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Digital Technology and Smart Buildings
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Digital Technology and Smart 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 November 2023) | Viewed by 10528

Special Issue Editor

Prof. Dr. Sehyun Park

E-Mail Website
Guest Editor
Department of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: smart energy; carbon neutrality; digital platform; AI-based data; digital twins; smart buildings and cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital technology and smart buildings are key elements for achieving a carbon-neutral and sustainable green society by 2050. In particular, due to the development of digital technologies such as the Internet of Things, big data, and artificial intelligence, buildings are rapidly changing into smart spaces. In addition, cloud-based platform technology has made buildings into a single digital product and provides a better life through the connection between buildings and cities. Furthermore, digital twin and energy data analysis technology have made it possible to use buildings as an object in establishing national strategies for carbon neutrality. In other words, various digital technologies are suggesting directions for the scalability and development potential of future smart buildings for carbon neutrality.

At this point, the direction of future building is clear. However, although various intelligent technologies are being applied to buildings, the practical verification of advanced technologies and smart services is still required in the fields of building management, lighting, energy, facilities, fire safety and security. Therefore, the purpose of this Special Issue is to exchange various ideas related to digital innovation technology applicable to buildings from an environmental, social and technological viewpoint. We invite you to submit interdisciplinary experiments and theoretical research on digital technologies and smart buildings. Research may include, but is not limited to, theoretical and empirical research on the following subjects:

  • Digital transformation for smart buildings;
  • Sustainable service in buildings;
  • The role of buildings for carbon neutrality;
  • Digital technology for smart buildings;
  • Cloud-based smart building platforms;
  • Digital-twin applications for smart building service;
  • Advanced building energy management systems;
  • AI-based automation control systems and algorithms for buildings;
  • Data analysis and prediction for smart buildings.

Prof. Dr. Sehyun Park
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

  • digital transformation
  • carbon neutrality
  • artificial intelligence
  • Internet of Things
  • big data analysis
  • smart digital platforms
  • digital twins
  • AI-based data and systems

Published Papers (5 papers)

