Lecture 13 Big Data and AI for Clean Energy

Gang He

November 21, 2022

Sample analytic questions

• What are the new advancements in AI and big data?
  
• How AI and big data could be used in facilitating clean energy transition and addressing climate change?
  
• Any good examples or success stories of AI for energy/climate change?
  
• What skills are need to work in this field?
  

Offshore wind turbine inspection using drones and AI

A local example

“AI is the new electricity”

Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.

–Andrew Ng

Milestones in AI

Perspective:

• Universe created:

  • 13.8 billion years ago

• Earth created:

  • 4.54 billion years ago

• Modern human:

  • 300,000 years ago

• Civilization:

  • 12,000 years ago

• Written record:

  • 5,000 years ago

• 1943: Neural networks
• 1957: Perceptron
• 1974-86: Backpropagation, RBM, RNN
• 1989-98: CNN, MNIST, LSTM, Bidirectional RNN
• 2006: “Deep Learning”, DBN
• 2009: ImageNet
• 2012: AlexNet, Dropout
• 2014: GANs
• 2014: DeepFace
• 2016: AlphaGo
• 2017: AlphaZero, Capsule Networks
• 2018: AlphaFold 1, BERT
• 2021: AlphaFold 2

Aartifitial intelligence, machine learning, and deep learning

AI Algorithms

How AI has transformed our society

Key enabling factors

• Technological maturity
  
• Availability and quality of data
  
• Growing importance of cybersecurity
  
• Training and re-skilling of energy sector professionals
  

Deep solar

AI to improve wind perfomance

AI saves data center cooling energy

Global inventory of solar PV

Machine learning for electricity access

AI for climate solutions

AI for GHG mitigation

AI ethics: why?

• Biases
  
• Privacy
  
• Mistakes
  
• Environmental impacts
  
• Other unaware impacts

AI ethics: principles

• Proportionality and do no harm
  
• Safety and security
  
• Fariness and non-discrimination
  
• Sustainability
  
• Right to privacy, and data protection
  
• Human oversight and determination
  
• Transparency and explainability
  
• Responsibility and accountability
  
• Awareness and literacy
  
• Multi-stakeholder and adaptive governance and colloboration

AI ethics: how?

• Still slow responding to the fast evolving challenges
• Education and research
  
• Assessment and communication
  
• Governance and cooperation
  
• Value alligned design

Summary

• AI and big data has transformed how we live, and how can we save the planet
  
• AI does not automatic add new insights, but it provides powerful tools to analyze increasingly available big data
  
• Learning those new tools and skills becomes necessary to work in the field
  
• The potential of using AI/big data to analyze our understanding of energy supply/demand and human behavior is enomorous.

References

Kaack, Lynn H, Priya L Donti, Emma Strubell, George Kamiya, Felix Creutzig, and David Rolnick. 2022. “Aligning Artificial Intelligence with Climate Change Mitigation.” Nature Climate Change, 1–10. https://doi.org/10.1038/s41558-022-01377-7.

Kruitwagen, L, KT Story, J Friedrich, L Byers, S Skillman, and C Hepburn. 2021. “A Global Inventory of Photovoltaic Solar Energy Generating Units.” Nature 598 (7882): 604–10. https://doi.org/10.1038/s41586-021-03957-7.

Lee, Mekyung, and Gang He. 2021. “An Empirical Analysis of Applications of Artificial Intelligence Algorithms in Wind Power Technology Innovation During 1980–2017.” Journal of Cleaner Production 297: 126536. https://doi.org/10.1016/j.jclepro.2021.126536.

Ratledge, Nathan, Gabe Cadamuro, Brandon de la Cuesta, Matthieu Stigler, and Marshall Burke. 2022. “Using Machine Learning to Assess the Livelihood Impact of Electricity Access.” Nature 611 (7936): 491–95. https://doi.org/10.1038/s41586-022-05322-8.

Rolnick, David, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, et al. 2022. “Tackling Climate Change with Machine Learning.” ACM Computing Surveys (CSUR) 55 (2): 1–96. https://doi.org/10.1145/3485128.

Yu, Jiafan, Zhecheng Wang, Arun Majumdar, and Ram Rajagopal. 2018. “DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States.” Joule 2 (12): 2605–17. https://doi.org/10.1016/j.joule.2018.11.021.