deep learning for energy time series - kit › _media › teaching › winter... · two main...

KIT – The Research University in the Helmholtz Association

ENERGY INFORMATICS SEMINARINSTITUTE FOR AUTOMATION AND APPLIED INFORMATICS (IAI)

www.kit.edu

Deep Learning for Energy Time SeriesKai Schmieder

Energy Informatics SeminarInstitute for Automation and Applied Informatics (IAI)

Motivation

Buildings are major energy consumer worldwide(Pérez-Lombard et al. 2008)

Minimize the energy wastage and making power generation and distribution more efficient by intelligent control decisions(Marino et al. 2016, p. 7046)

Load forecasting crucial for mitigating uncertainties of the future

Kai Schmieder: Deep Learning for Energy Time Series2 08.01.19


Motivation

Deep Learning proved to be useful in manifold fieldsComputer visionSpeech and audio processingNatural language processingRoboticsBioinformatics and chemistryFinance…

(Goodfellow et al. 2016, p.8f)

08.01.19 Kai Schmieder: Deep Learning for Energy Time Series3


Agenda

Foundation

Long Short-Term Memory for Energy Time Series

Evaluation

Discussion

Conclusion



FOUNDATION



Energy Time Series

Aggregate level and building level forecasting categorized byShort-term à one hour to one weekMedium-term à one week to one yearLong-term à ranges longer than one year

(Mocanu et al. 2016, p. 91)

Two main approaches to forecast Energy Time SeriesStatistical and Machine Learning based modelsPhysical Principles based models

(Mocanu et al. 2016, p. 91; Marino et al. 2016, p. 7046)

à here: Short-term & Statistical and Machine Learning based models

08.01.19 Kai Schmieder: Deep Learning for Energy Time Series

Foundation DiscussionLong Short-Term Memory for Energy Time Series Evaluation Conclusion

6


Deep Learning

Central goal of Machine LearningLearn useful representations of input dataThat get us closer to expected output

Deep LearningSpecific subfield of Machine LearningIdea of successive layers of representationsDepth: how many layers contribute to a model of the dataLayered representations (almost always) learned via neural networks

(Chollet 2018, p. 6ff)



Artificial Intelligence

Machine Learning

DeepLearning

Figure 1: Own representation based on Chollet, 2018, p.4


Overview of relevant Deep Architectures

08.01.19

DeepArchitectures

Deep Forward Networks

ConvolutionalNeural Networks

RecurrentNeural Networks

Deep Generative Models

Autoencoders

Mixes ofarchitectures

Kai Schmieder: Deep Learning for Energy Time Series8

Figure 2: Own representation based on Goodfellow et al., 2016, p. 11

e.g. Multi-Layer-Perceptrons

e.g. AlexNet for image classification

e.g. Long Short-Term Memory

e.g. Boltzman Machines


e.g. Convolutional Neural Networks with Recurrent Neural Network

e.g. For Feature Extraction


Recurrent Neural Networks

Family of neural networks for processing sequential dataBasic principle

Each member of the output is a function of the previous member of the outputUnrolled Recursive Neural Network

(Goodfellow et al. 2016, p. 363f)Vanishing gradient problem: challenge to learn long-term dependencies(Hochreiter 1991; Bengio et al. 1993, 1994)


Figure 3: Olah, 2015, http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png, Retrieved on 16.12.18


http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/RNN-unrolled.png


LONG SHORT-TERM MEMORY FOR ENERGY TIME SERIES



Short abstract of Marino et al. (2016)

Application of two architectural variations of Long Short-Term Memory

Stacked Long Short-Term Memory Sequence-to-Sequence Long Short-Term Memory

(Marino et al. 2016)

Experiments on benchmark dataset of electricity consumption for a single residential customer (“Individual household electric power consumption”)(Dheeru and Taniskidou 2017)


Building Energy Load Forecasting using Deep Neural Networks

Daniel L. Marino, Kasun Amarasinghe, Milos Manic

Department of Computer Science

Virginia Commonwealth University

Richmond, Virginia [email protected], [email protected], [email protected]

Abstract—Ensuring sustainability demands more efficient

energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past. However, it has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically, Long Short Term Memory (LSTM) algorithms. The presented work investigates two LSTM based architectures: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures were trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

Keywords—Deep Learning; Deep Neural Networks; Long-Short-Term memory; LSTM; Energy; Building Energy; Energy Load forecasting

I. INTRODUCTION

Buildings are identified as a major energy consumer worldwide, accounting for 20%-40% of the total energy production [1]-[3]. In addition to being a major energy consumer, buildings are shown to account for a significant portion of energy wastage as well [4]. As energy wastage poses a threat to sustainability, making buildings energy efficient is extremely crucial. Therefore, in making building energy consumption more efficient, it is necessary to have accurate predictions of its future energy consumption.

At the grid level, to minimizing the energy wastage and making the power generation and distribution more efficient, the future of the power grid is moving to a new paradigm of smart grids [5], [6]. Smart grids are promising, unprecedented flexibility in energy generation and distribution [7]. In order to provide that flexibility, the power grid has to be able to dynamically adapt to the changes in demand and efficiently distribute the generated energy from the various sources such as renewables [8]. Therefore, intelligent control decisions should

be made continuously at aggregate level as well as modular level in the grid. In achieving that goal and ensuring the reliability of the grid, the ability of forecasting the future demands is important. [6], [9].

Further, demand or load forecasting is crucial for mitigating uncertainties of the future [6]. In that, individual building level demand forecasting is crucial as well as forecasting aggregate loads. In terms of demand response, building level forecasting helps carry out demand response locally since the smart grids incorporate distributed energy generation [6]. The advent of smart meters have made the acquisition of energy consumption data at building and individual site level feasible. Thus data driven and statistical forecasting models are made possible [7].

Aggregate level and building level load forecasting can be viewed in three different categories: 1) Short-term 2) Medium-term and 3) Long-term [6]. It has been determined that the load forecasting is a hard problem and in that, individual building level load forecasting is even harder than aggregate load forecasting [6], [10]. Thus, it has received increased attention from researchers. In literature, two main methods can be found for performing energy load forecasting: 1) Physics principles based models and 2) Statistical and machine learning based models. Focus of the presented work is on the second category of statistical load forecasting. In [7], the authors used Artificial Neural Network (ANN) ensembles to perform the building level load forecasting. ANNs have been explored in detail for the purpose of all three categories of load forecasting [9], [11]-[13]. In [14], the authors use a support vector machines based regression model coupled with empirical mode decomposition to for long-term load forecasting. In [15], electricity demand is forecast using a kernel based multi-task learning methodologies. In [10], authors model individual household electricity loads using sparse coding to perform medium term load forecasting. In the interest of brevity, not all methods in literature are introduced in the paper. For surveys of different techniques used for load forecasting, readers are referred to [16], [17] and [8]. Despite the extensive research carried out in the area, individual site level load forecasting remains to be a difficult problem.

