTRODUCTION
AND
MOTIVATION
1
1.1
AMBIENT
DATA
AS
A
NEW
SOURCE
FOR
ANALYTICS
.
3
1.2
RESEARCH
GOAL:
VALUE
CREATION
FROM
AMBIENT
DATA
THROUGH
MA
CHINE
LEARNING
.
7
1.3
EMPIRICAL
RESEARCH
WITHIN
THE
THE
CONTEXT
OF
ENERGY
EFFICIENCY
AND
ENERGY
RETAILING
.
9
1.3.1
ELECTRICITY
RETAIL
MARKET:
CURRENT
CHALLENGES
AND
OPPOR
TUNITIES
.
10
1.3.2
CASE
1:
SCALABLE
ENERGY
EFFICIENCY
CAMPAIGNS
.
13
1.3.3
CASE
2:
RELATIONSHIP
MARKETING
IN
ENERGY
RETAILING
.
14
1.4
STRUCTURE
OF
THIS
WORK
AND
EARLIER
PUBLICATIONS
.
15
2
RELATED
WORK
AND
THEORETICAL
BACKGROUND
21
2.1
VALUE
CREATION
FROM
BIG
DATA
.
21
2.1.1
DATA-DRIVEN
DECISION
MAKING
PROCESS
.
23
2.1.2
PREDICTIVE
ANALYTICS
IN
INFORMATION
SYSTEMS
RESEARCH
.
25
2.1.3
FRAMEWORKS
AND
PROCESS
MODELS
FOR
BIG
DATA
ANALYTICS
.
.
27
2.1.4
RESEARCH
GAPS
IN
INFORMATION
SYSTEMS
RESEARCH
.
28
2.2
HOUSEHOLD
CHARACTERIZATION
BASED
ON
ELECTRICITY
CONSUMPTION
DATA
29
2.2.1
FACTORS
INFLUENCING
RESIDENTIAL
ELECTRICITY
CONSUMPTION
.
.
30
2.2.2
NON-INTRUSIVE
LOAD
MONITORING
(NILM)
.
30
2.2.3
CLUSTERING
OF
ENERGY
CUSTOMERS
.
30
2.2.4
HOUSEHOLD
CLASSIFICATION
.
31
2.2.5
RESEARCH
GAPS
IN
HOUSEHOLD
CLASSIFICATION
.
31
2.3
RELATIONSHIP
MARKETING
.
34
2.3.1
INFORMATION
SYSTEMS
FOR
CUSTOMER
RELATIONSHIP
MANAGE
MENT
.
35
2.3.2
PREDICTIVE
SEGMENTATION
AND
CUSTOMER
SCORING
.
36
2.3.3
PURCHASE
INTENTION
AND
BEHAVIOR
.
37
2.3.4
RESEARCH
GAPS
IN
RELATIONSHIP
MARKETING
RESEARCH
.
38
3
DATA
SOURCES
IN
ORGANIZATIONS
AND
EXTRACTION
OF
PREDICTOR
VARIABLES
39
3.1
THEORETICAL
BACKGROUND
AND
RESEARCH
QUESTIONS
.
41
3.1.1
NEED
FOR
A
SYSTEMATIC
OVERVIEW
TO
AVAILABLE
DATA
SOURCES
41
3.1.2
ALGORITHMIC
VERSUS
THEORY-BASED
EXTRACTION
OF
PREDICTOR
VARIABLES
(FEATURES)
.
43
3.2
TAXONOMY
OF
DATA
SOURCES
AVAILABLE
FOR
PREDICTIVE
BUSINESS
ANA
LYTICS
.
45
3.2.1
INTERNAL
BUSINESS
DATA
.
46
3.2.2
EXTERNAL
DATA
SOURCES
.
48
3.2.3
CONTRIBUTION
OF
THE
TAXONOMY
.
50
3.2.4
LIMITATIONS
AND
FUTURE
RESEARCH
.
50
3.3
DIMENSIONALITY
REDUCTION
THROUGH
EMPIRICAL
FEATURE
EXTRACTION
.
51
3.3.1
FEATURES
FROM
UTILITY
TRANSACTION
DATA
.
52
3.3.2
FEATURES
FROM
ENVIRONMENTAL
DATA
.
64
3.3.3
FEATURES
FROM
GEOGRAPHIC
INFORMATION
.
66
3.3.4
FEATURES
FROM
GOVERNMENTAL
STATISTICAL
DATA
.
68
3.3.5
CONTRIBUTION
OF
EMPIRICAL
FEATURE
EXTRACTION
TO
MODEL
BUILDING
.
