Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems

D. Michael Franklin, Philip Irminger, Heather Buckberry, Mahabir Bhandari

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2020 Computing Conference
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
PublisherSpringer
Pages19-32
Number of pages14
ISBN (Print)9783030522452
DOIs
StatePublished - 2020
EventScience and Information Conference, SAI 2020 - London, United Kingdom
Duration: Jul 16 2020Jul 17 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1229 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceScience and Information Conference, SAI 2020
Country/TerritoryUnited Kingdom
CityLondon
Period07/16/2007/17/20

Keywords

  • Artificial intelligence
  • Building management systems
  • HVAC
  • Machine learning
  • Multi-agent systems
  • Strategy

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