Therefore, the work presented in this paper investigates a deep learning based methodology for performing individual building level load forecasting. Deep learning allows models composed of multiple layers to learn representations in data. The use of multiple layers allow the learning process to be carried out with multiple layers of abstraction. A comprehensive overview and a review of deep learning methodologies can be

978-1-5090-3474-1/16/$31.00 ©2016 IEEE 7046


Figure 4: Marino et al., 2016, p. 7046

Energy Informatics Seminar

Institute for Automation and Applied Informatics (IAI)

Overview of presented Long Short-Term Memory architectures

Stacked

Long Short-Term Memory

Sequence-to-Sequence



Foundation DiscussionLong Short-Term Memory for

Energy Time Series Evaluation Conclusion

Figure 5:

Own representation

based on

Marino et al., 2016,

p. 7047Figure 6:

Own representation




Type of Recurrent Neural Network introduced by Sepp Hochreiter and

Jürgen Schmidhuber (1997)

Great success in various applications (Goodfellow et al. 2016, p. 363f)

Solves vanishing gradient problem

Key concepts

Cell State

Gates

Input

Forget

Output




Figure 7: Olah, 2015,

http://colah.github.io/posts/2015-08-Understanding-

LSTMs/img/LSTM3-chain.png, Retrieved on 03.12.2018

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-chain.png



Long Short-Term Memory Cells


0 Cell State

Legend



Figure 8: Olah, 2015,

http://colah.github.io/posts/2015-08-

Understanding-LSTMs/img/LSTM3-C-

line.png, Retrieved on 03.12.2018

1 Forget Gate

Figure 9: Adapted from Olah, 2015,


Understanding-LSTMs/img/LSTM3-

focus-f.png, Retrieved on 03.12.2018

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-C-line.png

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-f.png




2 Input Gate

Legend


Figure 10: Adapted from Olah, 2015, http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-i.png, Retrieved on 03.12.2018

3 Update Cell State

Figure 11: Adapted from Olah, 2015, http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-C.png, Retrieved on 03.12.2018

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-i.png

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-C.png





Legend

4 Output Gate



Figure 12: Adapted from Olah, 2015,


Understanding-LSTMs/img/LSTM3-

focus-o.png, Retrieved on 03.12.2018

http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-o.png


Stacked Long Short-Term Memory architecture

Stacked architectureStack multiple Long Short-Term Memory Cells into a multi-layer architectureDense layer

Input vector: i[t] = [ y[t-1] day[t] day-of-week[t] hour[t] ]Use also more than one time step as “context”

Output vector:




f[t]

Figure 5: Own representation based on Marino et al., 2016, p. 7047

y[t]^



Overview of presented Long Short-Term Memory architectures

Stacked


Sequence-to-Sequence





Figure 5:

Own representation

based on

Marino et al., 2016,

p. 7047

Slide from before

Figure 6:

Own representation


The idea of Sequence-to-Sequence Learning

Introduced by Sutskever et al. (2014) to map sequences of different lengths

Comprised of two sub-modelsEncoder

Convert input sequences of variable length andEncode them in a fixed length vector, which is then used as input state for the decoder

DecoderGenerates an output sequence of fixed length

(Marino et al. 2016, p. 7048)

Applications in speech recognition, machine translation and question answering (Goodfellow et al. 2016, p. 385)




Sequence-to-Sequence Long Short-Term Memory architecture

EncoderInput vector: i[t] = [ y[t-1] day[t] day-of-week[t] hour[t] ]

“Output”: Cell State

DecoderInput vector: f[t]

Output vector:




Figure 6: Own representation

f[t]

y[t]^


EVALUATION

Kai Schmieder: Deep Learning for Energy Time Series21 09.01.1908.01.19



Horizon

Data

Experiments in Marino et al. (2016)

“Individual household electric power consumption” (Dheeru and Taniskidou 2017)

Aggregation Level

1 minute (original)

1 hour

Training set: 3 years; Test set: 1 year


One Step

60 Steps

Evaluation and results

Model


Energy Time SeriesEvaluation Conclusion

Stacked LSTM

Sequence-to-Sequence LSTM

Stacked LSTM performs well with inputs further in the past for hourly data

Sequence-to-Sequence LSTM performs well in both datasets

Comparable results to Mocanu et al. (2016)

08.01.19


Implementation and hardware resources

Jupyter Notebooks at https://github.com/nicoleludwig/EnergyInformatics

Implementation with PythonPandas NumPy scikit-learnKeras with TensorFlow as backendChartify

Hardware resources for trainingGPU: NVIDIA Tesla V100*



09.01.19

* Made possible by Andreas Bartschat (IAI), thanks again! J

08.01.19

https://github.com/nicoleludwig/EnergyInformatics


Data for evaluation

Energy consumption data of KIT Campus NordBuilding 12415 minutes resolutionTraining set of three years(Mid May 2012 until Mid May 2015)Test set of one year(Mid May 2015 until Mid May 2016)



09.01.19

36

36

L559

L559

Einfahrt Nord

100m

1600

906

L559

L559

Karlsruher Allee

Grabener Straße

Eggensteiner Straße

Liedolsheimer Straße

eßartS remiehnekniL

eßartS rettetshcoH

ztalP-ztlohmle

H-nov-nnamre

H

eellA renefahsdlopoeL

eßartS relatshcirdeirF

.rtS rehcolneknalB

Spöcker Straße

Weingartener Straße


Unt

ergr

omba

cher

Str

aße

Blankenlocher Straße

Blan

kenl

oche

r St

raße

meteorologischerMessmast

Büchenauer StraßeBüchenauer Straße

Neureuter Straße


Karlsruher Allee


Spöcker Straße

Grabener Straße


CASINO

Werkfeuerwehr

KIT-Shuttle

Anmeldung

trha

fnie

tpua

H

Lieferzufahrt

H

Administration

Bibliothek

FTU

KITA

H

Einfahrt Nord

KIT-Shuttle

Anmeldung ITU

Anmeldung WAK

FIZ

Anmeldung WAK

Eltern-Kind-Büro

FIZ

FIZ

FIZ

FIZ

H2

Strahlenpassstelle KIT

211

212

210

213

232

231

226224222

227225

223

223/1

221

121122

123

101103

5131

131

126

124

142

144

141241

240

230

238239

244

242

249

248

245

243

220 2505252

256

257

23723

5

251

261

260

260/1

252

234

253

254

266

258

259

255

278

272

274273276

400

440

412413

415

420

414

410

418 419409

406

411

408

405

404

5404

403

407430

5402

401A

402

401

301

300

309

307

310

302

308303

305

304

317

312

306

316

152

151

311

330

332

333 315

324

329

327

326

322

321

421

422

429

428

423

424

9627

434 426

425

443

447436

433

432 444

438 446

448

435439

459

454

458

453

456

464

461

462

460

4529652

9653450

451

445442441

441/1

341

343

345

3499642

348

352

351

353

517 500518

511

510

513

512515

514

516

527

573

574575

549

544

541542

522523

521

5523

605 606

601 602

603

607

640

611

615

604

631633

629 620

5621

608

618

650614

690

663

660

670

692

691693 689

688

696

695

682

684/1684

684/2

686

708

704

706707

713 719

714

712

718

9670

733

724

723

726

736

732

721

725

727

730

729

229

323

681

441

1600

720

710 717

449

9651

264

737

457

9615

247

701

630

908

746

747

356

9602

9675

9683

612

687

9900

145

416

127

906

705

Richtung B36, Leopoldshafen und Campus Süd

Richtung Stutensee/Blankenloch

S1/ S11 von/nach

Karlsruhe

KIT-Campus NordLieferanschrift: Hermann-von-Helmholtz-Platz 176344 Eggenstein-Leopoldshafen

Richtung Stutensee/FriedrichstalRichtung B36, Linkenheim-Hochstetten

Web

erstr

aße

Beet

hofe

nstr

aße

Mozartstraße

Haydnplatz

Moltkestraße

Maximilianstraße

Nördliche Hildapromenade

100m

Nöthnitzer Str.

Hallwachsstraße

Helm

ho

ltzstraße

Zeuner Str.