71
3.4
DIMENSIONALITY
REDUCTION
THROUGH
AUTOMATIC
FEATURE
SELECTION
.
.
74
3.4.1
TYPES
OF
AUTOMATIC
FEATURE
SELECTION
APPROACHES
.
75
3.4.2
COLLECTION
OF
FEATURE
SELECTION
METHOD
(FSM)
IN
R
.
.
.
76
3.5
DISCUSSION
AND
IMPLICATIONS
.
80
4
MACHINE
LEARNING
METHODS
FOR
PREDICTIVE
ANALYTICS
83
4.1
OVERVIEW
TO
SUPERVISED
MACHINE
LEARNING
ALGORITHMS
.
85
4.2
CLASSIFICATION
PERFORMANCE
EVALUATION
.
87
4.2.1
METRICS
FOR
DEPENDENT
VARIABLES
WITH
MULTIPLE
CLASSES
.
.
88
4.2.2
METRICS
FOR
TWO-CLASS
PROBLEMS
.
90
4.2.3
REFERENCE
STATISTICS
FOR
INTERPRETATION
OF
PERFORMANCE
MET
RICS
.
92
4.2.4
CALCULATION
OF
PERFORMANCE
MEASURES
.
92
4.2.5
COMPARISON
OF
PERFORMANCE
METRICS
.
93
II
4.3
DESCRIPTION
OF
SELECTED
SUPERVISED
MACHINE
LEARNING
ALGORITHMS
.
94
4.3.1 K
NEAREST
NEIGHBORS
(KNN)
.
94
4.3.2
NAIVE
BAYES
.
94
4.3.3
SUPPORT
VECTOR
MACHINE
(SVM)
.
95
4.3.4
ADABOOST
.
95
4.3.5
RANDOM
FOREST
(RF)
.
95
4.3.6
EXTREME
GRADIENT
BOOSTING
(XGB)
.
96
4.4
DISCUSSION
AND
CONCLUSION
.
99
5
HOUSEHOLD
CLASSIFICATION
FOR
ENERGY
EFFICIENCY
AND
PERSONALIZED
CUSTOMER
COMMUNICATION
101
5.1
DATASETS
WITH
RESIDENTIAL
ENERGY
CONSUMPTION,
HOUSEHOLD
LOCA
TION
AND
SURVEY
DATA
.
105
5.1.1
DATASET
A
AND
B:
ANNUAL
ELECTRICITY
CONSUMPTION
DATA
.
107
5.1.2
DATASET
C:
DAILY
SMART
METER
DATA
.
109
5.1.3
DATASET
D:
15-MIN
SMART
METER
AND
SURVEY
DATASET
.
109
5.2
HOUSEHOLD
PROPERTIES
(DEPENDENT
VARIABLES)
.
113
5.3
PERFORMANCE
OF
MACHINE
LEARNING
ALGORITHMS
IN
SMART
METER
HOUSE
HOLD
CLASSIFICATION
.
118
5.4
BENCHMARK
OF
FSMS
FOR
SMART
METER
HOUSEHOLD
CLASSIFICATION
.
.
120
5.4.1
EARLIER
STUDIES
COMPARING
FSMS
.
121
5.4.2
QUALITY
CRITERIA
FOR
FSM
PERFORMANCE
.
123
5.4.3
ACCURACY
IMPROVEMENT
OF
FSMS
IN
A
MINIMAL
VIABLE
SETUP
124
5.4.4
STABILITY
OF
FEATURE
SELECTION
.
125
5.4.5
CORRELATION
OF
ALGORITHM
RUNTIME
AND
ACCURACY
IMPROVE
MENT
.
127
5.5
PREDICTABILITY
OF
HOUSEHOLD
CHARACTERISTICS
BASED
ON
DIFFERENT
DATA
GRANULARITIES
.
128
5.5.1
ANNUAL
ELECTRICITY
CONSUMPTION
DATA
WITH
GEOGRAPHIC
AND
STATISTICAL
DATA
.
128
5.5.2
DAILY
ELECTRICITY
CONSUMPTION
DATA
.
130
5.5.3
SMART
METER
DATA
.
132
5.5.4
GEOGRAPHIC
TRANSFERABILITY
OF
MODELS
.
134
5.6
DISCUSSION
AND
IMPLICATIONS
.
137
5.6.1
RECOGNITION
OF
HOUSEHOLD
CHARACTERISTICS
BASED
ON
ELEC
TRICITY
CONSUMPTION
DATA
.
137
5.6.2
LIMITATIONS
AND
FUTURE
RESEARCH
.
