Electrical load forecasting using adaptive neuro-fuzzy inference system:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Abschlussarbeit Buch |
| Sprache: | English |
| Veröffentlicht: |
Clausthal-Zellerfeld
Papierflieger Verlag GmbH
May 2019
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Beschreibung: | XVI, 146 Seiten Illustrationen 21 cm, 280 g |
| ISBN: | 9783869486987 3869486988 |
Internformat
MARC
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| 100 | 1 | |a Hashemifarzad, Ali |d 1985- |e Verfasser |0 (DE-588)1190187477 |4 aut | |
| 245 | 1 | 0 | |a Electrical load forecasting using adaptive neuro-fuzzy inference system |c Ali Hashemifarzad, M. Sc. |
| 264 | 1 | |a Clausthal-Zellerfeld |b Papierflieger Verlag GmbH |c May 2019 | |
| 300 | |a XVI, 146 Seiten |b Illustrationen |c 21 cm, 280 g | ||
| 336 | |b txt |2 rdacontent | ||
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| 338 | |b nc |2 rdacarrier | ||
| 502 | |b Dissertation |c Technische Universität Clausthal |d 2019 | ||
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| 653 | |a Adaptive Neuro-Fuzzy-Infernzsystem | ||
| 653 | |a elektrische Energie | ||
| 653 | |a 1683: Hardcover, Softcover / Technik/Wärmetechnik, Energietechnik, Kraftwerktechnik | ||
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Datensatz im Suchindex
| _version_ | 1804180643431579648 |
|---|---|
| adam_text | CONTENTS
ACKNOWLEDGMENTS
.................................................................................................IV
ABSTRACT
...................................................................................................................
V
KURZFASSUNG
........................................................................................................
VII
LIST
OF
FIGURES
.....................................................................................................
XII
LIST
OF
TABLES
.......................................................................................................
XV
ABBREVIATION
......................................................................................................XVI
I.
CHAPTER
1
........................................................................................................
1
1.
INTRODUCTION
........................................................................................................................................
1
2.
MOTIVATION
.........................................................................................................................................
3
3.
GENERAL
PHILOSOPHY
OF
PREDICTION
....................................................................................................
6
4.
THE
RESEARCH
STRUCTURE
...................................................................................................................
13
II.
CHAPTER
2
.............................................................................
.
.......................
15
1.
CONCEPT
DRIFT
....................................................................................................................................
16
2.
CHAOS
...............................................................................................................................................
18
2.1.
CHAOS
PHENOMENA
IN
TIME
SERIES
.........................................................................................
19
3.
PROVING
THE
EXISTENCE
OF
CHAOS
IN
SAMPLE
DATA
..........................................................................
25
3.1.
EMBEDDING
DIMENSION
..........................................................................................................
25
3.2.
GRASSBERGER-PROCACCIA
CORRELATION
DIMENSION
....................................................................
29
3.3.
LYAPUNOV
EXPONENT
................................................................................................................
31
4.
PROPOSED
ALGORITHM
.......................................................................................................................
33
III.
CHAPTER
3
.....................................................................................................
36
1.
LOAD
PREDICTION
IN
TODAY
*
S
ENERGY
SYSTEMS
..................................................................................
36
2.
LOAD
PREDICTION
METHODS
..............................................................................................................
38
2.1.
CLASSIC
METHODS
......................................................................................................................
39
2.1.1.
REGRESSION
ALGORITHMS
......................................................................................................
39
2.1.2.
CLASSIC
TIME
SERIES
ANALYSIS
.............................................................................................
39
2.1.3.
EXPONENTIAL
SMOOTHING
.....................................................................................................
40
2.1.4.
ADAPTIVE
FILTERING
................................................................................................................
40
2.1.5.
SIMILAR
DAY
LOOKUP
..........................................................................................................
41
2.2.
MODEM
APPROACHES
................................................................................................................
41
2.2.1.
FUZZY
LOGIC
.........................................................................................................................
41
2.2.2.
SUPPORT
VECTOR
MACHINES
(SVM)
......................................................................................
42
2.2.3.
ARTIFICIAL
NEURAL
NETWORKS
(ANN)
....................................................................................
42
2.2.4.
GENETIC
ALGORITHM
(GA)
.....................................................................................................
43
3.
NEURAL
NETWORKS
..............................................................................................................................
44
3.1.
INTRODUCTION
.............................................................................................................................
44
3.2.
ARTIFICIAL
NEURAL
NETWORKS
(ANNS)
......................................................................................
45
3.3.
NEURAL
NETWORKS
STRUCTURE
......................................................................................................
48
3.4.
ANNS
*
TRAINING
PROCESS
.........................................................................................................
52
3.4.1.
BACKPROPAGATION
ALGORITHM
...............................................................................................
53
3.4.2.
PERCEPTRON
MODEL
...............................................................................................................
55
4.