Dül

fers

traß

e

Wei

ßbac

hstr

aße

Mommsenstraße

100m

Kre

uzec

kbah

nstr

aße

100m

3101

StandortOstendorfhausLieferanschrift:Weberstraße 576133 Karlsruhe

Standort DresdenProjektträger Karlsruhe – Außenstelle Dresden

Lieferanschrift:Hallwachsstraße 301069 Dresden

KIT-Campus AlpinInstitut für Meteorologie und Klimaforschung (IMK-IFU)

Lieferanschrift:Kreuzeckbahnstraße 1982467 Garmisch-Partenkirchen

Richtung Garmisch-Partenkirchen und B23

RichtungKreuzeck- / Alpspitzbach

906

36

L559

L559

Karlsruher Allee

Grabener Straße


Liedolsheimer Straße


eßartS rettetshcoH

ztalP-ztlohmle

H-nov-nnamre

H


eßartS relatshcirdeirF

.rtS rehcolneknalB

Spöcker Straße



Unt

ergr

omba

cher

Str

aße

Blankenlocher Straße

Blan

kenl

oche

r St

raße

meteorologischerMessmast

Büchenauer StraßeBüchenauer Straße

Neureuter Straße


Karlsruher Allee


Spöcker Straße

Grabener Straße


CASINO

Werkfeuerwehr

KIT-Shuttle

Anmeldung

trha

fnie

tpua

H

Lieferzufahrt

H

Administration

Bibliothek

FTU

KITA

H

Einfahrt Nord

KIT-Shuttle

Anmeldung ITU

Anmeldung WAK

FIZ

Anmeldung WAK

Eltern-Kind-Büro

FIZ

FIZ

FIZ

FIZ

H2

Strahlenpassstelle KIT

211

212

210

213

232

231

226224222

227225

223

223/1

221

121122

123

101103

5131

131

126

124

142

144

141241

240

230

238239

244

242

249

248

245

243

220 2505252

256

257

23723

5

251

261

260

260/1

252

234

253

254

266

258

259

255

278

272

274273276

400

440

412413

415

420

414

410

418 419409

406

411

408

405

404

5404

403

407430

5402

401A

402

401

301

300

309

307

310

302

308303

305

304

317

312

306

316

152

151

311

330

332

333 315

324

329

327

326

322

321

421

422

429

428

423

424

9627

434 426

425

443

447436

433

432 444

438 446

448

435439

459

454

458

453

456

464

461

462

460

4529652

9653450

451

445442441

441/1

341

343

345

3499642

348

352

351

353

517 500518

511

510

513

512515

514

516

527

573

574575

549

544

541542

522523

521

5523

605 606

601 602

603

607

640

611

615

604

631633

629 620

5621

608

618

650614

690

663

660

670

692

691693 689

688

696

695

682

684/1684

684/2

686

708

705704

706707

713

719

714

712

718

9670

733

724

723

726

736

732

721

725

727

730

729

229

323

681

441

1600

720

710 717

449

9651

264

737

457

9615

247

701

630

908

746

747

356

9602

9675

9683

612

687

9950

145

416

127

906

Richtung B36, Leopoldshafen und Campus Süd

Richtung Stutensee/Blankenloch

S1/ S11 von/nach

Karlsruhe

KIT-Campus NordLieferanschrift: Hermann-von-Helmholtz-Platz 176344 Eggenstein-Leopoldshafen

Richtung Stutensee/FriedrichstalRichtung B36, Linkenheim-Hochstetten

Web

erstr

aße

Beet

hofe

nstr

aße

Mozartstraße

Haydnplatz

Moltkestraße

Maximilianstraße

Nördliche Hildapromenade

Nöthnitzer Str.

Hallwachsstraße

eßar

tszt

loh

mle

H

Zeuner Str.

Dül

fers

traß

e

Mommsenstraße

100m

Kre

uzec

kbah

nstr

aße

100m

3101

StandortOstendorfhausLieferanschrift:Weberstraße 576133 Karlsruhe

Standort DresdenProjektträger Karlsruhe – Außenstelle Dresden

Lieferanschrift:Hallwachsstraße 301069 Dresden

KIT-Campus AlpinInstitut für Meteorologie und Klimaforschung (IMK-IFU)

Lieferanschrift:Kreuzeckbahnstraße 1982467 Garmisch-Partenkirchen

Richtung Garmisch-Partenkirchen und B23

RichtungKreuzeck- / Alpspitzbach

HerausgeberKarlsruher Institut für TechnologieKaiserstraße 12, 76131 Karlsruhe

Kontakt

Facility Management (FM)Bauprojekte und Immobilien – Immobilienmanagement (BPI-IMM)[email protected]

Stand:Mai 2016

Karl-Wilhelm-Platz4, 5

H

H

H

H

H

Engesserstraße

Richard-Willstätter-Allee

Engl

er-B

unte

-Rin

g

Hagsfelder AlleeWaldparkplatz

Gotthard-Franz-Straße

Schönfeldstraße

Karl-Wilhelm-Stra

ße

Haid-und-Neu-Straße

EdelsheimstraßeAm Fasanengarten

Parkstraße

eßar

tS-z

tinß

eirP

-zne

cni

V

Ade

naue

rrin

g

Leonhard-Sohncke-Weg

Ernst-Gaber-Str.

Engelbert-Arnold-Straße

Wilhelm-Nußelt-Weg

Rudo

lf-P

lank

-Str

.

Frit

z-H

aber

-Weg

Wol

fgan

g-G

aede

-Str

aße

Iren

e.-R

osen

berg

.-Str

.

R.-Baumeister-Platz

Otto-Ammann-PlatzEhrenhof

Paulcke-Platz

W.-

Jord

an

-Weg

Stra

ße a

m F

orum

Haupteinfahrt

Nordeinfahrtmit Codekarte

Einfahrt

Einfahrt

Einf

ahrt

10.50

Studentenwerk

StudentenzentrumKIT-Shuttle

Forstschule

Sportanlagen

10.30

10.7

0

FORUM

MENSA

AUDIMAX

SPORT

BIBLIOTHEK

30.21

11.20

11.1

1

11.10

11.30

10.9

1

10.92 10.93 10.94

10.9

5

10.96

11.2

3

11.21

11.2

2

10.81

10.82

10.83 10.84 10.85

10.86 10.87

10.8

8 10.89

10.6

110

.64

10.6310.62

02.17

02.12

02.1

3

02.1

402.1

5

02.16

02.18

30.91

30.9

3

30.90

30.80

30.4

1

30.4

2

30.43

30.44

30.45

30.4

6

30.31

30.3

2

30.33

30.3

430.35

30.36

30.81 30.8230.51

30.5

0

01.12

01.1

3

30.60 30.6130.95

30.9

6

30.70

30.79

30.29

40.11

40.12

40.13

40.1

4

40.15

40.16

40.17 40.18

40.1

9

40.0

2

40.0

4

40.21 40.22 40.2

340

.24

40.2

5

40.2

6

40.27

40.28

40.29

40.3

140

.32

40.33

40.40

40.43

50.20

50.2150.22

50.2350.2450.25

50.1050.12

50.31

50.32

07.21 07.30

50.33

50.34

50.35

50.3650.38

50.4

0

50.41

7

51

13

11b

11a

4

15

42

2-6

68

17

7

31

8

6

4

2

7

1 1a

2

3

4

651

7

9 3

22

2

1

4

6 8 102

7

2018

5

5a

2

8

19

4

2

5

5

32

7

9

109

8

7

20

20b

71

3

59b

9a11

a 11c

11a

11b

1412

24

8

15

23

22

21

3

20a StandortsucheService-Telefon +49 721 608-100008:00 – 18:00 Uhr

Campuspläne als App www.pkm.kit.edu/navigator

Anmeldung / Information .......................................................................................................................... 221 ......B 09Casino ....................................................................................................................................................... 145 ......B 09Dienstleistungsrotunde .............................................................................................................................. 210 ......B 09

Fortbildungszentrum für Technik und Umwelt (FTU) ................................................................................. 101 ......B 09KiTa „Nanos“ ........................................................................................................................................... 213 ..... C 09KIT-Bibliothek Campus Nord ....................................................................................................................... 303 ......B 08 Ostendorfhaus .......................................................................................................................................... 191 ..... D 01

Administration:

Administration ........................................................................................................................................... 141 ......B 09Medizinische Abteilung ............................................................................................................................ 124 ......B 09Sicherheitsmanagement ........................................................................................................................... 439 ..... D 08Strahlenpassstelle des KIT Campus Nord .................................................................................................... 436 ..... C 08Werkfeuerwehr .......................................................................................................................................... 315 ..... A 08

Projektträger Karlsruhe (PTKA):

PTKA – Baden-Württemberg Programme (BWP) .......................................................................................... 438 ..C/D 08PTKA – Produktion und Fertigungstechnologien (PFT) ................................................................................. 436 ..... C 08PTKA – Standort Dresden ....................................................................................................................................... D 01PTKA – Wassertechnologie und Entsorgung (WTE) ................................................................................... 9675 ..C/D 08

