An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks

Bing Su and Shengwei Wang

Applied Energy, 2020, vol. 274, issue C, No S0306261920308345

Abstract: Existing studies on distributed optimal control of HVAC systems rarely consider the needs and constraints for practical applications and the deployment of control strategies on the physical building automation platforms. This paper proposes an agent-based distributed real-time control strategy for building HVAC systems with the objective to be deployed in the local control devices with limited programming and computation capacities, i.e. smart sensors integrated in future IoT-based field networks and local controllers in field networks of current LAN-based building automation systems. A complex optimization task with high computational complexity (i.e., computation code and computation load) is decomposed into a number of simple tasks, which can be handled by coordinating agents among the integrated local control devices. The computation task of an optimization decision is further distributed into a number of steps, each performed at a sampling interval of controllers. The test results show that the computation loads of all individual agents at each step were below 2000 FLOPs, which can be handled by the typical smart sensors using simple optimization codes and the number of iterations for each optimization decision was within 50, well below the convergence rate needed for optimal control with typical time interval of minutes. The proposed agent-based optimal control strategy is also convenient and effective to deal with multiple components of different performances and the optimization considering such performance deviations could reduce the overall energy consumption significantly.

Keywords: Agent-based control; Real-time optimal control; IoT-based optimal control; Smart sensor; Air-conditioning system; Energy efficiency (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (10)

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DOI: 10.1016/j.apenergy.2020.115322

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