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Optimal Control Strategy for HVAC System in Building Energy Management
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Optimal Control Strategy for HVAC System in Building Energy Management

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: 28 November 2024 | Viewed by 1929

Special Issue Editors

Prof. Dr. Alessandro Mauro

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Guest Editor
Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope”, Centro Direzionale, Isola C4, 80143 Napoli, Italy
Interests: energy systems; energy efficiency; geothermal energy; numerical modeling; heat transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Alfonso Capozzoli

E-Mail Website
Guest Editor
Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: energy data analytics; intelligent buildings; coordinated building energy management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to present original studies demonstrating the importance of considering optimal control strategies in modern HVAC systems in order to improve energy management in buildings.

Given the importance of management techniques for integrated energy systems in buildings, such as advanced control approaches, this Special Issue welcomes contributions that discuss these topics.

Papers that investigate topics related to predictive and adaptive control strategies, such as the management of integrated energy systems, that expand upon the current knowledge on HVAC systems are welcome. Topics may include one or more of the following:

  • Energy conversion systems;
  • Heating and air conditioning systems;
  • Integrated energy systems in buildings;
  • Building energy consumption;
  • Building energy management;
  • Predictive control strategies;
  • Adaptive control strategies;
  • Reinforcement learning;
  • Technologies for renewable energy sources;
  • Thermofluid dynamics in indoor environments.

Please note that the above list is not exhaustive. Therefore, works focused on other research areas that are relevant to this Special Issue will also be considered.

Prof. Dr. Alessandro Mauro
Dr. Alfonso Capozzoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • building adaptive control
  • HVAC control
  • energy efficiency
  • model predictive control
  • reinforcement learning
  • energy management
  • integrated energy systems in buildings

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Published Papers (2 papers)

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Research

20 pages, 3485 KiB  
Article
Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator
by Davide Fop, Ali Reza Yaghoubi and Alfonso Capozzoli
Energies 2024, 17(20), 5117; https://doi.org/10.3390/en17205117 - 15 Oct 2024
Viewed by 643
Abstract
In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on [...] Read more.
In recent years, advanced controllers, including Model Predictive Control (MPC), have emerged as promising solutions to improve the efficiency of building energy systems. This paper explores the capabilities of MPC in handling multiple control objectives and constraints. A first MPC controller focuses on the task of ensuring thermal comfort in a residential house served by a heat pump while minimizing the operating costs when subject to different pricing schedules. A second MPC controller working on the same system tests the ability of MPC to deal with demand response events by enforcing a time-varying maximum power usage limitation signal from the electric grid. Furthermore, multiple combinations of the control parameters are tested in order to assess their influence on the controller performance. The controllers are tested on the BOPTEST framework, which offers standardized test cases in high-fidelity emulation models, and pre-defined baseline control strategies to allow fair comparisons also across different studies. Results show that MPC is able to handle multi-objective optimal control problems, reducing thermal comfort violations by between 66.9% and 82% and operational costs between 15.8% up to 20.1%, depending on the specific scenario analyzed. Moreover, MPC proves its capability to exploit the building thermal mass to shift heating power consumption, allowing the latter to adapt its time profile to time-varying constraints. The proposed methodology is based on technologically feasible steps that are intended to be easily transferred to large scale, in-field applications. Full article
(This article belongs to the Special Issue Optimal Control Strategy for HVAC System in Building Energy Management)
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23 pages, 5248 KiB  
Article
Optimizing Energy Efficiency with a Cloud-Based Model Predictive Control: A Case Study of a Multi-Family Building
by Angelos Mylonas, Jordi Macià-Cid, Thibault Q. Péan, Nasos Grigoropoulos, Ioannis T. Christou, Jordi Pascual and Jaume Salom
Energies 2024, 17(20), 5113; https://doi.org/10.3390/en17205113 - 15 Oct 2024
Viewed by 778
Abstract
The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation [...] Read more.
The Energy Performance of Buildings Directive (EPBD) has set a target to achieve carbon-neutral building stock and generate 80% of its electricity from renewable sources by 2050. While Model Predictive Control (MPC) can contribute significantly to energy flexibility in buildings, its remote implementation remains relatively unexplored, especially in the residential sector. The purpose of this research is to demonstrate the reliability, robustness, and computational efficiency of a cloud-based application of an MPC called Smart Energy Management (SEM) on a multi-family residential building. The SEM was tested on a virtual building model in TRNSYS using an open-source distributed event streaming platform for data exchange and synchronization. Simplified models for thermal behavior prediction, including an R3C3 model of the building, were developed in C++. The SEM was evaluated in eight scenarios with varying weather conditions, optimization criteria, and runtime periods. The results demonstrate that the SEM maintains stability and robustness over a 2-week period with a 15-minute planning resolution while ensuring thermal comfort. The C++ implementation of the optimization algorithm enables SEM deployment on low-spec servers, supporting cost-effective applications in real buildings with minimal intervention. Full article
(This article belongs to the Special Issue Optimal Control Strategy for HVAC System in Building Energy Management)
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