RESULTS
OF
LOAD
PREDICTIONS
BASED
ON
ANNS
................................................................................
57
4.1.
WEEKLY
PREDICTION
RESULTS:
.....................................................................................................
60
4.2.
MONTHLY
PREDICTION
RESULTS:
..................................................................................................
62
4.3.
SEASONAL
PREDICTION
RESULTS:
..................................................................................................
64
5.
NEURAL
NETWORKS
DRAWBACKS
..........................................................................................................
66
IV.
CHAPTER
4
......................................................................................................
67
1.
ADAPTIVE
NEURO-FUZZY
INFERENCE
SYSTEM
(ANFIS)
......................................................................
67
1.1.
ANFIS
STRUCTURE
.....................................................................................................................
68
1.2.
ANFIS
TRAINING
METHODS
.......................................................................................................
71
1.3.
LEAST
SQUARE
ERROR
METHOD
....................................................................................................
71
1.4.
GRADIENT
DESCENT
METHOD
.......................................................................................................
73
1.5.
HYBRID
METHOD
........................................................................................................................
76
2.
RESULTS
OF
LOAD
PREDICTIONS
BASED
ON
ANFIS
...............................................................................
76
2.1.
WEEKLY
PREDICTION
RESULTS:
....................................................................................................
77
2.2.
MONTHLY
PREDICTION
RESULTS:
...................................................................................................
80
2.3.
SEASONAL
PREDICTION
RESULTS:
...................................................................................................
83
3.
COMPARISON
BETWEEN
ANN
AND
ANFIS
........................................................................................
86
V.
CHAPTER
5
......................................................................................................
88
1.
THE
AIM
OF
ENERGY
SYSTEM
MODELLING
..........................................................................................
88
2.
SIMULATION
MODEL
OF
A
DECENTRALIZED
ENERGY
SYSTEM
..................................................................
90
3.
CASE
STUDY
OF
ENERGY
PARK
IN
CUTEC
..........................................................................................
91
3.1.
INTRODUCTION
OF
ENERGY
PARK
CUTEC
....................................................................................
91
3.2.
MATLAB
AND
MATLAB-SIMULINK
.........................
93
3.3.
MODEL
.......................................................................................................................................
96
3.4.
SAMPLE
RESULTS
......................................................................................................................
102
3.4.1.
OVERALL
RESULTS
-
YEAR
2014
.............................................................................................
103
3.4.2.
SAMPLE
COLD
WEEK
/
DAY
................................................................................................
107
3.4.3.
SAMPLE
HOT
WEEK
/
DAY
.................................................................................................
111
3.5.
MODEL
VERIFICATION
.....................................................................................................................
115
VI.
CHAPTER
6
...................................................................................................
117
1.
ENERGY
SYSTEM
STRUCTURE
IN
IRAN
.......................................................................................................
117
2.1.
CURRENT
SITUATION
..................................................................................................................
117
2.1.1.
OIL
......................................................................................................................................
118
2.1.2.
NATURAL
GAS
.......................................................................................................................
118
2.1.3.
COAL
...................................................................................................................................
118
2.1.4.
RENEWABLES
.......................................................................................................................
119
2.1.5.
ELECTRICITY
.........................................................................................................................
120
2.2.
FUTURE
DEVELOPMENT
PLANS
..........................................................................
122
2.3.
TRANSFERABILITY
OF
PROPOSED
MODEL
TO
IRAN
*
S
SYSTEM
.........................................................
123
VII.
CHAPTER
7
...................................................................................................
125
1.
CONCLUSION,
FINAL
REMARKS
..........................................................................................................
125
2.
FUTURE
WORKS
.................................................................................................................................
126
VIII.
REFERENCES
..................................................................................................
128
IX.
APPENDICES
.................................................................................................
134
A.
IMPLEMENTATION
CODES
FOR
LOAD
PREDICTION
................................................................................
134
A.
L.
MUTUAL
INFORMATION
FUNCTION
..................................................................................................
134
A.2.
METHOD
OF
CAO
..........................................................................................................................
135
A.3.
CORRELATION
DIMENSION
.............................................................................................................
136
A.
4.
LYAPUNOV
EXPONENTS
................................................................................................................
139
B.
IMPLEMENTATION
CODES
AND
SIMULINK
MODEL
FOR
ENERGY
PARK
CUTEC
....................................
141
B.
1.
SIMULINK
MODEL
OF
ENERGY
PARK
..............................................................................................
141
C.
SIMULATION
RESULTS
OF
THE
ENERGY
SYSTEM
MODEL
FOR
ENERGY
PARK
CUTEC
IN
YEAR
2014
.......
142
C.
L.
ELECTRICITY
PRODUCTION
OVER
THE
YEAR
.....................................................................................
142
C.2.
HEAT
PRODUCTION
OVER
THE
YEAR
...............................................................................................
143
D.
