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Energy Management for Smart Buildings
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Energy Management for Smart Buildings

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 7047

Special Issue Editors

Prof. Dr. Ioana Fagarasan

E-Mail Website
Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computers, University “Politehnica” of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Interests: Systems theory with applications for hierarchical highly interconnected power systems; Modeling and simulation of industrial processes; Advanced control algorithms and structures with applications in the energy field; Intelligent systems for process management; Diagnosis and detection of faults in industrial processes; Advanced systems architectures for critical complex systems (smart grid, smart city, smart agriculture, environmental monitoring); Cyberphysical systems in the energy field
Dr. Grigore Stamatescu

E-Mail Website
Co-Guest Editor
Department of Automation and Industrial Informatics, Faculty of Automatic Control and Computers, University “Politehnica” of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
Interests: networked-embedded sensing; information processing; control engineering; building automation; smart city; data analytics; computational intelligence; industry and energy applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The adoption of new IT technologies driven by legislation and economic changes is reflected in the increased smartness of energy system applications and of buildings in particular. The role of buildings is changing from passively consuming energy to playing an active role in local grid control and optimization strategies, while focusing on occupant well-being through indoor environment monitoring.

Energy management solutions for buildings concern the efficient synchronization of distributed energy generation from renewable energy sources, energy storage, smart charging of electrical vehicles, vehicle to grid, smart metering or load shifting through demand response.

Innovative solutions that enable smart buildings to interact with their occupants and the grid in real time and to manage themselves efficiently, so as to become an active element of the energy system, are highly welcome.

Advances on intelligent and connected devices, smart sensors, and controllers integrated with building energy management systems (BEMS) are of great interest, alongside novel sensor data analytics approaches for efficient information extraction.

Control strategies and decision support systems for energy management in smart buildings can also be considered. The Special Issue welcomes quality submissions addressing both conceptual as well as technological approaches in the abovementioned areas of interest. The open access journal Energies (ISSN 1996-1073; 2.702 (2019)) is pleased to announce the launch of a new Special Issue entitled “Energy Management for Smart Buildings”. We are serving as Guest Editors for this issue.

The present Special Issue aims to present and highlight the advances and latest novel and emergent technology applications concerning energy management for smart buildings. It will provide a forum for the research community to share advances and new ideas regarding these technologies.

In recognition of your well-respected work, you are cordially invited to submit a paper to this Special Issue.

Papers may be submitted from now until 28 February 2022, as papers will be published on an ongoing basis. Submitted papers should not be under consideration for publication elsewhere.

In the hope that this invitation receives your favorable consideration, we look forward to our future collaboration.

Prof. Dr. Ioana Fagarasan
Dr. Grigore Stamatescu
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

  • Energy management
  • Smart buildings
  • Building energy services
  • Energy forecasting and anomaly detection for smart buildings
  • Internet of Things
  • Machine learning for smart building energy data
  • EVSE optimization
  • Grid integration and control

Published Papers (3 papers)

