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Smart Energy Buildings of the Future
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Smart Energy Buildings of the Future

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 39400

Special Issue Editors

Dr. Maria da Glória Gomes

E-Mail Website
Guest Editor
Department of Civil Engineering, Architecture and Georesources, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Interests: Building physics; Thermal and energy performance of buildings (laboratory and field experiments; modelling and simulation); Sustainable and energy efficient materials and solutions; Wind action and natural ventilation of buildings
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Silva

E-Mail Website
Co-Guest Editor
Department of Mechanical Engineering, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal
Interests: energy systems modelling and optimization; energy efficiency in buildings; intelligent energy management systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The building sector can provide an important contribution to mitigate climate change by reducing the energy demand and increasing the use of non-renewable energy sources. Therefore, innovative and high performing solutions to new and retrofitted building envelope and systems are needed to support the transformation towards net-zero energy and carbon buildings, whilst enhancing the indoor comfort conditions. This challenge has led to the search, not only for smart building materials and solutions to be integrated in the building envelope, but also for smart integration and management of building services and energy systems, with higher operability and performance than static solutions.

A deeper insight and understanding of smart solutions and applications in buildings is decisive for the definition of strategies, in a rational and technically informed way, to meet the energy and climate-neutral targets for the buildings of the future.

This Special Issue intends to provide an overview of the existing knowledge related with various aspects of Smart Energy Buildings of the Future.

Original research, theoretical and experimental, case studies, and comprehensive review papers are invited for possible publication in this special issue. Relevant topics to this special issue include, but not limited to the following subjects:

  • Smart building envelope materials
  • Adaptive and intelligent building envelope solutions
  • Building thermal and energy modelling and simulation of smart solutions
  • Lab test procedures and methods of field measurement to assess the performance of smart materials and building solutions
  • Smart prediction and validation of energy demand and/or renewable energy generation in existing and future buildings
  • Design and validation of control-oriented architectures for building management (e.g. predictive control, model-based control, reinforcement learning)
  • Integration of buildings management in energy smart-grid contexts (electricity or district-heating and cooling)
Prof. Maria da Glória Gomes
Prof. Carlos Silva
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. Applied Sciences 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 2400 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

  • Smart materials,
  • adaptive and intelligent buildings,
  • energy generation and demand forecast,
  • intelligent building control

Published Papers (3 papers)

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Research

40 pages, 163443 KiB  
Article
Kinetic Solar Envelope: Performance Assessment of a Shape Memory Alloy-Based Autoreactive Façade System for Urban Heat Island Mitigation in Athens, Greece
by Christina Koukelli, Alejandro Prieto and Serdar Asut
Appl. Sci. 2022, 12(1), 82; https://doi.org/10.3390/app12010082 - 22 Dec 2021
Cited by 6 | Viewed by 3559
Abstract
The paper explores the potentials of shape memory alloys (SMAs) for the design of autoreactive façade systems without using additional external energy. The exploration is conducted and assessed through the design of a façade concept for the city of Athens in Greece, aiming [...] Read more.
The paper explores the potentials of shape memory alloys (SMAs) for the design of autoreactive façade systems without using additional external energy. The exploration is conducted and assessed through the design of a façade concept for the city of Athens in Greece, aiming to improve both the indoor and outdoor environment by means of a kinetic autoreactive system featuring a dual-seasonal function, with a focus on the building’s direct and indirect impact on the urban heat island (UHI) effect. The paper covers a demonstration of the methodology followed, using a feedback-loop logic informed by environmental and energy performance evaluation studies in Grasshopper to optimize the geometry and movement of the shading component. During the façade design process, a comprehensive and systematic computational toolset is being developed, targeted on the abovementioned performance evaluation studies. Through the development and assessment of the façade concept, the objective is to explore the potentials and limitations for the application of autoreactive envelopes in the façade design. At the same time, the aim is to exploit the possibilities and optimization potentials offered through the developed iterative computational workflows, by showcasing the methodology and interoperability logic of the digital tools used for the data interchange. Full article
(This article belongs to the Special Issue Smart Energy Buildings of the Future)
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20 pages, 5655 KiB  
Article
Smart Trash Bin Model Design and Future for Smart City
by Jun-Ho Huh, Jae-Hyeon Choi and Kyungryong Seo
Appl. Sci. 2021, 11(11), 4810; https://doi.org/10.3390/app11114810 - 24 May 2021
Cited by 13 | Viewed by 32362
Abstract
The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this [...] Read more.
The trash disposal system, using standard trash bags, has been adopted by the government of the Republic of Korea (ROK) for more than two decades. This has caused a sanitary problem, as well as some secondary pollution. It is possible to solve this problem by deploying more manpower, but considering the manpower and maintenance costs that impose a heavy burden on the local governments who are experiencing tight financial situations, it would not be feasible. Thus, an Internet of Things (IoT)-based Smart Trash Separation Bin model that can reduce the cost of trash separation work has been proposed in this paper. The three efficient designs that respectively use a sensor, image processing, or spectroscope technology are presented. These IoT-based designs can bring significant merit to reducing the manpower costs, as well as the administrative cost involved. Full article
(This article belongs to the Special Issue Smart Energy Buildings of the Future)
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19 pages, 1066 KiB  
Article
A Novel Load Forecasting Approach Based on Smart Meter Data Using Advance Preprocessing and Hybrid Deep Learning
by Fatih Ünal, Abdulaziz Almalaq and Sami Ekici
Appl. Sci. 2021, 11(6), 2742; https://doi.org/10.3390/app11062742 - 18 Mar 2021
Cited by 15 | Viewed by 2617
Abstract
Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the [...] Read more.
Short-term load forecasting models play a critical role in distribution companies in making effective decisions in their planning and scheduling for production and load balancing. Unlike aggregated load forecasting at the distribution level or substations, forecasting load profiles of many end-users at the customer-level, thanks to smart meters, is a complicated problem due to the high variability and uncertainty of load consumptions as well as customer privacy issues. In terms of customers’ short-term load forecasting, these models include a high level of nonlinearity between input data and output predictions, demanding more robustness, higher prediction accuracy, and generalizability. In this paper, we develop an advanced preprocessing technique coupled with a hybrid sequential learning-based energy forecasting model that employs a convolution neural network (CNN) and bidirectional long short-term memory (BLSTM) within a unified framework for accurate energy consumption prediction. The energy consumption outliers and feature clustering are extracted at the advanced preprocessing stage. The novel hybrid deep learning approach based on data features coding and decoding is implemented in the prediction stage. The proposed approach is tested and validated using real-world datasets in Turkey, and the results outperformed the traditional prediction models compared in this paper. Full article
(This article belongs to the Special Issue Smart Energy Buildings of the Future)
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