SELECTED
LIST
OF
PRESENTATIONS
AND
PUBLICATIONS
.........................................................................
144
E.
CURRICULUM
VITAE
.........................................................................................................................
145
|
| any_adam_object | 1 |
| author | Hashemifarzad, Ali 1985- |
| author_GND | (DE-588)1190187477 |
| author_facet | Hashemifarzad, Ali 1985- |
| author_role | aut |
| author_sort | Hashemifarzad, Ali 1985- |
| author_variant | a h ah |
| building | Verbundindex |
| bvnumber | BV046231860 |
| classification_rvk | ZN 8520 ZN 8510 |
| ctrlnum | (OCoLC)1136274905 (DE-599)DNB1191277844 |
| dewey-full | 621.31920285632 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.31920285632 |
| dewey-search | 621.31920285632 |
| dewey-sort | 3621.31920285632 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Mathematik Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Thesis Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV046231860 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T08:39:01Z |
| institution | BVB |
| institution_GND | (DE-588)1064204619 |
| isbn | 9783869486987 3869486988 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031610338 |
| oclc_num | 1136274905 |
| open_access_boolean | |
| owner | DE-83 |
| owner_facet | DE-83 |
| physical | XVI, 146 Seiten Illustrationen 21 cm, 280 g |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Papierflieger Verlag GmbH |
| record_format | marc |
| spelling | Hashemifarzad, Ali 1985- Verfasser (DE-588)1190187477 aut Electrical load forecasting using adaptive neuro-fuzzy inference system Ali Hashemifarzad, M. Sc. Clausthal-Zellerfeld Papierflieger Verlag GmbH May 2019 XVI, 146 Seiten Illustrationen 21 cm, 280 g txt rdacontent n rdamedia nc rdacarrier Dissertation Technische Universität Clausthal 2019 Netzbelastung (DE-588)4194619-4 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Adaptives System (DE-588)4247928-9 gnd rswk-swf Neuro-Fuzzy-System (DE-588)4560677-8 gnd rswk-swf Inferenzsystem (DE-588)4288863-3 gnd rswk-swf Paperback / softback Fachhochschul-/Hochschulausbildung Fachpublikum/ Wissenschaft ANFIS Adaptive Neuro-Fuzzy-Infernzsystem elektrische Energie 1683: Hardcover, Softcover / Technik/Wärmetechnik, Energietechnik, Kraftwerktechnik (DE-588)4113937-9 Hochschulschrift gnd-content Netzbelastung (DE-588)4194619-4 s Prognose (DE-588)4047390-9 s Neuro-Fuzzy-System (DE-588)4560677-8 s Inferenzsystem (DE-588)4288863-3 s Adaptives System (DE-588)4247928-9 s DE-604 Papierflieger Verlag (DE-588)1064204619 pbl B:DE-101 application/pdf https://d-nb.info/1191277844/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031610338&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hashemifarzad, Ali 1985- Electrical load forecasting using adaptive neuro-fuzzy inference system Netzbelastung (DE-588)4194619-4 gnd Prognose (DE-588)4047390-9 gnd Adaptives System (DE-588)4247928-9 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd Inferenzsystem (DE-588)4288863-3 gnd |
| subject_GND | (DE-588)4194619-4 (DE-588)4047390-9 (DE-588)4247928-9 (DE-588)4560677-8 (DE-588)4288863-3 (DE-588)4113937-9 |
| title | Electrical load forecasting using adaptive neuro-fuzzy inference system |
| title_auth | Electrical load forecasting using adaptive neuro-fuzzy inference system |
| title_exact_search | Electrical load forecasting using adaptive neuro-fuzzy inference system |
| title_full | Electrical load forecasting using adaptive neuro-fuzzy inference system Ali Hashemifarzad, M. Sc. |
| title_fullStr | Electrical load forecasting using adaptive neuro-fuzzy inference system Ali Hashemifarzad, M. Sc. |
| title_full_unstemmed | Electrical load forecasting using adaptive neuro-fuzzy inference system Ali Hashemifarzad, M. Sc. |
| title_short | Electrical load forecasting using adaptive neuro-fuzzy inference system |
| title_sort | electrical load forecasting using adaptive neuro fuzzy inference system |
| topic | Netzbelastung (DE-588)4194619-4 gnd Prognose (DE-588)4047390-9 gnd Adaptives System (DE-588)4247928-9 gnd Neuro-Fuzzy-System (DE-588)4560677-8 gnd Inferenzsystem (DE-588)4288863-3 gnd |
| topic_facet | Netzbelastung Prognose Adaptives System Neuro-Fuzzy-System Inferenzsystem Hochschulschrift |
| url | https://d-nb.info/1191277844/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031610338&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT hashemifarzadali electricalloadforecastingusingadaptiveneurofuzzyinferencesystem AT papierfliegerverlag electricalloadforecastingusingadaptiveneurofuzzyinferencesystem |
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