Institute:

Angewandte Informatik (IAI) ...................................................................................................................... 449 ..... C 07Angewandte Materialien – Angewandte Werkstoffphysik (IAM-AWP) ........................................................ 681 C 05/06Angewandte Materialien – Keramische Werkstoffe und Technologien (IAM-KWT) ...................................... 575 ..... A 06Angewandte Materialien – Werkstoff- und Biomechanik (IAM-WBM) ......................................................... 696 ..... C 06Angewandte Materialien – Werkstoffkunde (IAM-WK) ............................................................................... 695 ..... C 06Angewandte Materialien – Energiespeichersysteme (IAM-ESS) ................................................................. 441/1 .. B/C 07Biologische Grenzfl ächen 1 – Biomolekulare Micro- und Nanostrukturen (IBG 1) ........................................ 601 ..... C 07Biologische Grenzfl ächen 2 – Molekulare Biophysik (IBG 2) ....................................................................... 352 ..... A 07Biologische Grenzfl ächen 3 – Makromolekulare Materialien (IBG 3) ............................................................ 601 ..... C 07Biologische Grenzfl ächen 4 – Magnetische Resonanz (IBG 4) ..................................................................... 351 ..... A 07Biologische Grenzfl ächen 5 – Biotechnologie und Mikrobielle Genetik (IBG 5) ........................................... 601 ..... C 07Beschleunigerphysik und Technologie (IBPT) ............................................................................................... 329 . B 07/08Experimentelle Kernphysik (IEKP) ................................................................................................................ 401 .. B/C 08Funktionelle Grenzfl ächen (IFG) .................................................................................................................. 330 ..... A 08Festkörperphysik (IFP) ................................................................................................................................. 425 ..... C 08Hochleistungsimpuls- und Mikrowellentechnik (IHM) ................................................................................. 421 .. B/C 08Katalyseforschung und -technologie (IKFT) ................................................................................................. 727 ......B 05Kernphysik (IKP) ......................................................................................................................................... 401 .. B/C 08Meteorologie und Klimaforschung – Atmosphärische Spurengase und Fernerkundung (IMK-ASF) ............... 435 ..... D 08Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU) ................... Campus Alpin ..... D 02Meteorologie und Klimaforschung – Atmosphärische Aerosol Forschung (IMK-AAF) ................................... 326 ......B 08Meteorologie und Klimaforschung – Forschungsbereich Troposphäre (IMK-TRO) ........................................ 435 ..... D 08Mikrostrukturtechnik (IMT) ......................................................................................................................... 301 ......B 08Mikroverfahrenstechnik (IMVT) .................................................................................................................. 605 .. B/C 07Nukleare Entsorgung (INE) ......................................................................................................................... 714 ..... A 05Neutronenphysik und Reaktortechnik (INR) ................................................................................................ 521 ......B 06Nanotechnologie (INT) ............................................................................................................................... 640 .C 06/07Prozessdatenverarbeitung und Elektronik (IPE) ............................................................................................ 242 ..... C 09Photon Science and Synchrotron Radiation (IPS) ......................................................................................... 329 . B 07/08Technikfolgenabschätzung und Systemanalyse (ITAS) ............................................................................... 3300 ......K‘heTechnische Chemie (ITC) ............................................................................................................................ 403 ..... C 08Technische Physik (ITEP) .............................................................................................................................. 418 ..... C 08Tritiumlabor Karlsruhe (ITEP-TLK) ................................................................................................................ 451 ..... C 07Toxikologie und Genetik (ITG) .................................................................................................................... 305 ......B 08Laboratorium für Applikationen der Synchrotronstrahlung (LAS)................................................................. 329 . B 07/08Steinbuch Centre for Computing (SCC) ...................................................................................................... 449 ..... C 07

Helmholtz-Programme:

Atmosphäre und Klima (ATMO) ................................................................................................................. 435 ..... D 08BioInterfaces in Technology and Medicine (BIFTM) ...................................................................................... 440 ..... D 08Energieeffizienz, Materialien und Ressourcen (EMR) ................................................................................... 435 ..... D 08Erneuerbare Energien (EE) ......................................................................................................................... 435 ..... D 08Kernfusion (FUSION) .................................................................................................................................. 451 ..... C 07Materie und Technologie (MT) .................................................................................................................... 242 ..... C 09Materie und Universum (MU) .................................................................................................................... .. B/C 08Nukleare Entsorgung und Sicherheit sowie Strahlenforschung (NUSAFE) .................................................... 433 ..... C 08Science and Technology of Nanosystems (STN) ........................................................................................... 440 ..... D 08Speicher und vernetzte Infrastrukturen (SCI) ............................................................................................... 418 ..... C 08Supercomputing & Big Data (SBD) .............................................................................................................. 449 ..... C 07Technologie, Innovation und Gesellschaft (TIG) ......................................................................................... 3300 ..... K‘heVon Materie zu Materialien und Leben (MML) ........................................................................................... .....B 7/8

Warnhinweise

Interne Notrufnummer 3333

Auf dem Campus gilt die Straßenverkehrsordnung.

Höchstgeschwindigkeit 50 km/h.

Vorfahrtsregel „rechts vor links“ beachten.

Sicherheitsbereiche dürfen nur unter Beachtung örtlicher Regularien betreten werden.

Zutritt ist tageszeitlich begrenzt.

Karlsruher SchlossKarlsruher SchlossKarlsruher Schloss


Kronenplatz

Marktplatz2, 3, 5, S1, S11, S4, S41, S5

Marktplatz1, 2, 4, 5, S2, S4, S41, S5

1, 2, 4, 5, S2, S4, S41, S5

H

H

Durlacher Tor /KIT Campus-Süd1, 2, 4, 5, S2, S4, S41, S5

01.85

01.90

01.9201.93

01.94

Waldparkplatz

Ernst-Gaber-Str.

Paulcke-Platz

W.-J

ord

an-W

eg

Engesserstraße

ZähringerstraßeKreuzstraße

Schlossplatz

Zirkel

Kronenstraße

Waldhornstraße

Fritz-Erler-Straße

Waldhornstraße

Berliner Platz

Kaiserstraße

Kapell

enstr

aße

Durlacher Allee

R.-Baumeister-Platz

Otto-Ammann-PlatzRu

dolf

-Pla

nk-S

traß

e

Wilhelm-Nusselt-WegEhrenhof


Iren

e-Ro

senb

erg-

Stra

ße

Engesserstraße

Englerstraße

Lehm

anns

traß

e Wol

fgan

g-G

aede

-Str

aße


Neu

er Z

irkel

geW-reba

H-ztirF

Schlossbezirk

Simon-M

oser-Weg


muroF ma eßartS

Engl

er-B

unte

-Rin

g

gnirreuanedA

Hagsfelder Allee

Am Fasanengarten

Gotthard-Franz-StraßeKornblumenstraße

Fraunhoferstraße

eßar

tS-z

tinß

eirP

-zne

cni

V

Edelsheimstraße

Parkstraße

Schönfeldstraße


Karl-Wilhelm-Stra

ße

Lärchenallee

Wes

tein

fahrt

mit

Codekar

te

Haupteinfahrt


Einfahrt

Einfahrt

trhafniE

10.50

Studierendenwerk


Administration

Studierendenservice

Steinbuch Centre forComputing (SCC)

