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Dear Colleagues,

We are delighted to invite you to contribute a research or review paper for this Collection on reducing the energy needs of buildings, improving building energy efficiency, and improving how energy is managed in buildings. The main topics are presented below:

Solar and other renewable energy sources in buildings;
Intelligent buildings;
Data science (AI, machine learning) for building energy management;
Zero-energy, low-energy, and carbon-neutral buildings;
Life-cycle energy efficiency of buildings and embodied energy;
Life-cycle assessment (LCA) of buildings;
Building physics as applied to energy;
Energy consumption in buildings;
Building energy demand management;
District heating and cooling;
Heat recovery systems for buildings;
Building energy storage;
Lighting and other factors related to energy;
Smart energy scheduling in hospitals and public buildings.

Prof. Dr. Anastasios Dounis
Dr. Paulo Santos
Guest Editors

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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.

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Research

31 pages, 4482 KiB

Open AccessArticle

In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures

Iole Nardi

and

Elena Lucchi

Energies 2023, 16(8), 3319; https://doi.org/10.3390/en16083319 - 07 Apr 2023

Cited by 6 | Viewed by 1704

Abstract

Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and in laboratory, have been published, focusing on theory, equipment, metrological performance, test conditions and data acquisition, data analysis, benefits, and limitations. However, steps forward have been done and not been deepened in previous works: in fact, the representative points method and the weighted area method have been proposed, too, whilst artificial intelligence and data-driven methods have begun to prove the reliability also in the U-value prevision using available datasets. Considering this context, this work aims at updating the literature background considering exclusively in situ methods. The work starts from bibliometric and scientometric analysis not previously conducted: this helped to group the methods and to sketch the innovations and the future perspectives. Indeed, from the bibliometric and scientometric literature analysis what emerged was (i) the richness of the background on this topic, especially in the recent years, (ii) two macro-groups (methods with and without measurements), and (iii) the importance of paper keywords (otherwise, interesting papers are eluded by the output of simple database queries). The method study that followed aims at providing (i) a broader view of the thermal transmittance (U-value) assessment procedures, including the utmost recent applications, proposal, and outlooks in this field, (ii) the understanding on the fundamental theories of the techniques, (iii) practical advice for building-envelope assessment, focusing on the advantages and limitations useful for professionals and researchers involved in the energy audit, conservation, or refurbishment of building stock, (iv) the identification of the interconnection between the techniques that often rely on one another, and (v) final remarks and future perspective of the procedures, which embrace the use of artificial intelligence (AI). From the topic analysis, as a result, it emerged that this is an open field for future research, especially with the implementation of AI, which requires good datasets and trials on the models’ architectures, in terms of input layer, number of hidden layer and neurons, and percentage of data to be employed for model training and testing. Full article

(This article belongs to the Special Issue Feature Paper Collection: Energy and Buildings)

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