Advanced Fuzzy-Logic-Based Context-Driven Control for HVAC Management Systems in Buildings.
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2020
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IEEE Access
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Abstract
Control in HVAC (heating, ventilation and air-conditioning) systems of buildings is not trivial,
and its design is considered challenging due to the complexity in the analysis of the dynamics of its nonlinear
characteristics for the identi cation of its mathematical model.HVAC systems are complex since they consist
of several elements, such as heat pumps, chillers, valves, heating/cooling coils, boilers, air-handling units,
fans, liquid/air distribution systems, and thermal storage systems. This article proposes the application of
LAMDA (learning algorithm for multivariable data analysis) for advanced control in HVAC systems for
buildings. LAMDA addresses the control problem using a fuzzy classi cation approach without requiring
a mathematical model of the plant/system. The method determines the degree of adequacy of a system for
every class and subsequently determines its similarity degree, and it is used to identify the functional state or
class of the system. Then, based on a novel inference method that has been added toLAMDA, a control action
is computed that brings the system to a zero-error state. The LAMDA controller performance is analyzed via
evaluation on a regulation problem of an HVAC system of a building, and it is compared with other similar
approaches. According to the results, our method performs impressively in these systems, thereby leading to
a trustable model for the implementation of improved building management systems. The LAMDA control
performs very well for disturbances by proposing control actions that are not abrupt, and it outperforms the
compared approaches.
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HVAC control, Control engineering, Fuzzy logic, Artificial intelligence