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DNV GL © 18 April 2019 SAFER, SMARTER, GREENER DNV GL ©

Elizabeth Traiger, Ph.D. M.Sc.

18 April 2019

MAKING RENEWABLES SMARTER

1

Artificial Intelligence and Machine Learning Applications in the Wind Industry

DNV GL © 18 April 2019

Data = New Oil… AI = New Electricity

2

DNV GL © 18 April 2019 3

Road Map

What is AI 01

Current applications in Renewables 02

Visions of the Future 03

Panel Discussions 04

DNV GL © 18 April 2019

01 What is AI? Perceptions and Definitions

4

DNV GL © 18 April 2019

Buzz Words Need Definitions

5

DNV GL © 18 April 2019

DS, ML & AI

Data science produces insights

Machine learning produces predictions

Artificial intelligence produces actions

6

DNV GL © 18 April 2019

Data Science produces Insights

7

Statistical inference

Data visualization

Experiment design

Domain knowledge

Communication

DNV GL © 18 April 2019

Machine learning produces predictions

8

Machine Learning

Pattern Recognition

Separation

Estimates

Generalization

DNV GL © 18 April 2019

Machine Learning produces Predictions

9

Statistics

Primary Data Analysis

‘Top Down’

Hypothesis Testing

Model Driven

Confirmatory Analysis

Machine Learning

Include Secondary

Observational Data

Hypothesis Generation

Data Driven

Knowledge Discovery

DNV GL © 18 April 2019

Machine Learning Based On Data

10

Supervised

Classification Regression

Unsupervised

Clustering Dimension Reduction

Other

Reinforcement Learning

Adversarial Networks

DNV GL © 18 April 2019

Common Supervised Machine Learning Algorithms

11 https://machinelearningmastery.com/blog/

DNV GL © 18 April 2019

Common Unsupervised Algorithms & Other

12

Reinforcement Learning

DNV GL © 18 April 2019

Natural Language Processing (NLTK) - LSTM

13

DNV GL © 18 April 2019

Artificial intelligence produces actions

14

DNV GL © 18 April 2019

Automation is not Artificial Intelligence

15

https://www.serbot.ch/en/solar-panels-cleaning/gekko-solar-farm-robot

DNV GL © 18 April 2019

Machine learning underpins advancements in AI

Properties of AI – human-like capabilities Converting human-like capabilities to data

science and ML

16

See: Image and video recognition

Hear: Understand input via text or

spoken language

Speak: Respond meaningfully to

input (from ‘hear’)

Make human-like decisions:

Offer advice or new knowledge

Learn: Change its behavior based

on environment changes

Move: Move and interact with

physical objects

Image processing:

Convolutional NNs (CNNs)

Natural Language Processing:

Recurrent NNs (RNNs), e.g. LSTM

Question Answering Machines

(Ontologies, e.g. IBM Watson)

Unsupervised learning:

Generative Adversarial NNs (GAN)

Reinforcement learning

(e.g. by RNNs)

Robotics

D

DNV GL © 18 April 2019

Big Data in the Renewables Industry – Potential for AI

17

SCADA

Atmospheric Performance

Demand Response

Temperature

Grid

Market

IoT

DNV GL © 18 April 2019

02 Applications in Renewables

18

DNV GL © 18 April 2019

Generative Design & Computational Chemistry

19 https://www.autodesk.com/solutions/generative-design

DNV GL © 18 April 2019

Data QC - Cleaning

20

DNV GL © 18 April 2019

Automating Preconstruction and Operational Assessments

21

Outputs

DNV GL © 18 April 2019

CFD Flow Modelling

22

NREL using Gaussian Processes in model

corrections

Autodesk replacing CFD numerical simulations

and solvers with CNNs

DNV GL © 18 April 2019

Optimization via Reinforcement Learning

Need to carefully define utility

23

DNV GL © 18 April 2019

Construction & Decommissioning – Autonomous Robotics

24

Remote-controlled machines begin dismantling a cooling tower at the Mülheim-Kärlich nuclear power station on the banks of the Rhine Photograph: Thomas Frey/AFP/Getty Images

Built autonomous dozer

Canrig Robotic Technologies autonomous robotic drilling

rig for unmanned drilling operations

DNV GL © 18 April 2019

Autonomous Drone Inspections & CV Analysis

25

E Smart Systems & SkySpecs AI software

DNV GL © 18 April 2019

Computer Vision - Object Recognition within Energy

Environmental Permitting

GIS & Satellite Surveys

Field Inspection Services

Laboratory Services

Flow Modelling

26

DNV GL © 18 April 2019

Forecasting – Resource, Demand, Price, etc.

28

DNV GL © 18 April 2019

Non-Traditional Industry Players

29

DNV GL © 18 April 2019

Power Curve Specifications – Change in Standards

PCWG & IEA Task 32– Regression Forest Ensemble Models

University of Strathclyde – Gaussian Process Models

30

DNV GL © 18 April 2019

Condition Monitoring for Predictive Maintenance

31

Time

Anomaly KPI

Normal Behaviour Older Threshold Methods

DNV GL © 18 April 2019

Production Performance Monitoring

32

DNV GL © 18 April 2019

AI - Wind Farm Control – Solar Dual Axis Panel Control

33

DNV GL © 18 April 2019

Grid Balancing

34

DNV GL © 18 April 2019

NLTK in Renewables – Reduce barriers to entry

Maintenance Diagnosis

Independent Engineering Contract Review

Site Inspection Log Analysis

35

DNV GL © 18 April 2019

Change in the Air - Financials, Market Analysis & Consumer Behaviour

36

Financing Incentives in Area Consumption Recommendations

DNV GL © 18 April 2019

04 Visions of the Future

37

DNV GL © 18 April 2019

ETIP Wind(.eu) thoughts

38

DNV GL © 18 April 2019

DNV GL’s Vision of the Future

39

DNV GL © 18 April 2019

SAFER, SMARTER, GREENER

www.dnvgl.com

The trademarks DNV GL®, the Horizon Graphic and Det Norske Veritas® are the properties of companies in the Det Norske Veritas group. All rights reserved

Thank You

40

Elizabeth Traiger, Ph.D., M.Sc.

Elizabeth.Traiger@dnvgl.com

Senior Researcher, Group Technology & Research – Power & Renewables

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