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Research

16 pages, 18182 KiB  
Article
Machine Learning-Based Cost-Effective Smart Home Data Analysis and Forecasting for Energy Saving
by Sanguk Park
Buildings 2023, 13(9), 2397; https://doi.org/10.3390/buildings13092397 - 21 Sep 2023
Cited by 1 | Viewed by 2218
Abstract
This study aims to enable cost-effective Internet of Things (IoT) system design by removing redundant IoT sensors through the correlation analysis of sensing data collected in a smart home environment. This study also presents a data analysis and prediction technology that enables meaningful [...] Read more.
This study aims to enable cost-effective Internet of Things (IoT) system design by removing redundant IoT sensors through the correlation analysis of sensing data collected in a smart home environment. This study also presents a data analysis and prediction technology that enables meaningful inference through correlation analysis of data from different heterogeneous IoT sensors installed inside a smart home for energy efficiency. An intelligent service model that can be implemented based on a machine learning algorithm in a smart home environment is proposed. Herein, seven types of sensor data are collected and classified into sets of input data (six environmental data) and target data (power data of HVAC). By using the six new input data, the power data can be predicted by the artificial intelligence model. The model performance was measured using RMSE, and the gradient-boosting regressor (gb) model performed the best, with an RMSE of 22.29. Also, the importance of sensor data is extracted through correlation analysis, and sensors with low importance are removed according to the importance of sensor values. This process can reduce costs by 13%, thereby providing a design guide for a cost-effective IoT system. Full article
(This article belongs to the Special Issue Digital Technology and Smart Buildings)
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24 pages, 6102 KiB  
Article
Carbon-Neutral ESG Method Based on PV Energy Generation Prediction Model in Buildings for EV Charging Platform
by Guwon Yoon, Seunghwan Kim, Haneul Shin, Keonhee Cho, Hyeonwoo Jang, Tacklim Lee, Myeong-in Choi, Byeongkwan Kang, Sangmin Park, Sanghoon Lee, Junhyun Park, Hyeyoon Jung, Doron Shmilovitz and Sehyun Park
Buildings 2023, 13(8), 2098; https://doi.org/10.3390/buildings13082098 - 18 Aug 2023
Cited by 1 | Viewed by 1175
Abstract
Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based prediction models for photovoltaic (PV) energy, integrating models with carbon emission [...] Read more.
Energy prediction models and platforms are being developed to achieve carbon-neutral ESG, transition buildings to renewable energy, and supply sustainable energy to EV charging infrastructure. Despite numerous studies on machine learning (ML)-based prediction models for photovoltaic (PV) energy, integrating models with carbon emission analysis and an electric vehicle (EV) charging platform remains challenging. To overcome this, we propose a building-specific long short-term memory (LSTM) prediction model for PV energy supply. This model simulates the integration of EV charging platforms and offer solutions for carbon reduction. Integrating a PV energy prediction model within buildings and EV charging platforms using ICT is crucial to achieve renewable energy transition and carbon neutrality. The ML model uses data from various perspectives to derive operational strategies for energy supply to the grid. Additionally, simulations explore the integration of PV-EV charging infrastructure, EV charging control based on energy, and mechanisms for sharing energy, promoting eco-friendly charging. By comparing carbon emissions from fossil-fuel-based sources with PV energy sources, we analyze the reduction in carbon emission effects, providing a comprehensive understanding of carbon reduction and energy transition through energy prediction. In the future, we aim to secure economic viability in the building energy infrastructure market and establish a carbon-neutral city by providing a stable energy supply to buildings and EV charging infrastructure. Through ongoing research on specialized models tailored to the unique characteristics of energy domains within buildings, we aim to contribute to the resolution of inter-regional energy supply challenges and the achievement of carbon reduction. Full article
(This article belongs to the Special Issue Digital Technology and Smart Buildings)
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22 pages, 8256 KiB  
Article
Enhancing Zero-Energy Building Operations for ESG: Accurate Solar Power Prediction through Automatic Machine Learning
by Sanghoon Lee, Sangmin Park, Byeongkwan Kang, Myeong-in Choi, Hyeonwoo Jang, Doron Shmilovitz and Sehyun Park
Buildings 2023, 13(8), 2050; https://doi.org/10.3390/buildings13082050 - 11 Aug 2023
Cited by 1 | Viewed by 920
Abstract
Solar power systems, such as photovoltaic (PV) systems, have become a necessary feature of zero-energy buildings because efficient building design and construction materials alone are not sufficient to meet the building’s energy consumption needs. However, solar power generation is subject to fluctuations based [...] Read more.
Solar power systems, such as photovoltaic (PV) systems, have become a necessary feature of zero-energy buildings because efficient building design and construction materials alone are not sufficient to meet the building’s energy consumption needs. However, solar power generation is subject to fluctuations based on weather conditions, and these fluctuations are higher than other renewable energy sources. This phenomenon has emphasized the importance of predicting solar power generation through weather forecasting. In this paper, an Automatic Machine Learning (AML)-based method is proposed to create multiple prediction models based on solar power generation and weather data. Then, the best model to predict daily solar power generation is selected from these models. The solar power generation data used in this study was obtained from an actual solar system installed in a zero-energy building, while the weather data was obtained from open data provided by the Korea Meteorological Administration. In addition, To verify the validity of the proposed method, an ideal data model with high accuracy but difficult to apply to the actual system and a comparison model with a relatively low accuracy but suitable for application to the actual system were created. The performance was compared with the model created by the proposed method. Based on the validation process, the proposed approach shows 5–10% higher prediction accuracies compared to the comparison model. Full article
(This article belongs to the Special Issue Digital Technology and Smart Buildings)
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27 pages, 10573 KiB  
Article
Design and Implementation of a Futuristic EV Energy Trading System (FEETS) Connected with Buildings, PV, and ESS for a Carbon-Neutral Society
by Sangmin Park, SeolAh Park, Sang-Pil Yun, Kyungeun Lee, Byeongkwan Kang, Myeong-in Choi, Hyeonwoo Jang and Sehyun Park
Buildings 2023, 13(3), 829; https://doi.org/10.3390/buildings13030829 - 22 Mar 2023
Cited by 4 | Viewed by 1791
Abstract
To realize carbon neutrality, understanding the energy consumed in the building sector, which is more than that in other sectors, such as industry, agriculture, and commerce, is pivotal. Approximately 37% of energy consumption belongs to the building sector, and management of building energy [...] Read more.
To realize carbon neutrality, understanding the energy consumed in the building sector, which is more than that in other sectors, such as industry, agriculture, and commerce, is pivotal. Approximately 37% of energy consumption belongs to the building sector, and management of building energy is a critical factor. In this paper, we present an energy sharing scenario for energy stabilization, assuming that electric vehicles and their charging stations are widely distributed in the future. Consequently, fewer fuel cars will exist, and electric cars will become the major mode of transportation. Therefore, it is essential to install charging stations for electric vehicles in the parking lots of future buildings, and business models are expected to expand. In this paper, we introduce a future energy stabilization mechanism for peak power management in buildings and present a platform that entails connection-based energy trading technology based on a scenario. We also propose an energy supply strategy to prevent excess prices incurred due to peak consumption. Then, we analyzed the electricity bill for one month through scenario-based simulations of an existing building and the proposed system. When applying the proposed system, we derived a result that can reduce electricity rates by 38.3% (best case) to 78.5% (worst case) compared with the existing rates. Full article
(This article belongs to the Special Issue Digital Technology and Smart Buildings)
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23 pages, 14018 KiB  
Article
Factors Influencing Adoption of Digital Twin Advanced Technologies for Smart City Development: Evidence from Malaysia
by Ahsan Waqar, Idris Othman, Hamad Almujibah, Muhammad Basit Khan, Saleh Alotaibi and Adil A. M. Elhassan
Buildings 2023, 13(3), 775; https://doi.org/10.3390/buildings13030775 - 15 Mar 2023
Cited by 36 | Viewed by 3611
Abstract
Digital Twin Technology (DTT) has gained significant attention as a vital technology for the efficient management of smart cities. However, its successful implementation in developing countries is often hindered by several barriers. Despite limited research available on smart city development in Malaysia, there [...] Read more.
Digital Twin Technology (DTT) has gained significant attention as a vital technology for the efficient management of smart cities. However, its successful implementation in developing countries is often hindered by several barriers. Despite limited research available on smart city development in Malaysia, there is a need to investigate the possible challenges that could affect the effective implementation of DTT in the country. This study employs a mixed methodology research design, comprising an interview, a pilot survey, and the main survey. Firstly, we identified barriers reported in the literature and excluded insignificant factors through interviews. Next, we conducted an Exploratory Factor Analysis (EFA) on the pilot survey results to further refine the factors. Finally, we performed a Structural Equation Modeling (SEM) analysis on the main survey data to develop a model that identifies barriers to DTT implementation in smart city development in Malaysia. Our findings suggest the presence of 13 highly significant barriers, which are divided into four formative constructs. We found that personalization barriers are highly crucial, while operational barriers were less important for DTT implementation in smart city development in Malaysia. Full article
(This article belongs to the Special Issue Digital Technology and Smart Buildings)
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