Industry

From Climate Change AI Wiki
This page is about the intersection of industrial systems and machine learning in the context of climate change mitigation. For an overview of industry as a whole, please see the Wikipedia page on this topic.
Selected opportunities to use machine learning to reduce greenhouse gas emissions in industry.
Industrial production, logistics, and building materials are leading causes of difficult-to-eliminate GHG emissions[1]. Fortunately for ML researchers, the global industrial sector spends billions of dollars annually gathering data on factories and supply chains[2] – aided by improvements in the cost and accessibility of cloud-based storage and computing, as well as sensors and data-gathering mechanisms such as QR codes and image recognition (vaguely referred to as "Industry 4.0" and "Smart/Connected Factories").

ML can potentially reduce industrial emissions by helping to streamline supply chains, invent cleaner materials and chemicals, design for fewer materials[3], improve production quality, predict machine breakdowns, optimize heating and cooling systems[4], and prioritize the use of clean electricity over fossil fuels[5][6].

ML can have a substantial positive climate impact on the industrial sector under the following circumstances:

  • When there is enough accessible, high-quality data around specific processes or transport routes.
  • When firms have an incentive to share their proprietary data and/or algorithms with researchers and other firms.
  • When aspects of production or shipping can be readily fine-tuned or adjusted, and there are clear objective functions.
  • When firms’ incentives align with reducing emissions (for example, through efficiency gains, regulatory compliance, or high GHG prices).

Yet greater efficiency may increase the production of goods and thus GHG emissions (via the Jevons paradox) unless industrial actors have sufficient incentives to reduce overall emissions[7].

Machine Learning Application Areas

Optimizing supply chains

    • Supply and demand forecasting: The supply and demand of power must both be forecast ahead of time to inform electricity planning and scheduling. ML can help make these forecasts more accurate, improve temporal and spatial resolution, and quantify uncertainty.
  • Optimizing supply chains and logistics
  • Carbon-conscious sourcing

Developing cleaner products and materials

  • Accelerated science for clean energy technologies: Designing new materials is important for many applications, including energy storage via batteries or solar/chemical fuels. ML can help suggest promising materials to try, thereby speeding up the materials discovery process.
  • Minimizing material use through intelligent design
  • Designing for carbon-conscious products/processes

Optimizing factory operations

Background Readings

Online Courses and Course Materials

Autodesk Sustainability Workshop: "free online learning resources that teach the principles and practice of sustainability in engineering and design. This site was created to help students, educators and professionals learn more about sustainability in engineering and architecture professions." Practical videos on sustainable product design, materials selection, and engineering available here.

Conferences, Journals, and Professional Organizations

Major conferences

Major journals

Major professional organizations

IEEE Power & Energy Society: "[T]he world's largest forum for sharing the latest in technological developments in the electric power industry, for developing standards that guide the development and construction of equipment and systems, and for educating members of the industry and the general public." Website here.

Past and upcoming events

Libraries and Tools

Data

See the subpages listed above for application-specific datasets.

References

  1. Davis, Steven J.; Lewis, Nathan S.; Shaner, Matthew; Aggarwal, Sonia; Arent, Doug; Azevedo, Inês L.; Benson, Sally M.; Bradley, Thomas; Brouwer, Jack; Chiang, Yet-Ming; Clack, Christopher T. M. (2018-06-29). "Net-zero emissions energy systems". Science. 360 (6396): eaas9793. doi:10.1126/science.aas9793. ISSN 0036-8075.
  2. Mike Gualtieri, Noel Yuhanna, Holger Kisker, Rowan Curran, Brandon Purcell, Sophia Christakis, Shreyas Warrier, and Matthew Izzi. The Forrester Wave: Big data streaming analytics, Q1 2016. Forrester.com, January 2016.
  3. Kazi, Rubaiat Habib; Grossman, Tovi; Cheong, Hyunmin; Hashemi, Ali; Fitzmaurice, George (2017-10-20). "DreamSketch: Early stage 3D design explorations with sketching and generative design". Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM. doi:10.1145/3126594.3126662. ISBN 978-1-4503-4981-9.
  4. Richard Evans and Jim Gao. DeepMind AI reduces Google data centre cooling bill by 40%. DeepMind blog, 20, 2016.
  5. Zhang, Xiao; Hug, Gabriela; Kolter, J. Zico; Harjunkoski, Iiro (2016-07). "Model predictive control of industrial loads and energy storage for demand response". 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE. doi:10.1109/pesgm.2016.7741228. ISBN 978-1-5090-4168-8. Check date values in: |date= (help)
  6. Berral, Josep Ll.; Goiri, Íñigo; Nou, Ramón; Julià, Ferran; Guitart, Jordi; Gavaldà, Ricard; Torres, Jordi (2010). "Towards energy-aware scheduling in data centers using machine learning". Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking - e-Energy '10. New York, New York, USA: ACM Press. doi:10.1145/1791314.1791349. ISBN 978-1-4503-0042-1.
  7. Sorrell, Steve (2009-04). "Jevons' Paradox revisited: The evidence for backfire from improved energy efficiency". Energy Policy. 37 (4): 1456–1469. doi:10.1016/j.enpol.2008.12.003. ISSN 0301-4215. Check date values in: |date= (help)