Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study
Authored by Iakovos T Michailidis, Thomas Schild, Roozbeh Sangi, Panagiotis Michailidis, Christos Korkas, Johannes Fuetterer, Dirk Mueller, Elias B Kosmatopoulos
Date Published: 2018
DOI: 10.1016/j.apenergy.2017.11.046
Sponsors:
European Union
Platforms:
MATLAB
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Abstract
A variety of novel, recyclable and reusable, construction materials has
already been studied within literature during the past years, aiming at
improving the overall energy efficiency ranking of the building
envelope. However, several studies show that a delicate control of
indoor climating elements can lead to a significant performance
improvement by exploiting the building's savings potential via smart
adaptive HVAC regulation to exogenous uncertain disturbances (e.g.
weather, occupancy). Building Optimization and Control (BOC) systems can
be categorized into two different groups: centralized (requiring high
data transmission rates at a central node from every corner of the
overall system) and decentralized(1) (assuming an intercommunication
among neighboring constituent systems). Moreover, both approaches can be
further divided into two subcategories, respectively: model-assisted
(usually introducing modeling oversimplifications) and model-free
(typically presenting poor stability and very slow convergence rates).
This paper presents the application of a novel, decentralized,
agent-based; model-free BOC methodology (abbreviated as L4GPCAO) to a
modern non-residential building (E.ON. Energy Research Center's main
building), equipped with controllable HVAC systems and renewable energy
sources by utilizing the existing Building Management System (BES). The
building testbed is located inside the RWTH Aachen University campus in
Aachen, Germany. A combined rule criterion composed of the non-renewable
energy consumption (NREC) and the thermal comfort index - aligned to
international comfort standards - was adopted in all cases presented
herein. Besides the limited availability of the specified building
testbed, real-life experiments demonstrated operational effectiveness of
the proposed approach in BOC applications with complex, emerging
dynamics arising from the building's occupancy and thermal
characteristics. L4GPCAO outperformed the control strategy that was
designed by the planers and system provider, in a conventional manner,
requiring no more than five test days.
Tags
Climate
Design
environment
Optimization
distributed control
System
Consumption
Agent-based optimization
Optimized hvac
management
Building energy-efficiency
Real-life tests
Commercial
building
Thermal insulation materials
Model-predictive control
Performance
evaluation
Inertia