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Research

13 pages, 1839 KiB  
Article
Application of a Thermal Performance-Based Model to Prediction Energy Consumption for Heating of Single-Family Residential Buildings
by Tomasz Szul
Energies 2022, 15(1), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/en15010362 - 05 Jan 2022
Cited by 10 | Viewed by 1714
Abstract
Energy consumption for heating of single-family residential buildings is a basic item in energy balance and significantly affects their operating costs. Accuracy of heat consumption assessment in existing buildings to a large extent determines the decision on taking actions aimed at heat consumption [...] Read more.
Energy consumption for heating of single-family residential buildings is a basic item in energy balance and significantly affects their operating costs. Accuracy of heat consumption assessment in existing buildings to a large extent determines the decision on taking actions aimed at heat consumption rationalization, both at the level of a single building and at regional or national level. In the case of energy calculations for the existing buildings, a problem often arises in the form of lack of complete architectural and construction documentation of the analyzed objects. Therefore, there is a need to search for methods that will be suitable for rapid energy analysis in existing buildings. These methods should give satisfactory results in predicting energy consumption when there is limited access to data characterizing the building. Therefore, the aim of this study was to check the usefulness of a model based on thermal characteristics for estimating energy consumption for heating in single-family residential buildings. The research was conducted on a group of 84 buildings, for which the energy characteristics were determined based on the actual energy consumption. In addition, information was collected on variables describing these buildings in terms of construction technology and building geometry, from which the following were extracted for further calculations: cubic capacity, heated area, and year of construction. This made it possible to build a prediction model, which enables the application of a fast, relatively simple procedure of estimating the final energy demand index for heating buildings. The resulting calculations were compared with actual values (calculated from energy bills) and then evaluated according to the standards for evaluating model quality proposed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). In this way, it was possible to determine whether, in the absence of building documents, the indicative method gives good results when estimating the energy demand for heating single-family residential buildings. Full article
(This article belongs to the Special Issue Energy Management for Smart Buildings)
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15 pages, 3610 KiB  
Article
Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error
by Gustavo Felipe Martin Nascimento, Frédéric Wurtz, Patrick Kuo-Peng, Benoit Delinchant and Nelson Jhoe Batistela
Energies 2021, 14(24), 8325; https://0-doi-org.brum.beds.ac.uk/10.3390/en14248325 - 10 Dec 2021
Cited by 11 | Viewed by 2754
Abstract
Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption [...] Read more.
Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption. Full article
(This article belongs to the Special Issue Energy Management for Smart Buildings)
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20 pages, 10233 KiB  
Article
Long-Term Prediction of Weather for Analysis of Residential Building Energy Consumption in Australia
by Shu Chen, Zhengen Ren, Zhi Tang and Xianrong Zhuo
Energies 2021, 14(16), 4805; https://0-doi-org.brum.beds.ac.uk/10.3390/en14164805 - 06 Aug 2021
Cited by 3 | Viewed by 1825
Abstract
Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions [...] Read more.
Globally, buildings account for nearly 40% of the total primary energy consumption and are responsible for 20% of the total greenhouse gas emissions. Energy consumption in buildings is increasing with the increasing world population and improving standards of living. Current global warming conditions will inevitably impact building energy consumption. To address this issue, this report conducted a comprehensive study of the impact of climate change on residential building energy consumption. Using the methodology of morphing, the weather files were constructed based on the typical meteorological year (TMY) data and predicted data generated from eight typical global climate models (GCMs) for three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from 2020 to 2100. It was found that the most severe situation would occur in scenario RCP8.5, where the increase in temperature will reach 4.5 °C in eastern Australia from 2080–2099, which is 1 °C higher than that in other climate zones. With the construction of predicted weather files in 83 climate zones all across Australia, ten climate zones (cities)—ranging from heating-dominated to cooling-dominated regions—were selected as representative climate zones to illustrate the impact of climate change on heating and cooling energy consumption. The quantitative change in the energy requirements for space heating and cooling, along with the star rating, was simulated for two representative detached houses using the AccuRate software. It could be concluded that the RCP scenarios significantly affect the energy loads, which is consistent with changes in the ambient temperature. The heating load decreases for all climate zones, while the cooling load increases. Most regions in Australia will increase their energy consumption due to rising temperatures; however, the energy requirements of Adelaide and Perth would not change significantly, where the space heating and cooling loads are balanced due to decreasing heating and increasing cooling costs in most scenarios. The energy load in bigger houses will change more than that in smaller houses. Furthermore, Brisbane is the most sensitive region in terms of relative space energy changes, and Townsville appears to be the most sensitive area in terms of star rating change in this study. The impact of climate change on space building energy consumption in different climate zones should be considered in future design strategies due to the decades-long lifespans of Australian residential houses. Full article
(This article belongs to the Special Issue Energy Management for Smart Buildings)
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