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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G: Energy and Buildings".

Deadline for manuscript submissions: closed (25 April 2022) | Viewed by 30167

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Special Issue Editors

Prof. Dr. Jin Woo Moon

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Guest Editor

School of Architecture and Building Science, Chung-Ang University, Seoul 06974, Korea
Interests: building environment and control; thermal comfort; energy efficiency; artificial intelligence; air quality
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Geun Young Yun

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Guest Editor

Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Interests: urban heat islands; energy and environment; adaptive comfort; occupant behavior
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jonghoon Ahn

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Guest Editor

School of Architecture and Design Convergence, Hankyong National University, Anseong 17579, Korea
Interests: energy convergence; building science; simulation model; artificial intelligence

Special Issue Information

Dear Colleagues,

The aims and scope of this special issue is located on the theoretical and practical methodologies that can build a futuristic foundation for better built environment without compromising both of the energy efficiency and human comfort. It can be investigated how to efficiently control indoor environment and provide optimized energy supply that have been studied in various energy-related fields. In particular, the smart solutions have been increasingly applied for the provision of the comfortable and effient built environment. This special issue will cover the diverse & cutting-edge theories and technologies which are relevant to this innovative soluations. Hopefully the usability of innovative viewpoints of several studies from this special issue can be further expanded in different and disciplanary areas including art, education, economy, policy, medicine as well as energy-inspired science.

Prof. Dr. Jin Woo Moon
Prof. Dr. Geun Young Yun
Prof. Dr. Jonghoon Ahn
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.

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Keywords

Human comfort
Occupant perception and behaviour
Indoor environment
Building energy and control efficiency
Building systems
HVAC system
Lighting system
Smart and dynamic envelope
Renewable energy and passive systems
Artificial intelligence
Artificial neural network
Fuzzy Logic
Genetic Algorithm
PID
Regression model
Deep learning
Machine learning
Big data
Predictive and adaptive control
Network-based control
IoT based building control

Published Papers (12 papers)

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Research

17 pages, 15490 KiB

Open AccessArticle

Optimization of Window Design for Daylight and Thermal Comfort in Cold Climate Conditions

by Tony-Andreas Arntsen and Bozena Dorota Hrynyszyn

Energies 2021, 14(23), 8013; https://0-doi-org.brum.beds.ac.uk/10.3390/en14238013 - 30 Nov 2021

Cited by 3 | Viewed by 1899

Abstract

Window design affects the overall performance of a building. It is important to include window design during the initial stages of a project since it influences the performance of daylight and thermal comfort as well as the energy demand for heating and cooling. The Norwegian building code facilitates two alternative methods for achieving a sufficient daylight, and only guidelines for adequate indoor thermal comfort. In this study, a typical Norwegian residential building was modeled to investigate whether the criteria and methods facilitate consistent and good performance through different scenario changes and furthermore, how the national regulations compare to European standards. A better insulated and more air-tight building has usually a lower annual heating demand, with only a marginal decrease in the daylight performance when the window design is unchanged. A more air-tight construction increases the risk of overheating, even in cold climates. This study confirms that a revision of the window design improves the overall performance of a building, which highlights the importance of proper window design. The pursuit of lower energy demand should not be at the expense of indoor thermal comfort considering the anticipated future weather conditions. This study indicates that criteria for thermal comfort and daylight, if clearly defined, can affect the energy demand for heating and cooling, as well as the indoor climate positively, and should be taken into account at the national level. A comparison between the national regulations and the European standards was made, and this study found that the results are not consistent. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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19 pages, 18998 KiB

Open AccessArticle

Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings

by Hossein Moayedi and Amir Mosavi

Energies 2021, 14(6), 1649; https://0-doi-org.brum.beds.ac.uk/10.3390/en14061649 - 16 Mar 2021

Cited by 32 | Viewed by 2456

Abstract

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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25 pages, 5227 KiB

Open AccessArticle

Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings

by Hossein Moayedi and Amir Mosavi

Energies 2021, 14(5), 1331; https://0-doi-org.brum.beds.ac.uk/10.3390/en14051331 - 01 Mar 2021

Cited by 33 | Viewed by 2478

Abstract

A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R² correlation = 0.977 and RMSE error = 0.183) and testing (R² correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R² correlation = 0.99 and RMSE error = 0.147) and testing (R² correlation = 0.99 and RMSE error = 0.148)). Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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14 pages, 3405 KiB

Open AccessArticle

Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

by Eun Ji Choi, Jin Woo Moon, Ji-hoon Han and Yongseok Yoo

Energies 2021, 14(3), 696; https://0-doi-org.brum.beds.ac.uk/10.3390/en14030696 - 29 Jan 2021

Cited by 8 | Viewed by 1680

Abstract

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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30 pages, 8798 KiB

Open AccessArticle

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

by Bernardo Calabrese, Ramiro Velázquez, Carolina Del-Valle-Soto, Roberto de Fazio, Nicola Ivan Giannoccaro and Paolo Visconti