BauamtGastdozentenhaus

Forstliches Bildungs-zentrum Karlsruhe

Sportanlagen

10.40

10.30

10.7

0

FORUM

MENSA

Audimax

SPORT

10.31

10.32 10.34

10.33

KIT-BIBLIOTHEK

01.5101.52

30.12 30.11

30.10

20.1120.12

20.13

20.14

01.53

20.20

20.21

20.50 20.51 20.52

02.1

1

02.1

0

20.54

20.5302.02 02.03 02.04

20.30

02.95

30.21

30.22

30.2

3

30.24

30.2

5

20.4011.20

11.1

1

11.10

11.30

11.4

010

.1201

.80

10.11

10.9

1

10.92 10.93 10.94

10.9

5

10.96

10.21

01.95

10.23

11.2

3

11.21

11.2

2

10.81

10.82

10.83 10.84 10.85

10.86 10.87

10.8

8 10.89

10.6

110

.64

10.6310.62

02.17

02.12

02.1

3

02.1

402.1

5

02.16

02.18

30.91

30.9

3

30.90

30.41

30.42

30.43

30.44

30.45

30.31

30.33

30.3

430.35

30.36

30.81 30.8230.51

30.5

0

01.12

01.1

3

30.60 30.6130.95

30.9

6

30.70

30.79

30.29

40.12

40.13

40.1

4

40.15

40.16

40.17 40.18

40.1

9

40.0

2

40.0

4

40.21 40.22 40.2

340

.24

40.2

5

40.2

6

40.27

40.28

40.29

40.3

140

.32

40.33

40.40

40.44

40.41

40.43

50.20

50.2150.22

50.2650.25

50.1050.12

50.3150.32

07.21

50.33

50.34

50.35

50.3650.38

50.4

0

50.41

01.70

05.01 05.20

01.96

07.0707.08

07.30

07.31

07.01

Adolf-Würth-Gebäude

30.28

PRÄSIDIUM

11.30

10.9

1

10.9610.96

11.30

10.9

1

10.9610.96

KinderUniversum

Studienberatung

30.46

30.3

2

11

12

13

14

19

1

2a

2

2

3

5

5a 5b

24

6

13

2

7

7

9

7

51

13

11b

11a

4

15

42

2-6

68

17

31

8

64

2

7

1 1a

2

3

4

651

7

9

8a4a

8 422a 2b

3

22

1042

1

4

6 8 10

12

2

13

14

11

7

2018

5

5a

2

8

1

6

4

2

5

5

3

13

2

7

9

109

8

7

65

11

71

3

59b

9a11

a 11c

11a

11b

14122

4

8

15

23

2221

3

89-93

1-3

27

3032

34

53

3

6

14

13

31

1a

13

30.49

30.48

KIT-Campus Süd

Lieferanschrift:Kaiserstraße 1276131 Karlsruhe

Eggenstein-Leopoldshafen

Karlsruhe A5Durlacher Allee

Moltkestr.

Basel/Stuttgart

Ausfahrt Karlsruhe Durlach

Frankfurt

Karlsruhe

Graben-Neudorf

36

L558

L559

Adenauerring

KIT-Campus Nord

Ostendorfhaus

KIT-Campus West

KIT-Campus Ost

KIT-Campus Süd

KIT-StandorteKarlsruhe

LAGEPLAN CAMPUS NORD

KIT-StandorteDeutschland

Karlsruhe

Dresden

Garmisch

Ulm

Black Forest Observatory

Helmholtz-Institut Ulm (HIU)Lieferanschrift:Helmholtzstraße 1189081 Ulm

Richtung Ulm-West und Stuttgart (A8)

B10/A8

B10

Richtung Neu-Ulm/Wiblingen/Biberach

Helmholtzstraße

Berliner Ring

Albert-Einstein-Allee

Blaubeuren

B10

Richtung Ulm-Donautal

Ulm Eselsberg

1

2

3

4

5

6

7

8

9

10

A B C D

D

A B C D

1

2

1

2

3

4

5

6

7

8

9

10

3

KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft

100 mN

Neubaumaßnahmen

Fremdinstitutionen

Zaunanlage/Arealbegrenzung Fremdinstitution

4

401

329

Eltern-Kind-Büro .............................................................................................................................. 226 C 09 .....

HerausgeberKarlsruher Institut für TechnologieKaiserstraße 12, 76131 Karlsruhe

Kontakt

Facility Management (FM)Bauprojekte und Immobilien – Immobilienmanagement (BPI-IMM)[email protected]

Stand:Juli 2016

Karlsruher Schloss

Durlacher Tor4, 5, S2


Durlacher Tor1, 2, S4, S5

Kronenplatz

Marktplatz2, 5, S1, S11, S4, S41

Marktplatz1, 2, 3, 4, 5, S1, S11, S2, S4, S41, S5

1, 3, 4, 5, S2, S4, S5

3

H

H

H

H

H

H

Kaiserstraße

Durlacher Allee

Engesserstraße

Schlossbezirk

Engesserstraße


Eng

ler-

Bu

nte

-Rin

g

Hagsfelder AlleeWaldparkplatz

Gotthard-Franz-Straße

Schönfeldstraße

Karl-Wilhelm-Stra

ße


EdelsheimstraßeAm Fasanengarten

Parkstraße

Vin

cenz-Prieß

nitz-Straß

e

Ad

enau

erri

ng


Ernst-Gaber-Str.


Wilhelm-Nußelt-Weg

Ru

do

lf-P

lan

k-St

r.

Frit

z-H

aber

-Weg

Wo

lfg

ang

-Gae

de-

Stra

ße

Iren

e.-R

ose

nb

erg

.-St

r.

Waldhornstraße

Kronenstraße

Fritz-Erler-Straße

Waldhornstraße

Schlossplatz

Zirkel

Englerstraße

Simon-M

oser-Weg

Lehm

anns

traß

e

Neu

er Z

irkel

R.-Baumeister-Platz

Otto-Ammann-Platz

Berliner Platz

Zähringerstraße

Kreuzstraße

Ehrenhof

Paulcke-Platz

Kapell

enstr

aße

W.-

Jord

an

-Weg

Moltkestraße

Stra

ße

am F

oru

m

Wes

tein

fahrt

mit

Codekar

te

Haupteinfahrt


Einfahrt

Einfahrt

Ein

fah

rt

10.50

Studentenwerk


Administration

Studentensekretariat Prüfungsamt

Rechenzentrum


Forstschule

Sportanlagen

10.40

10.30

10.70

FORUM

MENSA

AUDIMAX

SPORT

10.31

10.32 10.34

10.33

BIBLIOTHEK

01.5101.52

30.12 30.11

30.10

20.1120.12

20.13

20.14

01.53

20.20

20.21

20.50 20.51 20.52

20.53

02.11

02.10

02.02 02.03 02.04

20.30

02.95

30.21

30.22

30.23

30.24

30.25

20.40

11.20

11.11

11.10

11.30

11.40

10.12

01.80

10.11

10.91

10.92 10.93 10.94

10.95

10.96

10.2110.23

11.23

11.21

11.22

10.81

10.82

10.83 10.84 10.85

10.86 10.87

10.88 10.89

10.61

10.64

10.63

10.62

02.17

02.12

02.13

02.1402.15

02.16

02.18

30.91

30.93

30.90

30.80

30.41

30.42

30.43

30.44

30.45

30.46

30.31

30.32

30.33

30.3430.35

30.36

30.81 30.82

30.51

30.50

01.12

01.13

30.60 30.6130.95

30.96

30.70

30.79

30.29

40.11

40.12

40.13

40.14

40.15

40.16

40.17 40.18

40.19

40.02

40.04

40.21 40.22 40.23

40.24

40.25

40.26

40.27

40.28

40.29

40.31

40.32

40.33

40.40

40.43

50.20

50.21

50.22

50.23

50.2450.25

50.1050.12

50.31

50.32

07.21 07.30

50.33

50.34

50.35

50.3650.38

50.40

50.41

01.70

05.01 05.20

01.96

11

12

13

14

19

1

2a

2

2

3

5

5a 5b

24

6

13

2

7

7

9

3

11a

7

51

13

11b

11a

4

15

42

2-6

68

17

7

31

8

6

4

2

7

1 1a

2

3

4

651

7

9

9a9b

8 422a 2b

3

22

1042

1

4

6 8 10

12

2

13

14

11

7

2018

5

5a

2

8

19

4

2

5

5

32

7

9

109

8

7

20

20b

65

11

71

3

59b

9a

11a 11c

11a

11b

14

12

24

8

15

23

22

21

3

89-9

3

27

20a

40.1140.11

40.1240.1240.1240.1240.1240.12

40.1340.13

40.14

40.19

40.02

40.02

40.02

40.21

40.2740.27

771

3

5

24

StandortsucheService-Telefon +49 721 608-100008:00 – 18:00 Uhr

Campuspläne als App www.pkm.kit.edu/navigator

Anmeldung / Information ............................................................................................................................ 221 ......B 09Casino ....................................................................................................................................................... 145 ......B 09Dienstleistungsrotunde .............................................................................................................................. 210 ......B 09Eltern-Kind-Büro ........................................................................................................................................ 226 .... C 09Fortbildungszentrum für Technik und Umwelt (FTU) ................................................................................... 101 ......B 09KiTa „Nanos“ ........................................................................................................................................... 213 ..... C 09KIT-Bibliothek Campus Nord ....................................................................................................................... 303 ......B 08 Ostendorfhaus .......................................................................................................................................... 191 ..... D 01

