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Artificial Intelligence in Energy Efficient Buildings

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 2285

Special Issue Editors

Department of Architecture, Izmir Institute of Technology, İzmir 35430, Turkey
Interests: performance-based design; computational design; self-sufficiency; high-rise buildings; artificial intelligence; machine learning; heuristic optimisation
Prof. Dr. I. Sevil Sariyildiz
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Guest Editor
Prof. Dr. Ir. I. Sevil Sariyildiz, Chair of Design Informatics, Faculty of Architecture and the Built Environment, Delft University of Technology, Julianalaan 134, 2628 BL Delft, The Netherlands
Interests: performance-based design; computational design, architecture
Special Issues, Collections and Topics in MDPI journals
Department of Industrial and Systems Engineering, 3301 Shelby Center, Auburn University, Auburn, AL 36849, USA
Interests: artificial intelligence; metaheuristics; swarm and evolutionary computation; architectural design optimisation; scheduling
Department of Architecture, Izmir Institute of Technology, Gülbahçe Kampüs, Urla, İzmir 35430, Turkey
Interests: daylight performance of buildings; architectural lighting in building physics; energy performance and its relation to building attributes
Department of Energy Systems Engineering, Izmir Institute of Technology, Gülbahçe Kampüs, Urla, İzmir 35430, Turkey
Interests: building energy performance; personalised thermal comfort; renewable energy systems

Special Issue Information

Dear Colleagues,

The International Energy Agency (IEA) has stated that buildings and the construction sector are responsible for almost one-third of global final energy consumption. For this reason, energy efficiency has become an inevitable necessity in buildings to achieve sustainable cities in the future. In this context, researchers should deal with the efficiency of the existing building stock, as well as forthcoming constructions. Although transforming existing buildings may require different strategies/actions to designing new buildings for achieving energy efficiency, recent applications of artificial intelligence (AI) in buildings suggest that swift and remarkable improvements in energy performance can be attained. Thanks to the data-driven approach of AI methods, the performance of the buildings can be enhanced in a wide array of ways, such as reducing the heating, cooling, and lighting energy consumption, etc. In this respect, we encourage researchers to contribute to this Special Issue entitled “Artificial Intelligence in Energy-Efficient Buildings” by considering novel methods and applications using either digital (e.g., building performance simulation) or empirical (e.g., real-time monitoring) data in areas including, but not limited to:

  • AI methods for swift and accurate energy performance evaluation in the conceptual design and building operation phases.
  • Machine learning for predicting building energy consumption (heating, cooling, lighting, HVAC).
  • Deep learning for building operation and occupancy behaviour.
  • Building energy optimisation with surrogate modelling.
  • Improving energy efficiency via building-integrated photovoltaics using machine learning and optimisation algorithms.
  • AI in the performative design of buildings.
  • AI tools, techniques, and methods in computational form-finding strategies.
  • AI in the performance of smart and liveable cities.

We are deeply saddened by the loss of Prof. Dr. M. Fatih Taşgetiren, a world-renowned expert on heuristic optimization and scheduling in operations research, who was one of the guest editors of the “Artificial Intelligence in Energy Efficient Buildings” special issue in Energies. We wish Prof. Taşgetiren rest in peace and present our condolences to the Taşgetiren Family, his colleagues, students and all his loved ones worldwide.

Dr. Berk Ekici
Prof. Dr. I. Sevil Sariyildiz
Prof. Dr. M. Fatih Tasgetiren
Prof. Dr. Z. Tuğçe Kazanasmaz
Prof. Dr. Gülden Gökçen Akkurt
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 energy efficiency
  •  building integrated photovoltaics
  •  surrogate modeling
  •  artificial intelligence
  •  optimization
  •  computational design
  •  building operation
  •  smart buildings and cities

Published Papers (1 paper)

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Research

19 pages, 1854 KiB  
Article
Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast
Energies 2023, 16(19), 6980; https://doi.org/10.3390/en16196980 - 07 Oct 2023
Cited by 1 | Viewed by 616
Abstract
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper [...] Read more.
Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Efficient Buildings)
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