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Energy Systems in Buildings
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Energy Systems in Buildings

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 4062

Special Issue Editor

Prof. Dr. Alireza Afshari

E-Mail Website
Guest Editor
Department of the Built Environment, Aalborg University, 9100 Copenhagen, Denmark
Interests: ventilation; indoor climate; energy; district heating; energy storage; control; building
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy is about addressing sustainable development in the environment, social, and economic dimensions. Energy systems are central to the functioning of our society, and they are primarily designed to supply energy services to end-users. The purpose of energy systems is to minimize energy losses, to optimize the use of sustainable energy sources, and to ensure the efficient use of energy. To achieve this purpose, it is of urgent concern to consider climate change, carbon emission reduction, and energy security.

The scope of this Special Issue includes the development of theories or technologies with clear links to energy efficiency, energy services, sustainable energy, and renewable energy technologies.

Prof. Dr. Alireza Afshari
Guest Editor

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

  • thermal energy storage
  • renewable energy
  • sustainable energy
  • energy services
  • energy sources

Published Papers (3 papers)

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Research

14 pages, 3479 KiB  
Article
Optimal Sizing and Management of Hybrid Renewable Energy System for DC-Powered Commercial Building
by Abdul Ghani Olabi, Rania M. Ghoniem, Abdul Hai Alami and Mohammad Ali Abdelkareem
Buildings 2023, 13(8), 2109; https://doi.org/10.3390/buildings13082109 - 21 Aug 2023
Viewed by 966
Abstract
DC power may be more efficient than AC power in certain applications, especially when it comes to local generation and storage. This is because AC power requires extra equipment to convert it to DC power, which can lead to energy losses. Using DC [...] Read more.
DC power may be more efficient than AC power in certain applications, especially when it comes to local generation and storage. This is because AC power requires extra equipment to convert it to DC power, which can lead to energy losses. Using DC power, on the other hand, makes it easier for devices to use it directly, resulting in higher energy efficiency. Additionally, using DC power can reduce equipment capital costs as it eliminates the need for additional AC–DC conversion equipment. Finally, DC power systems can offer new communication capabilities, including plug-and-play for generation and storage devices, making it simpler to integrate these systems into existing infrastructure. This paper analyzes the optimal size of a photovoltaic/PEM fuel cell system to supply a certain DC commercial load in NEOM city. To identify the best size of the PV/PEMFC, minimizing the cost of energy (COE) and minimizing the net present cost (NPC) are considered. The paper studies three sizes of PEMFCs: 15 kW, 20 kW, and 25 kW. In addition, five different PV modules are selected: Axitec 450 Watt, Jinko 415 Watt, REC Solar 410 Watt, Seraphim 310 Watt, and Tongwei 415 Watt. The results of the study confirmed that the best size of the hybrid system comprises a 15 kW PEMFC, a 267 kW Tongwei PV array, a 60 kg electrolyzer, and a 20 kg hydrogen tank. Under these conditions, the COE and NPC are 0.293 USD/kWh and 498,984 USD, respectively. Full article
(This article belongs to the Special Issue Energy Systems in Buildings)
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26 pages, 3185 KiB  
Article
Design of High-Performing Hybrid Ground Source Heat Pump (GSHP) System in an Educational Building
by Tianchen Xue, Juha Jokisalo, Risto Kosonen and Yuchen Ju
Buildings 2023, 13(7), 1825; https://doi.org/10.3390/buildings13071825 - 19 Jul 2023
Viewed by 1522
Abstract
Underground thermal imbalance poses a challenge to the sustainability of ground source heat pump systems. Designing hybrid GSHP systems with a back-up energy source offers a potential way to address underground thermal imbalance and maintain system performance. This study aims to investigate different [...] Read more.
Underground thermal imbalance poses a challenge to the sustainability of ground source heat pump systems. Designing hybrid GSHP systems with a back-up energy source offers a potential way to address underground thermal imbalance and maintain system performance. This study aims to investigate different methods, including adjusting indoor heating and cooling setpoints and dimensioning air handling unit (AHU) cooling coils, heat pump and borehole field, for improving the long-term performance of a hybrid GSHP system coupled to district heating and an air-cooled chiller. The system performance, life cycle cost and CO2 emissions were analyzed based on 25-year simulations in IDA ICE 4.8. The results showed studied methods can significantly improve the hybrid GSHP system performance. By increasing the AHU cooling water temperature level and decreasing indoor heating and cooling setpoints, the ground thermal imbalance ratio was reduced by 12 percentage points, and the minimum borehole outlet brine temperature was increased by 3 °C in the last year. However, ensuring long-term operation still required a reduction in GSHP capacity or an increase in the total borehole length. The studied methods had varying effects on the total CO2 emissions, while insignificantly affecting the life cycle cost of the hybrid GSHP system. Full article
(This article belongs to the Special Issue Energy Systems in Buildings)
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17 pages, 5825 KiB  
Article
A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings
by Yu Gong, Erzsébet Szeréna Zoltán and János Gyergyák
Buildings 2023, 13(5), 1167; https://doi.org/10.3390/buildings13051167 - 28 Apr 2023
Cited by 3 | Viewed by 988
Abstract
Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is [...] Read more.
Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects. Full article
(This article belongs to the Special Issue Energy Systems in Buildings)
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