Administration:

Administration ........................................................................................................................................... 141 ......B 09Medizinische Abteilung ............................................................................................................................ 124 ......B 09Sicherheitsmanagement ........................................................................................................................... 439 ..... D 08Strahlenpassstelle des KIT Campus Nord .................................................................................................... 436 ..... C 08Werkfeuerwehr .......................................................................................................................................... 315 ..... A 08

Projektträger Karlsruhe (PTKA):

PTKA – Baden-Württemberg Programme (BWP .......................................................................................... 438 ..C/D 08PTKA – Produktion und Fertigungstechnologien (PFT) ................................................................................. 436 ..... C 08PTKA – Standort Dresden ....................................................................................................................................... D 01PTKA – Wassertechnologie und Entsorgung (WTE) ................................................................................... 9675 ..C/D 08

Institute:

Angewandte Informatik (IAI) ...................................................................................................................... 449 ..... C 07Angewandte Materialien – Angewandte Werkstoffphysik (IAM-AWP) ........................................................ 681 C 05/06Angewandte Materialien – Keramische Werkstoffe und Technologien (IAM-KWT) ...................................... 575 ..... A 06Angewandte Materialien – Werkstoff- und Biomechanik (IAM-WBM) ......................................................... 696 ..... C 06Angewandte Materialien – Werkstoffkunde (IAM-WK) ............................................................................... 695 ..... C 06Angewandte Materialien – Energiespeichersysteme (IAM-ESS) ................................................................. 441/1 .. B/C 07Biologische Grenzfl ächen 1 – Biomolekulare Micro- und Nanostrukturen (IBG 1) ........................................ 601 ..... C 07Biologische Grenzfl ächen 2 – Molekulare Biophysik (IBG 2 ) ....................................................................... 352 ..... A 07Biologische Grenzfl ächen 3 – Makromolekulare Materialien (IBG 3) ............................................................ 601 ..... C 07Biologische Grenzfl ächen 4 – Magnetische Resonanz (IBG 4) ..................................................................... 351 ..... A 07Biologische Grenzfl ächen 5 – Biotechnologie und Mikrobielle Genetik (IBG 5 ) ........................................... 601 ..... C 07Beschleunigerphysik und Technologie (IBPT) ............................................................................................... 329 . B 07/08Experimentelle Kernphysik (IEKP) ................................................................................................................ 401 .. B/C 08Funktionelle Grenzfl ächen (IFG) .................................................................................................................. 330 ..... A 08Festkörperphysik (IFP) ................................................................................................................................. 425 ..... C 08Hochleistungsimpuls- und Mikrowellentechnik (IHM) ................................................................................. 421 .. B/C 08Katalyseforschung und -technologie (IKFT) ................................................................................................. 727 ......B 05Kernphysik (IKP) ......................................................................................................................................... 401 .. B/C 08Meteorologie und Klimaforschung - Atmosphärische Spurengase und Fernerkundung (IMK-ASF) ............... 435 ..... D 08Meteorologie und Klimaforschung – Atmosphärische Umweltforschung (IMK-IFU) ................... Campus Alpin ..... D 02Meteorologie und Klimaforschung – Atmosphärische Aerosol Forschung (IMK-AAF) ................................... 326 ......B 08Meteorologie und Klimaforschung – Forschungsbereich Troposphäre (IMK-TRO) ........................................ 435 ..... D 08Mikrostrukturtechnik (IMT) ......................................................................................................................... 301 ......B 08Mikroverfahrenstechnik (IMVT) .................................................................................................................. 605 .. B/C 07Nukleare Entsorgung (INE) ......................................................................................................................... 714 ..... A 05Neutronenphysik und Reaktortechnik (INR) ................................................................................................ 521 ......B 06Nanotechnologie (INT) ............................................................................................................................... 640 .C 06/07Prozessdatenverarbeitung und Elektronik (IPE) ............................................................................................ 242 ..... C 09Photon Science and Synchrotron Radiation (IPS) ......................................................................................... 329 . B 07/08Technikfolgenabschätzung und Systemanalyse (ITAS) ............................................................................... 3300 ......K‘heTechnische Chemie (ITC) ............................................................................................................................ 403 ..... C 08Technische Physik (ITEP) .............................................................................................................................. 418 ..... C 08Tritiumlabor Karlsruhe (ITEP-TLK) ................................................................................................................ 451 ..... C 07Toxikologie und Genetik (ITG) .................................................................................................................... 305 ......B 08Laboratorium für Applikationen der Synchrotronstrahlung (LAS)................................................................. 329 . B 07/08Steinbuch Centre for Computing (SCC) ...................................................................................................... 449 ..... C 07

Helmholtz-Programme:

Atmosphäre und Klima (ATMO) ................................................................................................................. 435 ..... D 08BioInterfaces in Technology and Medicine (BIFTM) ...................................................................................... 440 ..... D 08Energieeffizienz, Materialien und Ressourcen (EMR) ................................................................................... 435 ..... D 08Erneuerbare Energien (EE) ......................................................................................................................... 435 ..... D 08Kernfusion (FUSION) .................................................................................................................................. 451 ..... C 07Materie und Technologie (MT) .................................................................................................................... 242 ..... C 09Materie und Universum (MU) .................................................................................................................... 401 .. B/C 08Nukleare Entsorgung und Sicherheit sowie Strahlenforschung (NUSAFE) .................................................... 433 ..... C 08Science and Technology of Nanosystems (STN) ........................................................................................... 440 ..... D 08Speicher und vernetzte Infrastrukturen (SCI) ............................................................................................... 418 ..... C 08Supercomputing & Big Data (SBD) .............................................................................................................. 449 ..... C 07Technologie, Innovation und Gesellschaft (TIG) ......................................................................................... 3300 ..... K‘heVon Materie zu Materialien und Leben (MML) ........................................................................................... 329 .....B 7/8

Warnhinweise

Interne Notrufnummer 3333

Auf dem Campus gilt die Straßenverkehrsordnung.

Höchstgeschwindigkeit 50 km/h.

Vorfahrtsregel „rechts vor links“ beachten.

Sicherheitsbereiche dürfen nur unter Beachtung örtlicher Regularien betreten werden.

Zutritt ist tageszeitlich begrenzt.

Karlsruher SchlossKarlsruher SchlossKarlsruher Schloss


Kronenplatz

Marktplatz2, 3, 5, S1, S11, S4, S41, S5

Marktplatz1, 2, 4, 5, S2, S4, S41, S5

1, 2, 4, 5, S2, S4, S41, S5

H

H

Durlacher Tor /KIT Campus-Süd1, 2, 4, 5, S2, S4, S41, S5

01.85

01.90

01.9201.93

01.94

Waldparkplatz

Ernst-Gaber-Str.