142
5.6.3
PRACTICAL
IMPLICATIONS:
MODEL
IMPROVEMENT
BEYOND
ALGO
RITHMS
TUNING
.
142
III
6
PERSONALIZED
HOME
ENERGY
REPORTS
FOR
USER
ENGAGEMENT
AND
RESI
DENTIAL
ENERGY
EFFICIENCY
145
6.1
CUSTOMER
ENGAGEMENT
THROUGH
TAILORED
ENERGY
FEEDBACK
.
146
6.2
DEVELOPMENT
OF
A
PERSONALIZED
E-MAIL
ENERGY
REPORT
.
148
6.3
EXPERIMENTAL
AND
SURVEY-BASED
EVALUATION
OF
THE
ENERGY
REPORT
.
150
6.3.1
TIMELINE
OF
THE
STUDY
.
150
6.3.2
SAMPLE
DESCRIPTION
.
151
6.3.3
CUSTOMER
SURVEY
.
153
6.4
ANALYSIS
OF
THE
FIRST
EXPERIMENT
AND
RESULTS
FOR
THE
BASE
ENERGY
REPORT
.
154
6.4.1
CUSTOMER
REACTIONS
TO
THE
ENERGY
REPORT
MAILING
.
154
6.4.2
PORTAL
USAGE
.
154
6.4.3
ELECTRICITY
CONSUMPTION
.
158
6.4.4
USABILITY
AND
USER
PERCEPTION
OF
THE
REPORT
.
162
6.4.5
CUSTOMER
SATISFACTION
WITH
THE
UTILITY
COMPANY
.
164
6.4.6
ATTITUDES
TOWARDS
ENERGY
CONSERVATION
.
166
6.5
ANALYSIS
OF
THE
SECOND
EXPERIMENT
AND
CONTRIBUTION
OF
HOUSEHOLD
CLASSIFICATION
.
167
6.5.1
PERSONALIZED
FEEDBACK
ELEMENT
FOR
COMPARISON
WITH
SIMI
LAR
HOUSEHOLDS
.
168
6.5.2
EXPERIMENT
SETUP
.
169
6.5.3
EXPERIMENT
RESULTS
.
169
6.6
DISCUSSION
AND
IMPLICATIONS
.
170
7
SUPPORTING
CROSS-SELLING
MARKETING
CAMPAIGNS
WITH
PREDICTIVE
AN
ALYTICS
173
7.1
FIBER-TO-THE-HOME
(FTTH)
AS
A
RELEVANT
PRODUCT
FOR
UTILITY
COM
PANIES
.
175
7.2
DESCRIPTIVE
INSIGHTS
ON
THE
PURCHASE
INTENTION
OF
RESIDENTIAL
CUS
TOMERS
.
176
7.3
PREDICTIVE
ANALYTICS
TO
IDENTIFY
CUSTOMERS
WITH
HIGH
INTEREST
IN
FTTH
.
179
7.3.1
DATA
AND
FEATURES
.
180
7.3.2
SUPERVISED
MACHINE
LEARNING
AND
PERFORMANCE
EVALUATION
181
7.4
CONTRIBUTION
TO
THE
PLANNING
AND
EXECUTION
OF
CROSS-SELLING
MAR
KETING
CAMPAIGN
.
183
7.4.1
CUSTOMER
SCORING
(OPERATIONAL
DECISION
SUPPORT)
.
183
7.4.2
COST-BENEFIT
ANALYSIS
(TACTICAL
DECISION
SUPPORT)
.
184
IV
7.4.3
CONVERTING
PREDICTED
PURCHASE
INTENTIONS
INTO
PURCHASE
PROBABILITIES
TO
ESTIMATE
MARKET
SIZE
(STRATEGIC
DECISION
SUPPORT)
.
186
7.5
CONCLUSION
AND
LIMITATIONS
.
188
8
SUMMARY
AND
IMPLICATIONS
191
8.1
SUMMARY
OF
THE
RESULTS
.
193
8.2
IMPLICATIONS
FOR
RESEARCH
AND
FUTURE
WORK
.
198
8.2.1
VALUE
CREATION
THROUGH
PREDICTIVE
ANALYTICS
.
198
8.2.2
ENERGY
INFORMATICS
TO
SUPPORT
ENERGY
EFFICIENCY
.
199
8.2.3
RELATIONSHIP
MARKETING
.
201
8.2.4
MACHINE
LEARNING
.
201
8.3
ASSUMPTIONS
AND
LIMITATIONS
.
202
8.4
PRACTICAL
IMPLICATIONS
.
