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Advances in Optimization and Machine Learning in Indoor Environmental Quality...
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Advances in Optimization and Machine Learning in Indoor Environmental Quality and Energy in Buildings

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 7100

Special Issue Editors

Dr. Nikolaos Ploskas

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Interests: optimization; mathematical programming; machine learning; algorithms; decision support systems
Dr. Giorgos Panaras

E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of Western Macedonia, 50132 Kozani, Greece
Interests: energy efficient buildings; HVAC systems; solar thermal energy; indoor environmental quality; ventilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at including works applying optimization and machine learning techniques in assessing indoor environmental quality and/or its relationship with building energy systems. The abovementioned techniques, coupled with Internet of Things sensors, can be utilized in real-time monitoring of various aspects related to indoor environment and energy in buildings, thus allowing for intelligent self-learning procedures towards design and operation optimization. Within this context, works investigating the development and application of optimization and machine learning techniques on indoor environmental quality and/or energy systems in buildings are of interest.

Techniques of interest include classical mathematical programming methods, AI-based techniques, deep learning models, surrogate models, derivative-free methods, etc.

Topics of interest include indoor environmental quality components, indoor environment sensors, ventilation, HVAC systems, building energy systems, energy performance of buildings, etc.

Dr. Nikolaos Ploskas
Dr. Giorgos Panaras
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. Buildings is an international peer-reviewed open access monthly 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

  • indoor environmental quality
  • energy in buildings
  • optimization
  • machine learning
  • indoor air quality
  • thermal comfort
  • ventilation
  • HVAC systems
  • IoT sensors
  • intelligent systems

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

18 pages, 3115 KiB  
Article
Model Predictive Control for Energy Optimization of HVAC Systems Using EnergyPlus and ACO Algorithm
by Keivan Bamdad, Navid Mohammadzadeh, Michael Cholette and Srinath Perera
Buildings 2023, 13(12), 3084; https://doi.org/10.3390/buildings13123084 - 12 Dec 2023
Cited by 8 | Viewed by 3238
Abstract
The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case [...] Read more.
The deployment of model-predictive control (MPC) for a building’s energy system is a challenging task due to high computational and modeling costs. In this study, an MPC controller based on EnergyPlus and MATLAB is developed, and its performance is evaluated through a case study in terms of energy savings, optimality of solutions, and computational time. The MPC determines the optimal setpoint trajectories of supply air temperature and chilled water temperature in a simulated office building. A comparison between MPC and rule-based control (RBC) strategies for three test days showed that the MPC achieved 49.7% daily peak load reduction and 17.6% building energy savings, which were doubled compared to RBC. The MPC optimization problem was solved multiple times using the Ant Colony Optimization (ACO) algorithm with different starting points. Results showed that ACO consistently delivered high-quality optimized control sequences, yielding less than a 1% difference in energy savings between the worst and best solutions across all three test days. Moreover, the computational time for solving the MPC problem and obtaining nearly optimal control sequences for a three-hour prediction horizon was observed to be around 22 min. Notably, reasonably good solutions were attained within 15 min by the ACO algorithm. Full article
(This article belongs to the Special Issue Advances in Optimization and Machine Learning in Indoor Environmental Quality and Energy in Buildings)
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23 pages, 6882 KiB  
Article
Quantifying Energy Savings from Optimal Selection of HVAC Temperature Setpoints and Setbacks across Diverse Occupancy Rates and Patterns
by Riccardo Talami, Ilyas Dawoodjee and Ali Ghahramani
Buildings 2023, 13(12), 2998; https://doi.org/10.3390/buildings13122998 - 30 Nov 2023
Cited by 4 | Viewed by 3177
Abstract
With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption [...] Read more.
With the advent of flexible working arrangements, we are observing a dramatic shift in how buildings are occupied today, which presents an opportunity to optimize Heating, Ventilation, and Air Conditioning system temperature setpoints based on variations in occupancy. Guidelines often suggest the adoption of the highest or lowest setpoint or setback to minimize energy consumption in hot or cold climates, respectively. However, at outdoor temperatures where variations in occupancy heat loads prompt buildings to fluctuate across cooling, free-running, and heating mode, optimal setpoints and setbacks are not always the lowest or highest. In addition, the perturbations caused by rapid switching between setpoint and setback could diminish energy savings due to system destabilization. This paper aims to systematically compare the potential energy savings from fixed and optimal setpoints and setbacks across wide-ranging occupancy scenarios (four occupancy rates and 14 patterns). Energy simulations were conducted using the Department of Energy reference models for small, medium, and large office buildings to enable an exhaustive search of optimal setpoint/setbacks in 17 climate zones. Explored setpoints were 19.5 °C to 25.5 °C with intervals of 1 °C, and setbacks were 17 °C/19 °C for heating and 26 °C/28 °C for cooling. The findings indicate that, on average, while lower occupancy heat loads results in 5.48% energy reduction, a conventional fixed setpoint and setback strategy provides an additional 11.80%, and optimal selection of setpoints and setbacks could provide an additional 34.36–38.08%, emphasizing the untapped potential energy saving. To facilitate practical applications, this paper presents an interactive graphical interface: Optimal Temperature Setpoint Tool. Full article
(This article belongs to the Special Issue Advances in Optimization and Machine Learning in Indoor Environmental Quality and Energy in Buildings)
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