Paulcke-Platz

W.-J

orda

n-W

eg

Moltkestraße

Engesserstraße

ZähringerstraßeKreuzstraße

Schlossplatz

Zirkel

Kronenstraße

Waldhornstraße

Fritz-Erler-Straße

Waldhornstraße

Berliner Platz

Kaiserstraße

Kapell

enstr

aße

Durlacher Allee

R.-Baumeister-Platz

Otto-Ammann-PlatzRu

dolf

-Pla

nk-S

traß

e

Wilhelm-Nusselt-WegEhrenhof


Iren

e-Ro

senb

erg-

Stra

ße

Engesserstraße

Englerstraße

Lehm

anns

traß

e Wol

fgan

g-G

aede

-Str

aße


Neu

er Z

irkel

Frit

z-H

aber

-Weg

Schlossbezirk

Simon-M

oser-Weg


Stra

ße a

m F

orum

Engl

er-B

unte

-Rin

g

Ade

naue

rrin

g

Hagsfelder Allee

Am Fasanengarten

Gotthard-Franz-StraßeKornblumenstraße

Fraunhoferstraße

Vincenz-Prießnitz-Straße

Edelsheimstraße

Parkstraße

Schönfeldstraße


Karl-Wilhelm-Stra

ße

Lärchenallee

Wes

tein

fahrt

mit

Codekar

te

Haupteinfahrt


Einfahrt

Einfahrt

Einf

ahrt

10.50

Studierendenwerk


Administration

Studierendenservice

Steinbuch Centre forComputing (SCC)


Forstliches Bildungs-zentrum Karlsruhe

Sportanlagen

10.40

10.30

10.7

0

FORUM

MENSA

Audimax

SPORT

10.31

10.32 10.34

10.33

KIT-BIBLIOTHEK

01.5101.52

30.12 30.11

30.10

20.1120.12

20.13

20.14

01.53

20.20

20.21

20.50 20.51 20.52

02.1

1

02.1

0

20.54

20.5302.02 02.03 02.04

20.30

02.95

30.21

30.22

30.2

3

30.24

30.2

5

20.4011.20

11.1

1

11.10

11.30

11.4

010

.1201

.80

10.11

10.9

1

10.92 10.93 10.94

10.9

5

10.96

10.21

01.95

10.23

11.2

3

11.21

11.2

2

10.81

10.82

10.83 10.84 10.85

10.86 10.87

10.8

8 10.89

10.6

110

.64

10.6310.62

02.17

02.12

02.1

3

02.1

402.1

5

02.16

02.18

30.91

30.9

3

30.90

30.41

30.42

30.43

30.44

30.45

30.31

30.33

30.3

430.35

30.36

30.81 30.8230.51

30.5

0

01.1

3

30.60 30.6130.95

30.9

6

30.70

30.79

30.29

40.12

40.13

40.1

4

40.15

40.16

40.17 40.18

40.1

9

40.0

240

.51

40.0

4

40.21

40.50

40.22 40.2

340

.24

40.2

5

40.2

6

40.27

40.28

40.29

40.3

140

.32

40.33

40.40

40.44

40.41

40.43

50.20

50.2150.22

50.2650.25

50.1050.12 07.21

50.33

50.34

50.35

50.3650.38

50.4

0

50.41

01.70

05.01 05.20

01.96

07.0707.08

07.30

07.31

07.01

Adolf-Würth-Gebäude

30.28

PRÄSIDIUM

11.30

10.9

1

10.9610.96

11.30

10.9

1

10.9610.96

KinderUniversum

Studienberatung

30.46

30.32

01.1

2

11

12

13

14

19

1

2a

2

2

3

5

5a 5b

24

6

13

2

7

7

9

7

1a

51

13

11b

7

5

11a

4

15

42

2-6

68

17

31

8

64

2

7

1 1a

2

3

4

651

7

9

8a4a

8 422a 2b

3

22

1042

1

12

4

6 8 10

12

2

13

14

11

7

2018

5

14

5a

2

8

1

6

4

2

5

5

3

13

2

7

9

109

8

7

65

11

71

3

59b

9a11

a 11c

11a

11b

14122

4

8

15

23

2221

3

1-3

27

3032

34

53

3

6

14

13

31

1a

13

30.4

9

30.4

8

50.3

1 50.32

KIT-Campus Süd

Lieferanschrift:Kaiserstraße 1276131 Karlsruhe

Eggenstein-Leopoldshafen

Karlsruhe A5Durlacher Allee

Moltkestr.

Basel/Stuttgart

Ausfahrt Karlsruhe Durlach

Frankfurt

Karlsruhe

Graben-Neudorf

36

L558

L559

Adenauerring

KIT-Campus Nord

Ostendorfhaus

KIT-Campus West

KIT-Campus Ost

KIT-Campus Süd

KIT-StandorteKarlsruhe

LAGEPLAN CAMPUS NORD

KIT-StandorteDeutschland

Karlsruhe

Dresden

Garmisch

Ulm

Black Forest Observatory

1

2

3

44

5

6

7

8

9

10

A B C D

D

A B C D

1

2

1

2

3

4

5

6

7

8

9

10

3

KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft

100 mN

Neubaumaßnahmen

Fremdinstitutionen

Zaunanlage/Arealbegrenzung Fremdinstitution

Figure 13: https://www.kit.edu/downloads/Campus-Nord.pdf,Retrieved on 05.01.2019

08.01.19

https://www.kit.edu/downloads/Campus-Nord.pdf



Additional time series approaches

Stationarity

“A stationary time series is one whose properties do not depend on the

time at which the series is observed.” (Hyndman and Athanasopoulos

2018)

Implementation with differencing to remove effects

Normalization (Brownlee 2018, p. 249, Chollet 2018, p. 210f)

Min-Max-Scaling

Transform features by scaling each feature to a given range

Implementation with range [0, 1]

Standardization

Standardize features by removing the mean and scaling to unit variance (z-

transformation)

Implementation with mean = 0 and standard deviation = 1




09.01.1908.01.19


Overview of evaluation scenario characteristics



Characteristic Category

Level of Aggregation 15 minutes 1 hour

Stationarity Stationary Non-Stationary

Normalization None Min-Max-Scaling Standardization

Models Stacked Long Short-Term Memory

Sequence-to-Sequence Long Short-Term Memory

Horizon One step One Day One Week

à 72 scenarios per building

09.01.1908.01.19


Evaluation metric

Root mean squared error (RMSE)

(Mocanu et al. 2016, p. 95)


RMSE = 1' ∗ )*

+,-.

/+0-.

12(40,, − 740,,)9

)* à number of steps in horizon' à total number of steps predicted into the future40 à real values for time-step t740 à value predicted by the model for time-step t



Baseline models

Naïve ForecastValue of the last week on the same day of the week at the same timePrediction for timestep t: (t - 672 / t - 168)

Ordinary RegressionRegression of the respective load time series with the following regressors

Load before one day (t - 96 / t - 24)Load before two days (t - 192 / t - 48)Load before one week (t - 672 / t - 168)Dummy variables for weekend, month, hour and minute (only for 15 minutes)



Legend: (15 minutes / 1 hour)


Results for Building 124


Level of Aggre-gation

Sta-tionary

Normal-ization

RMSE per time horizon

Stacked-LSTM S2S-LSTM Naïve ForecastOrdinary

Regression

15 Minutes

No

None (0,015; 0,853; 0,893) (1,474; 1,496; 1,491)

0,909 (0,397; 0,789; 0,825)

Min-Max (0,01; 0,792; 0,865) (1,482; 1,473; 1,491)

Standard (0,011; 0,816; 0,868) (1,475; 1,489; 1,494)

Yes

None (0,007; 1,193; 1,726) (0,427; 1,755; 2,022)

Min-Max (0,016; 1,039; 3,196) (0,427; 1,761; 2,093)

Standard (0,008; 1,177; 1,693) (0,427; 1,755; 2,023)

1 Hour

No

None (0,021; 0,742; 0,871) (1,453; 1,449; 1,452)

0,829 (0,311; 0,724; 0,76)

Min-Max (0,026; 0,712; 0,81) (1,433; 1,447; 1,453)

Standard (0,03; 0,73; 0,864) (1,443; 1,456; 1,452)

Yes

None (0,008; 0,839; 1,291) (0,558; 1,693; 1,964)

Min-Max (0,049; 0,885; 1,899) (0,558; 1,695; 2,015)

Standard (0,009; 0,856; 1,208) (0,558; 1,7; 1,964)

Legend Root Mean Squared Error (One Step; One Day; One Week) | LSTM – Long Short-Term Memory | S2S – Sequence-to-Sequence




Remarks on results

Technical and practical insightsHandling time shift and some missing days in 2012Data pre-processing pipeline

Traditional time series analysis: 2D [samples, features]Deep Learning: 3D tensor [samples, time-steps, features]

Distinction between one-step and multi-step forecastsParameters

Time for training per Long Short-Term Memory modelapprox. 6,5 hours for all scenarios




DISCUSSION



Comparable results with Mocanu et al. (2016)

Missing parameter configurations

Critical reflection on Marino et al. (2016)

Training batch and epochsNorm clippingDropoutActivation function at Dense layer…



In Mocanu et al. (2016) seven experiments with different resolutions and time horizons on the same dataset (Dheeru and Taniskidou 2017)Implementation and evaluation of Conditional Restricted Boltzmann Machine and Factored Conditional Restricted Boltzmann MachineComparable result in one of seven scenarios à Results of other scenarios?