203
8.4.1
UTILITIES
CAN
TURN
CHALLENGES
INTO
OPPORTUNITIES
THROUGH
DATA-DRIVEN
INNOVATIONS
.
203
8.4.2
RECOMMENDATIONS
FOR
INTRODUCING
PREDICTIVE
ANALYTICS
IN
FIRMS
.
206
A
SYSTEMATIC
LITERATURE
ANALYSIS
ON
PREDICTIVE
ANALYTICS
209
A.L
DATA
COLLECTION
.
209
A.
2
CONTENT
ANALYSIS
.
210
B
CONDUCTED
CASE
STUDIES
IN
ENERGY
RETAIL
217
C
SURVEY
INSTRUMENTS
219
C.L
ENVIRONMENTAL
ATTITUDE
.
219
C.2
CUSTOMER-BASED
REPUTATION
OF
A
FIRM
.
220
C.3
PURCHASE
INTENTION
.
221
C.4
USABILITY
PERCEPTION
SCALE
FOR
ENERGY
FEEDBACK
.
223
BIBLIOGRAPHY
225
GLOSSARY
249
V |
| any_adam_object | 1 |
| author | Hopf, Konstantin |
| author_GND | (DE-588)1191118517 |
| author_facet | Hopf, Konstantin |
| author_role | aut |
| author_sort | Hopf, Konstantin |
| author_variant | k h kh |
| building | Verbundindex |
| bvnumber | BV046058187 |
| classification_rvk | ST 515 |
| collection | ebook |
| ctrlnum | (OCoLC)1110074170 (DE-599)BVBBV046058187 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV046058187 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T08:01:11Z |
| institution | BVB |
| institution_GND | (DE-588)1068114681 |
| isbn | 9783863096687 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031439558 |
| oclc_num | 1110074170 |
| open_access_boolean | 1 |
| owner | DE-473 DE-BY-UBG DE-22 DE-BY-UBG DE-12 DE-83 |
| owner_facet | DE-473 DE-BY-UBG DE-22 DE-BY-UBG DE-12 DE-83 |
| physical | xxvi, 251 Seiten Illustrationen, Diagramme 24 cm |
| psigel | ebook |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | University of Bamberg Press |
| record_format | marc |
| series | Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg |
| series2 | Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg |
| spelling | Hopf, Konstantin Verfasser (DE-588)1191118517 aut Predictive analytics for energy efficiency and energy retailing Konstantin Hopf Bamberg University of Bamberg Press 2019 xxvi, 251 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg Band 36 Energie (DE-588)4014692-3 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Prognose (DE-588)4047390-9 s Statistik (DE-588)4056995-0 s Energie (DE-588)4014692-3 s DE-604 University of Bamberg Press (DE-588)1068114681 pbl Erscheint auch als Online-Ausgabe urn:nbn:de:bvb:473-opus4-548335 10.20378/irbo-54833 978-3-86309-669-4 Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg Band 36 (DE-604)BV035260948 36 https://fis.uni-bamberg.de/handle/uniba/45617 Verlag kostenfrei Volltext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031439558&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hopf, Konstantin Predictive analytics for energy efficiency and energy retailing Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik der Otto-Friedrich-Universität Bamberg Energie (DE-588)4014692-3 gnd Prognose (DE-588)4047390-9 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4014692-3 (DE-588)4047390-9 (DE-588)4056995-0 (DE-588)4193754-5 |
| title | Predictive analytics for energy efficiency and energy retailing |
| title_auth | Predictive analytics for energy efficiency and energy retailing |
| title_exact_search | Predictive analytics for energy efficiency and energy retailing |
| title_full | Predictive analytics for energy efficiency and energy retailing Konstantin Hopf |
| title_fullStr | Predictive analytics for energy efficiency and energy retailing Konstantin Hopf |
| title_full_unstemmed | Predictive analytics for energy efficiency and energy retailing Konstantin Hopf |
| title_short | Predictive analytics for energy efficiency and energy retailing |
| title_sort | predictive analytics for energy efficiency and energy retailing |
| topic | Energie (DE-588)4014692-3 gnd Prognose (DE-588)4047390-9 gnd Statistik (DE-588)4056995-0 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Energie Prognose Statistik Maschinelles Lernen |
| url | https://fis.uni-bamberg.de/handle/uniba/45617 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031439558&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035260948 |
| work_keys_str_mv | AT hopfkonstantin predictiveanalyticsforenergyefficiencyandenergyretailing AT universityofbambergpress predictiveanalyticsforenergyefficiencyandenergyretailing |