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Energies
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Artificial Intelligence in Energy Efficient Buildings
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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: closed (31 August 2024) | Viewed by 8173

Special Issue Editors

Dr. Berk Ekici

E-Mail Website
Guest Editor
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

E-Mail Website
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
Prof. Dr. Z. Tuğçe Kazanasmaz

E-Mail Website
Guest Editor
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
Prof. Dr. Gülden Gökçen Akkurt

E-Mail Website
Guest Editor
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. 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

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Published Papers (3 papers)

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Research

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32 pages, 8446 KiB  
Article
Weather-Based Prediction of Power Consumption in District Heating Network: Case Study in Finland
by Aleksei Vakhnin, Ivan Ryzhikov, Christina Brester, Harri Niska and Mikko Kolehmainen
Energies 2024, 17(12), 2840; https://doi.org/10.3390/en17122840 - 9 Jun 2024
Cited by 1 | Viewed by 850
Abstract
Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak [...] Read more.
Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for heat losses in the pipeline. This helps to avoid exceedance of power plant capacity. The purpose of this study is to automate the process of building machine learning (ML) models to solve a short-term power demand prediction problem. The dataset contains a district heating network’s measured hourly power consumption and ambient temperature for 415 days. In this paper, we propose a hybrid evolutionary-based algorithm, named GA-SHADE, for the simultaneous optimization of ML models and feature selection. The GA-SHADE algorithm is a hybrid algorithm consisting of a Genetic Algorithm (GA) and success-history-based parameter adaptation for differential evolution (SHADE). The results of the numerical experiments show that the proposed GA-SHADE algorithm allows the identification of simplified ML models with good prediction performance in terms of the optimized feature subset and model hyperparameters. The main contributions of the study are (1) using the proposed GA-SHADE, ML models with varying numbers of features and performance are obtained. (2) The proposed GA-SHADE algorithm self-adapts during operation and has only one control parameter. There is no fine-tuning required before execution. (3) Due to the evolutionary nature of the algorithm, it is not sensitive to the number of features and hyperparameters to be optimized in ML models. In conclusion, this study confirms that each optimized ML model uses a unique set and number of features. Out of the six ML models considered, SVR and NN are better candidates and have demonstrated the best performance across several metrics. All numerical experiments were compared against the measurements and proven by the standard statistical tests. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Efficient Buildings)
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19 pages, 1854 KiB  
Article
Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast
by Oscar Villegas Mier, Anna Dittmann, Wiebke Herzberg, Holger Ruf, Elke Lorenz, Michael Schmidt and Rainer Gasper
Energies 2023, 16(19), 6980; https://doi.org/10.3390/en16196980 - 7 Oct 2023
Cited by 2 | Viewed by 1040
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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Review

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33 pages, 948 KiB  
Review
Energy Forecasting: A Comprehensive Review of Techniques and Technologies
by Aristeidis Mystakidis, Paraskevas Koukaras, Nikolaos Tsalikidis, Dimosthenis Ioannidis and Christos Tjortjis
Energies 2024, 17(7), 1662; https://doi.org/10.3390/en17071662 - 30 Mar 2024
Cited by 6 | Viewed by 3914
Abstract
Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between generation and consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) [...] Read more.
Distribution System Operators (DSOs) and Aggregators benefit from novel energy forecasting (EF) approaches. Improved forecasting accuracy may make it easier to deal with energy imbalances between generation and consumption. It also helps operations such as Demand Response Management (DRM) in Smart Grid (SG) architectures. For utilities, companies, and consumers to manage energy resources effectively and make educated decisions about energy generation and consumption, EF is essential. For many applications, such as Energy Load Forecasting (ELF), Energy Generation Forecasting (EGF), and grid stability, accurate EF is crucial. The state of the art in EF is examined in this literature review, emphasising cutting-edge forecasting techniques and technologies and their significance for the energy industry. It gives an overview of statistical, Machine Learning (ML)-based, and Deep Learning (DL)-based methods and their ensembles that form the basis of EF. Various time-series forecasting techniques are explored, including sequence-to-sequence, recursive, and direct forecasting. Furthermore, evaluation criteria are reported, namely, relative and absolute metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2), and Coefficient of Variation of the Root Mean Square Error (CVRMSE), as well as the Execution Time (ET), which are used to gauge prediction accuracy. Finally, an overall step-by-step standard methodology often utilised in EF problems is presented. Full article
(This article belongs to the Special Issue Artificial Intelligence in Energy Efficient Buildings)
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