Related work


DeepArchitecture

Paper Reference

Deep Forward Networks

Con-volutional

Neural Networks

Recurrent Neural

Networks

Deep Generative Networks

Auto-encoders

Amarasinghe et al. 2017 CNN

Gensler et al. 2016 LSTM* AE*

He 2017 CNN* LSTM*

Heghedus et al. 2018 GRU

Jarábek et al. 2017 S2S-LSTM

Li et al. 2017 ELM* SAE*

Marino et al. 2016 Stacked- & S2S-LSTM

Mocanu et al. 2016 RNN CRBM & FCRBM

Ryu et al. 2016 RBM

Voß et al. 2018 WaveNet


Legend: * - Combined Approach | CNN – Convolutional Neural Networks | (F)(C)RBM – (Factored) (Conditional) Restricted Boltzmann Machine | ELM – Extreme Learning Machine | GRU – Gated Recurrent Unit | LSTM – Long Short-Term Memory | RNN – Recurrent Neural Network | S2S – Sequence-to-Sequence | (S)AE – (Stacked) Autoencoder


CONCLUSION



Summary

AchievementsLong Short-Term Memory as one Deep Learning method for Energy Time Series ForecastingOverview of current Deep Architectures for Energy Time SeriesImplementation and evaluation of

Stacked Long Short-Term Memory and Sequence-to-Sequence Long Short-Term Memory

based on Marino et. al. (2016)

Summarised resultsStacked Long Short-Term Memory outperformed baseline models for hourly data in one-step and one-day predictionsGood performance of Sequence-to-Sequence Long Short-Term Memory could not be confirmed (without hyperparameter optimization)




Time Series

Deep Learning

Outlook

Hyperparameter optimizationGrid SearchRandom Search Bayesian Optimization, etc.

Variations of Long Short-Term Memory



Verify results with more buildings“Real” univariate Time Series ForecastMultivariate Time Series Forecast


Questions

08.01.19

?Kai Schmieder: Deep Learning for Energy Time Series38


Bibliography (1/5)Amarasinghe, K., Marino, D. L., & Manic, M. (2017). Deep neuralnetworks for energy load forecasting. In 26th {IEEE} International

Symposium on Industrial Electronics, {ISIE} 2017, Edinburgh, United

Kingdom, June 19-21, 2017 (pp. 1483–1488). IEEE.Bengio, Y., Frasconi, P., & Simard, P. (1993). The problem of learning long-term dependencies in recurrent networks. In Neural Networks,

1993., IEEE International Conference on (pp. 1183–1188).Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on

Neural Networks, 5(2), 157–166.Brownlee, J. (2018). Deep Learning for Time Series Forecasting. Machine Learning Mastery. Retrieved from https://machinelearningmastery.com/deep-learning-for-time-series-forecasting/Li, C., Ding, Z., Zhao, D., Yi, J., & Zhang, G. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1–20.


https://machinelearningmastery.com/deep-learning-for-time-series-forecasting/


Bibliography (2/5)

Chollet, F. 2018. Deep Learning with Python. Manning. Shelter Island.Dheeru, D., & Karra Taniskidou, E. (2017). “Individual household electric power consumption”. UCI Machine Learning Repository. Retrieved from http://archive.ics.uci.edu/mlGoodfellow, I., Bengio, Y. & Courville, A. (2016). Deep Learning. Cambridge, MA, USA: MIT Press.He, W. (2017). Load Forecasting via Deep Neural Networks. In V. Ahuja, Y. Shi, D. Khazanchi, N. Abidi, Y. Tian, D. Berg, & J. M. Tien (Eds.), Proceedings of the 5th International Conference on Information Technology and Quantitative Management, {ITQM} 2017, December 8-10, 2017, New Delhi, India (Vol. 122, pp. 308–314). Elsevier.Heghedus, C., Chakravorty, A., & Rong, C. (2018). Energy Load Forecasting Using Deep Learning. In 2018 IEEE International Conference on Energy Internet (ICEI) (pp. 146–151).


http://archive.ics.uci.edu/ml


Bibliography (3/5)

Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma, Technische Universität München, 91(1).Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.Hyndman, R. J. & Athanasopoulos, G. Forecasting: Principles and Practice. https://otexts.org/fpp2/stationarity.html#fn14, Retrieved on 17.12.18Jarábek, T., Laurinec, P., & Lucká, M. (2017). Energy load forecast using S2S deep neural networks with k-Shape clustering. In 2017 IEEE 14th International Scientific Conference on Informatics (pp. 140–145).Li, C., Ding, Z., Zhao, D., Yi, J., & Zhang, G. (2017). Building energy consumption prediction: An extreme deep learning approach. Energies, 10(10), 1–20.


https://otexts.org/fpp2/stationarity.html


Bibliography (4/5)

Marino, D. L., Amarasinghe, K., & Manic, M. (2016). Building energy load forecasting using Deep Neural Networks. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, 7046–7051.Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91–99.Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398.Ryu, S., Noh, J., & Kim, H. (2016). Deep neural network based demandside short term load forecasting. 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016, 308–313.Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems (NIPS), 3104–3112.



Bibliography (5/5)

Voß, M., Bender-Saebelkampf, C., & Albayrak, S. (2018). Residential Short-Term Load Forecasting Using Convolutional Neural Networks. In IEEE International Conference on Communications, Control, andComputing Technologies for Smart Grids (SmartGridComm).



APPENDIX AND BACKUP



How does deep learning basically work?


Source: Own representation based on Chollet (2018, p.11)


Activation functions

Sigmoid (σ)! t = $

$% &'(Value range [0; 1]

Tangens hyperbolicus (tanh)tanh , = &( - &'(

&(% &'(Value range [-1; 1]


Source: https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Hyperbolic_Tangent.svg/1920px-Hyperbolic_Tangent.svg.png (Retrieved on 06.01.2019)

Source: https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/Sigmoid-function-2.svg/1920px-Sigmoid-function-2.svg.png (Retrieved on 06.01.2019)

https://upload.wikimedia.org/wikipedia/commons/thumb/8/87/Hyperbolic_Tangent.svg/1920px-Hyperbolic_Tangent.svg.png

https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/Sigmoid-function-2.svg/1920px-Sigmoid-function-2.svg.png


Details: Stacked Long Short-Term Memory

Objective function

Unrolling implemented with M=50

Optimizer Adam

Training with Backpropagation Through Time

Optimization with Norm clipping


! = #$%&

'() $ − +)[$])/

Figure 5: Own representation based on Marino et al., 2016, p. 7047


Details: S2S Long Short-Term Memory

Similar setup as Stacked Long Short-Term Memory

Encoder objective function

Decoder objective function

Optimization with Norm clippingRegularization with Dropout


Figure 6: Own representation

!" = $%&'

((* % − ,*[%])0

!1 = $%&(2'

3(* % − ,*[%])0


Backpropagation Through Time for S2S LSTM


deep learning for energy time series - kit › _media › teaching › winter... · two main...

Documents