Abstract
This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.
Highlights
The need for saving energy to improve the sustainability of the Planet is increasingly worrying society and requires to put significant research on it
Recent articles emphasize the use of advanced control algorithms [2]–[4], [6] and the optimization of the HVAC system parameters [8], [18], [19], for improving the energy efficiency in buildings, as an inefficient operation of HVAC systems can result in excessive energy consumption
Is it possible to expand the study to different contexts The selected references have the closest topics to the proposed HVAC concept, including multi-objective optimization, control systems, energy optimization, multi-chiller systems, BMS and data-driven predicting models
Summary
The need for saving energy to improve the sustainability of the Planet is increasingly worrying society and requires to put significant research on it. In this work are analyzed buildings with multi-HVAC systems In this context, it is required the determination of the optimal functional mode of the multi-HVAC systems for a given situation, in order to improve their energy consumption, equipment performance and thermal comfort. This work analyzes buildings with heterogeneous multi-HVAC systems (different heat pumps, chillers, etc.) for testing the versatility of the paradigm ACODAT to deliver the optimal functional mode of the multi-HVAC system for any given situation. The main contributions of this article are: i) A proposal of a general autonomous architecture based on ACODAT paradigm to manage multi-HVAC systems in buildings, optimizing multiple goals, according to the changing contextual information; ii) An extension of domain-based models with data-driven knowledge models, to predict on the fly multiHVAC systems context-driven behaviors.
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