Energies 2020, 13(22), 6104; https://0-doi-org.brum.beds.ac.uk/10.3390/en13226104 - 21 Nov 2020

Cited by 25 | Viewed by 4827

Abstract

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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15 pages, 4923 KiB

Open AccessArticle

Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season

by Sung Hoon Yoon and Jonghoon Ahn

Energies 2020, 13(22), 6023; https://0-doi-org.brum.beds.ac.uk/10.3390/en13226023 - 18 Nov 2020

Cited by 5 | Viewed by 1412

Abstract

For improving control methods in the thermal environment, various algorithms have been studied to satisfy the specific conditions required by the characteristics of building spaces and to reduce the energy consumed in operation. In this research, a network-based learning control equipped with an adaptive controller is proposed to investigate the control performance for supply air conditions with maintaining the levels of indoor thermal comfort. In order to examine its performance, the proposed model is compared to two different models in terms of the patterns of heating and cooling energy use and the characteristics of operational signals and overshoots. As a result, the energy efficiency of the proposed control has been slightly decreased due to the energy consumption increased by precise controls, but the thermal comfort has improved by about 10.7% more than a conventional thermostat and by about 19.8% more than a deterministic control, respectively. This result can contribute to the reduction of actual installation and maintenance costs by reducing the operating time of dampers and the energy use of heating coils without compromising indoor thermal comfort. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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19 pages, 6058 KiB

Open AccessArticle

Energy Conservation Potential of Economizer Controls Using Optimal Outdoor Air Fraction Based on Field Study

by Goopyo Hong, Chul Kim and Jun Hong

Energies 2020, 13(19), 5038; https://0-doi-org.brum.beds.ac.uk/10.3390/en13195038 - 24 Sep 2020

Cited by 5 | Viewed by 2462

Abstract

In commercial buildings, HVAC systems are becoming a primary driver of energy consumption, which already account for 45% of the total building energy consumption. In the previous literature, researchers have studied several energy conservation measures to reduce HVAC system energy consumption. One of the effective ways is an economizer in air-handling units. Therefore, this study quantified the impact of the outdoor air fraction by economizer control type in cooling system loads based on actual air-handling unit operation data in a hospital. The optimal outdoor air fraction and energy performance for economizer control types were calculated and analyzed. The result showed that economizer controls using optimal outdoor air fraction were up to 45% more efficient in cooling loads than existing HVAC operations in the hospital. The energy savings potential was 6–14% of the differential dry-bulb temperature control, 17–27% of the differential enthalpy control, 8–17% of the differential dry-bulb temperature and high-limit differential enthalpy control, and 16–27% of the differential enthalpy and high-limit differential dry-bulb temperature control compared to the no economizer control. The result of this study will contribute to providing a better understanding of economizer controls in the hospital when the building operates in hot-humid climate regions. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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17 pages, 3577 KiB

Open AccessArticle

Impact of Building Orientation on Daylight Availability and Energy Savings Potential in an Academic Classroom

by Aniela Kaminska

Energies 2020, 13(18), 4916; https://0-doi-org.brum.beds.ac.uk/10.3390/en13184916 - 19 Sep 2020

Cited by 5 | Viewed by 2756

Abstract

In this paper, daylight availability depending on building orientation in a typical educational classroom was investigated. Measurements of daylight illuminance distributions in the room depth for different illuminance outside the building allowed to determine the conditions when the luminaires in a classroom could be turned off, turned on, or dimmed. The outdoor daylight illuminance on the south-east and north façade of the building was recorded and the numbers of hours per year of university activity during which the lighting had to be switched on (up to 100% or brightened) were determined. Based on these numbers and luminaires powers the electricity consumption for lighting was estimated. It was proven that by using dimming control depending on daylight distribution in a room, comparable energy savings could be achieved for different building orientations. These savings of over 30% were greater than through the implementation of on/off control which, for a south-east oriented classroom reached about 28% and for a north-oriented one they were two times lower. Economic analysis showed payback time for dimming control around two years, which was longer than for on/off control. The electricity consumption estimated experimentally was also compared with the lighting energy numerical indicator (LENI) calculated according the standard EN 15193 1: 2017. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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20 pages, 6653 KiB

Open AccessArticle

Windcatcher Louvers to Improve Ventilation Efficiency

by Young Kwon Yang, Min Young Kim, Yong Woo Song, Sung Ho Choi and Jin Chul Park

Energies 2020, 13(17), 4459; https://0-doi-org.brum.beds.ac.uk/10.3390/en13174459 - 28 Aug 2020

