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Artificial Intelligence Approaches for Energy Efficiency: A Review (2407.21726v1)

Published 31 Jul 2024 in cs.AI

Abstract: United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals. Climate change is a major concern in our society; for this reason, a current global objective is to reduce energy waste. This work summarizes all main approaches towards energy efficiency using Artificial Intelligence with a particular focus on multi-agent systems to create smart buildings. It mentions the tight relationship between AI, especially IoT, and Big Data. It explains the application of AI to anomaly detection in smart buildings and a possible classification of Intelligent Energy Management Systems: Direct and Indirect. Finally, some drawbacks of AI approaches and some possible future research focuses are proposed.

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References (27)
  1. BACnet International. 2024. BACnet. https://bacnet.org.
  2. JADE–A FIPA-compliant Agent Framework. In Proceedings of PAAM, volume 99, 33. London, UK: The Practical Application Company Ltd.
  3. Connectivity Standards Alliance. 2020. Project-Chip/Connectedhomeip. Connectivity Standards Alliance.
  4. Connectivity Standards Alliance. 2022. Build With Matter | Smart Home Device Solution. https://csa-iot.org/all-solutions/matter/.
  5. Data Mining of Building Performance Simulations Comprising Occupant Behaviour Modelling. Advances in Building Energy Research, 13(2): 157–173.
  6. Darby, S. 2006. The Effectiveness of Feedback on Energy Consumption. A Review for DEFRA of the Literature on Metering, Billing and direct Displays, 486.
  7. Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency. Applied Sciences, 11(2): 763.
  8. Ferber, J. 1999. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. USA: Addison-Wesley Longman Publishing Co., Inc., 1st edition. ISBN 0-201-36048-9.
  9. Agreement Technologies for Energy Optimization at Home. Sensors, 18(1633).
  10. Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review. Energies, 11(8): 1928.
  11. Artificial Intelligence Based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives. Applied Energy, 287: 116601.
  12. IDC. 2021. Volume of Data/Information Created, Captured, Copied, and Consumed Worldwide from 2010 to 2020, with Forecasts from 2021 to 2025 (in Zettabytes) [Graph]. In Statista. https://www.statista.com/statistics/871513/worldwide-data-created/.
  13. Big Data in Building Energy Efficiency: Understanding of Big Data and Main Challenges. Procedia Engineering, 172: 544–549.
  14. Energy Savings, Emission Reductions, and Health Co-Benefits of the Green Building Movement. Journal of Exposure Science & Environmental Epidemiology, 28(4): 307–318.
  15. A Review of the Applications of Artificial Intelligence and Big Data to Buildings for Energy-Efficiency and a Comfortable Indoor Living Environment. Energy and Buildings, 202: 109383.
  16. Intelligent Energy Management Systems: A Review. Artificial Intelligence Review, 56(10): 11635–11674.
  17. Energy Intelligent Buildings Based on User Activity: A Survey. Energy and Buildings, 56: 244–257.
  18. FIPA — Towards a Standard for Software Agents. BT Technology Journal, 16(3): 51–59.
  19. Big Data: A Review. In 2013 International Conference on Collaboration Technologies and Systems (CTS), 42–47. San Diego, CA, USA: IEEE. ISBN 978-1-4673-6404-1 978-1-4673-6403-4 978-1-4673-6402-7.
  20. A Formal Analysis of Potential Energy in a Multi-Agent System. In International Workshop on Formal Approaches to Agent-Based Systems, volume 3228 of Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 131–145. Springer. ISBN 978-3-540-24422-6.
  21. An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games. Autonomous Agents and Multi-Agent Systems, 12(1): 115–153.
  22. Evolutionary Game Theory and Multi-Agent Reinforcement Learning. The Knowledge Engineering Review, 20(1): 63–90.
  23. United Nations. 2015. Take Action for the Sustainable Development Goals. https://www.un.org/sustainabledevelopment/sustainable-development-goals/.
  24. WBCSD. 2009. Transforming the Market: Energy Efficiency in Buildings.
  25. Wooldridge, M. 1999. 1 Intelligent Agents. In Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, 3–44. MA, USA: MIT Press: Cambridge.
  26. Development of Multi-Agent System for Building Energy and Comfort Management Based on Occupant Behaviors. Energy and Buildings, 56: 1–7.
  27. Big Data Driven Smart Energy Management: From Big Data to Big Insights. Renewable and Sustainable Energy Reviews, 56: 215–225.

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