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Energies
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Optimization and Innovation of Nearly Zero-Energy Buildings
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Optimization and Innovation of Nearly Zero-Energy 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 (30 April 2022) | Viewed by 7405

Special Issue Editor

Dr. Bojan Milovanovic

E-Mail Website
Guest Editor
Department of materials, Faculty of Civil Engineering, University of Zagreb, 10000 Zagreb, Croatia
Interests: energy efficiency in building; building materials; building; building simulation; energy management; infrared thermography; sustainable architecture; built environment; sustainable construction; green building; building technology; concrete; nondestructive testing; concrete structures; materials for sustainable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nearly zero energy buildings (NZEBs) are required as the minimum standard for all new buildings in Europe by January 2021. NZEBs should, according to the Energy Performance of Buildings Directive (EPBD), be cost optimal, i.e., the cost of constructing and operating the building over its lifetime should be at its minimum. To achieve the goal of optimization- and innovation-driven NZEB, a lot of research is needed to accumulate knowledge and to utilize it in practical applications.

Optimizing construction processes to fully integrate available solutions (in the areas of process, technology, and contracting) is seen as the largest potential in lifecycle cost reduction and is of significant interest to this Special Issue.

The aim of this Special Issue is also to collect papers focused on new research results regarding optimization and innovation of nearly zero-energy buildings. Special focus should be given to accurate understanding of building performance, sustainable renovation of buildings, and enabling a more active role of buildings in a smart energy system.

We encourage submissions on applied results of research, present advanced modeling techniques, and innovative strategies and technologies, as well as papers addressing the challenges related to the NZEBs and the quality of NZEB construction.

Researchers that investigate the application of innovative approaches at neighborhood and district level, with the aim to maximize synergies in renovation work and processes, decrease costs, and optimize the use of energy-related shared district resources are also invited to publish papers in this Special Issue.

Dr. Bojan Milovanovic
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. 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

  • New building envelope materials and technologies for NZEBs
  • Innovative HVAC systems and control
  • Novel ventilation strategies
  • Thermal energy storage systems for NZEBs
  • Building information modeling for NZEBs
  • Advanced modeling techniques for NZEBs
  • Reduced performance gap
  • Deep renovation up to NZEB level
  • Cultural heritage buildings
  • Industrialized deep renovation
  • Innovative processes and technologies
  • Nearly zero energy neighborhoods

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Published Papers (2 papers)

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19 pages, 5957 KiB  
Article
Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement
by Sanjin Gumbarević, Bojan Milovanović, Bojana Dalbelo Bašić and Mergim Gaši
Energies 2022, 15(14), 5029; https://doi.org/10.3390/en15145029 - 9 Jul 2022
Cited by 3 | Viewed by 1536
Abstract
Transmission losses through the building envelope account for a large proportion of building energy balance. One of the most important parameters for determining transmission losses is thermal transmittance. Although thermal transmittance does not take into account dynamic parameters, it is traditionally the most [...] Read more.
Transmission losses through the building envelope account for a large proportion of building energy balance. One of the most important parameters for determining transmission losses is thermal transmittance. Although thermal transmittance does not take into account dynamic parameters, it is traditionally the most commonly used estimation of transmission losses due to its simplicity and efficiency. It is challenging to estimate the thermal transmittance of an existing building element because thermal properties are commonly unknown or not all the layers that make up the element can be found due to technical-drawing information loss. In such cases, experimental methods are essential, the most common of which is the heat-flux method (HFM). One of the main drawbacks of the HFM is the long measurement duration. This research presents the application of deep learning on HFM results by applying long-short term memory units on temperature difference and measured heat flux. This deep-learning regression problem predicts heat flux after the applied model is properly trained on temperature-difference input, which is backpropagated by measured heat flux. The paper shows the performance of the developed procedure on real-size walls under the simulated environmental conditions, while the possibility of practical application is shown in pilot in-situ measurements. Full article
(This article belongs to the Special Issue Optimization and Innovation of Nearly Zero-Energy Buildings)
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36 pages, 10736 KiB  
Article
Framework for Developing a Low-Carbon Energy Demand in Residential Buildings Using Community-Government Partnership: An Application in Saudi Arabia
by Mohammad AlHashmi, Gyan Chhipi-Shrestha, Kh Md. Nahiduzzaman, Kasun Hewage and Rehan Sadiq
Energies 2021, 14(16), 4954; https://doi.org/10.3390/en14164954 - 12 Aug 2021
Cited by 11 | Viewed by 5065
Abstract
Rapid population growth has led to significant demand for residential buildings around the world. Consequently, there is a growing energy demand associated with increased greenhouse gas (GHG) emissions. The residential building energy demand in arid countries such as Saudi Arabia is supplied with [...] Read more.
Rapid population growth has led to significant demand for residential buildings around the world. Consequently, there is a growing energy demand associated with increased greenhouse gas (GHG) emissions. The residential building energy demand in arid countries such as Saudi Arabia is supplied with fossil fuel. The existing consumption pattern of fossil fuels in Saudi Arabia is less sustainable due to the depletion of fossil fuel resources and resulting environmental impacts. Buildings built in hot and arid climatic conditions demand high energy for creating habitable indoor environments. Enormous energy is required to maintain a cool temperature in hot regions. Moreover, climate change may have different impacts on hot climatic regions and affect building energy use differently. This means that different building interventions may be required to improve the performance of building energy performance in these geographical regions, thereby reducing the emissions of GHGs. In this study, this framework has been applied to Saudi Arabia, a hot and arid country. This research proposes a community–government partnership framework for developing low-carbon energy in residential buildings. This study focuses on both the operational energy demand and a cost-benefit analysis of energy use in the selected geographical regions for the next 30 years (i.e., 2050). The proposed framework primarily consists of four stages: (1) data collection on energy use (2020 to 2050); (2) setting a GHG emissions reduction target; (3) a building intervention approach by the community by considering cost, energy, and GHG emissions using the Technique for Order of Performance by Similarity to the Ideal Solution (TOPSIS) to select the best combinations in each geographical region conducting 180 simulations; and (4) a clean energy approach by the government using grey relational analysis (GRA) to select the best clean energy system on the grid. The clean energy approach selected six different renewable power generation systems (i.e., PV array, wind turbine, hybrid system) with two storage systems (i.e., battery bank and a combination of electrolyte, fuel cell, and hydrogen tank storage). This approach is designed to identify the best clean energy systems in five geographical regions with thirty scenario analyses to define renewable energy-economy benefits. This framework informs through many engineering tools such as residential building energy analysis, renewable energy analysis, multi-criteria decision analysis (MCDA) techniques, and cost-benefit analysis. Integration between these engineering tools with the set of energy policies and public initiatives is designed to achieve further directives in the effort to reach greater efficiency while downsizing residential energy demands. The results of this paper propose that a certain level of cooperation is required between the community and the government in terms of financial investments and the best combinations of retrofits and clean energy measures. Thus, retrofits and clean energy measures can help save carbon emissions (enhancing the energy performance of buildings) and decrease associated GHG emissions, which can help policy makers to achieve low-carbon emission communities. Full article
(This article belongs to the Special Issue Optimization and Innovation of Nearly Zero-Energy Buildings)
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