Cited by 5 | Viewed by 2973

Abstract

Windcatcher louvers are designed to capture air flowing outside a building in order to increase its natural ventilation. There are no studies that have designed the shape of the louver to increase the natural ventilation efficiency of the building. This study aimed to conduct a computational fluid dynamics simulation and mock-up test of a Clark Y airfoil-type windcatcher louver designed to increase the natural ventilation in a building. The following test results were obtained. The optimal angle of attack of the airfoil was calculated via a numerical analysis, which demonstrated that the wind speed was at its highest when the angle of attack was 8°; further, flow separation occurred at angles exceeding 8°, at which point the wind speed began to decrease. The results of the mock-up test demonstrated that the time required to reduce the concentration of fine particles in the indoor air was 120 s shorter when the windcatcher was installed than when it was not, which indicating that the time to reduce particles represents a 37.5%reduction. These results can be seen as reducing the energy consumption of ventilation in the building because the natural ventilation efficiency is increased. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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17 pages, 4506 KiB

Open AccessArticle

Energy Consumption Prediction in Vietnam with an Artificial Neural Network-Based Urban Growth Model

by Hye-Yeong Lee, Kee Moon Jang and Youngchul Kim

Energies 2020, 13(17), 4282; https://0-doi-org.brum.beds.ac.uk/10.3390/en13174282 - 19 Aug 2020

Cited by 7 | Viewed by 2732

Abstract

In developing countries, energy planning is important in the development planning due to high rates of economic growth and energy demand. However, existing approaches of energy prediction, using gross domestic product, hardly demonstrate how much energy specific regions or cities may need in the future. Thus, this study seeks to predict the amount of energy demand by considering urban growth as a crucial factor for investigating where and how much energy is needed. An artificial neural network is used to forecast energy patterns in Vietnam, which is a quickly developing country and seeks to have an adequate energy supply. Urban growth factors, population, and night-time light intensity are collected as an indicator of energy use. The proposed urban-growth model is trained with data of the years 1995, 2000, 2005, and 2010, and predicts the light distribution in 2015. We validated the model by comparing the predicted result with actual light data to display the spatial characteristics of energy-consumption patterns in Vietnam. In particular, the model with urban growth factors estimated energy consumption more closely to the actual consumption. This spatial prediction in Vietnam is expected to help plan geo-locational energy demands. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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21 pages, 1019 KiB

Open AccessArticle

Comparison of Economic Feasibility for Efficient Peer-to-Peer Electricity Trading of PV-Equipped Residential House in Korea

by Min Hee Chung

Energies 2020, 13(14), 3568; https://0-doi-org.brum.beds.ac.uk/10.3390/en13143568 - 10 Jul 2020

Cited by 11 | Viewed by 1727

Abstract

Since the sharing economy emerged as a new paradigm with the development of technology, the global sharing economy market has grown rapidly. In the energy sector, peer-to-peer energy trading is being conducted to share energy produced through renewable energy systems. In this study, in the situation where energy transactions among individuals are expected to expand in the future, the types of buildings and trading to secure the economics of energy trading were compared. The types of buildings were limited to residential buildings, and the economic efficiency according to energy performance was compared. Because the government has strengthened energy performance regulations, the performance varied depending on the time of construction. Therefore, building types were divided into existing houses, new houses, and zero-energy houses. The trading types were compared to the existing methods, net-metering and feed-in tariff for small-scale distributed PV systems, with P2P trading. Thus, consuming only the amount of electricity in Tier 1 and trading the rest between individuals was the most economical strategy in residential buildings to which the progressive tariff system was applied. As the performance of a building improves, the more electricity that can be traded, and the wider the range for securing economic feasibility. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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21 pages, 8014 KiB

Open AccessArticle

Performance Evaluation of Control Methods for PV-Integrated Shading Devices

by Sung Kwon Jung, Youngchul Kim and Jin Woo Moon

Energies 2020, 13(12), 3171; https://0-doi-org.brum.beds.ac.uk/10.3390/en13123171 - 18 Jun 2020

Cited by 7 | Viewed by 1797

Abstract

This study aimed to develop a building-integrated photovoltaic (BIPV) device and optimal control methods that increase the photovoltaic (PV) efficiency and visual comfort of the indoor space. A louver-type PV-integrated shading device was suggested and an artificial neural networks (ANN) model was developed to predict PV electricity output, work plane illuminance, and daylight glare index (DGI). The slat tilt angle of the shading device was controlled to maximize PV electricity output based on three different strategies: one without visual comfort constraints, and the other two with visual comfort constraints: work plane illuminance and DGI. Optimal tilt angle was calculated using predictions of the ANN. Experiments were conducted to verify the system modeling and to evaluate the performance of the shading device. Experiment results revealed that the ANN model successfully predicted the PV output, work plane illuminance, and DGI. The PV-integrated shading device was more efficient in producing electricity than the conventional wall-mount PV systems, the control method without visual comfort constraints was most efficient in generating electricity than the other two with such constraints, and excluding the constraints resulted in less comfortable visual environment and reduced energy benefit. From the results analysis, it can be concluded that based on the accurate predictions, the PV-integrated shading device controlled using the proposed methods produced more electricity compared to the wall-mount counterpart. Full article

(This article belongs to the Special Issue Smart Built Environment for Health and Comfort with Energy Efficiency)

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