Online ISSN 2759-8578 Print ISSN 2189-7972 indexed in J-Stage https://www.jstage.jst.go.jp/browse/aiue/ The Journal of Asian Urban Environment is a scholarly publication dedicated to the study and analysis of urban environments in Asia. It focuses on various aspects of urban life, including sustainable development, urban planning, environmental challenges, and policy solutions specific to Asian cities. The journal provides a platform for researchers, policymakers, and practitioners to share insights, case studies, and innovative approaches to address the unique urban issues faced by Asian countries. It aims to contribute to the advancement of knowledge and the promotion of sustainable urban practices in the region.
jaue2021-086: Application of energy flexible buildings and grid interactive building under demand response
Increasing renewable penetration poses challenge to the grid manager, such as grid stability, renewable curtailment. With the advance of information communication technology, aggregated energy prosumers is expected to play an vital role in real time energy balance with optimal demand side management. This paper reviews the development of energy flexible building and grid interactive building. Firstly, describes the definition and characteristics of the energy flexible building and grid interactive buildings, the main optimized control techniques are introduced. Then the main assessment indicators for building energy flexibility are proposed. Finally, we proposed the suggestions for better exploration of demand side flexibility.
jaue2021-085: Study on the influencing factors of indoor temperature distribution of directly beneficial buildings in areas of high altitude and intense radiation
Through the experimental analysis of Tibet Lhasa city Chengguan Town indoor temperature,we found that in the daytime as the solar radiation and outdoor temperature rising, the south room of the directly benefit building in the areas of high latitude and intense radiation show varying degrees of temperature fluctuation characteristics: The closer you get to the south window, the greater the indoor vertical temperature gradient, the greater the temperature fluctuations.There are also great differences in the temperature distribution in the depth direction. During the daytime, the maximum vertical temperature difference in the southern area can exceed 7.5℃, while most of the vertical temperature difference in the northern area is within 3℃. In order to analyze the influencing factors of indoor non-uniform temperature distribution in plateau area, amultiple regression model was established to analyze the influence of key outdoor meteorological parameters on indoor inhomogeneous thermal environment. The results show that solar radiation has the greatest influence on indoor inhomogeneous thermal environment compared with temperature. Therefore, in Tibetan areas, the potential of rational setting of transparent envelope to improve the indoor thermal environment is far greater than that of improving the thermal insulation of non-transparent envelope.
jaue2021-084: Hierarchical Ni-Co nitride nanosheets as a highly efficient and stable electrocatalyst for hydrogen evolution
Hydrogen, generated by electrocatalytic process, is one of the most promising sources of green and sustainable energy. Herein, we prepared a three-dimensional Ni-Co nitride nanosheet catalyst via electrodeposition process uniformly supported on carbon cloth (CC), which serves as an eminently active and durable hydrogen evolution reaction (HER) catalyst for alkaline water electrolysis. The effect of the electrodeposition time on the performance of the NiCoN(15)/CC catalyst was investigated, and it was demonstrated that the optimized NiCoN(15)/CC catalyst displays an excellent catalytic performance and stability. The electrodes can have very low overpotential of 72 mV for HER in 1.0 M KOH at a low current density of 10 mA cm-2. The high electrocatalytic activity of NiCoN(15)/CC is attributed to the excellent morphology and exposed active crystal plane.
jaue2021-083: Optimally sizing and economic analysis of energy storage system in home energy management system
With the popularization of distributed generation and renewable energy technology, the demand side management of residential energy system has been further developed. Aiming at the problems of low operation efficiency and insufficient system security in residential distributed system, the application of energy storage technology can realize peak shaving and valley filling, improve the system reliability and stability. On this basis, the rational utilization of energy storage equipment and the coordinated operation with other energy supply equipment have become the key to improve the system economy and flexibility from the user side. In this study, an independent house in “Jono zero carbon advanced community” in Japan is selected as the research object. Based on the measured energy consumption data of one year, energy storage equipment is introduced to optimize the system. Four cases are designed according to the introduction of different energy storage devices, and an optimization model is established based on adaptive particle swarm optimization(APSO) algorithm. The results indicate that energy storage equipment can optimize the economy of residential energy system to varying degrees.
jaue2021-082: The capacity credit analysis of PV and wind energy and its impacts on Japan power grid
With the increasing proportion of renewable energy represented by photovoltaics (PV) and wind in the power grid, the existing power grid has faced some new challenges due to its intermittent and uncontrollable characteristics. It is important to evaluate the impact of PV and wind on the public electricity supply system when they are introduced into grid. We presented a new method to predicts the maximum penetration of renewable energy and its impact on the public electricity supply system from both qualitative and quantitative perspectives. Based on the method of residual load duration curve (RLDC), the new method has combined a renewables capacity credit analysis approach and dynamic investment payback period (DIPP) to explain the impacts on the reduction of peak load demand and VRE curtailment. The real data of Kyushu and Hokkaido of Japan power grid are used. Results indicate that the capacity credit has being increasing with the increase of PV and wind shares in the grid. However, it seems to be saturated when the share of PV and wind power in the grid reached at 25% and 60%, respectively. Compared with the wind integration in Hokkaido, the PV penetration in Kyushu reaches saturation more rapidly.
jaue2021-081: A study on the properties of modified fly ash produced from three kinds of coal by flotation method
So far, there are still various problems in expanding the use of fly ash for concrete. Due to the different treatment methods used in coal production areas and power plants, the physical properties and quality of fly ash are also very different, and the impact of fly ash on concrete also depends on these factors. Therefore, when expanding the use of fly ash for concrete, it is necessary to understand the influence of fly ash on concrete in various countries and regions, and to stabilize the quality of fly ash by controlling the quality of fly ash. Therefore, this paper mainly studies the various properties of fly ash in different countries.
jaue2021-080: Study on The Determinants of University Artificial Lighting Based on Natural Daylighting
A large part of the energy consumption expenditure of university building classrooms is classroom artificial lighting. Students' habitual behavior of using artificial lighting in classrooms has a great impact on building energy consumption in universities. Based on the questionnaire survey of 154 students in a university, this paper analyzes the results of some problems about artificial lighting, through the actual measurement and analysis of the lighting conditions of two typical north and south classrooms in a teaching building of the University, and the simulation and analysis of the lighting conditions of typical classrooms in other three types of teaching buildings of the University by using Ecotect analysis software, combined with the collection of documents on students' psychological behavior habits. It is concluded that most of the reasons why students use classroom artificial lighting are students' daily habit of turning on the light, but there are also a small part of the reasons are insufficient lighting in the classroom. It is suggested that universities can establish an artificial lighting control system for classrooms divided into north-south and far-near the windows.
jaue2021-079: The fault detection and diagnosis (FDD) for deep belief network (DBN) - based AHUs using data feature selection
The fault detection and diagnosis (FDD) for air handling units (AHUs) often impact greatly on building energy efficiency and indoor environmental quality. To better deal with the complexity of FDD for AHU, a deep learning data-driven technology is indispensable. In this paper, a deep belief network (DBN) - based AHU FDD model is developed. The model training and testing are implemented using the preprocessed feature set from the ASHRAERP- 1312 summer experimental data. The FDD accuracy of up to 99.89% shows the high effectiveness of the given DNB. Then, to reduce the number of features while still maintain their characteristics, two data feature selection methods, Max-relevance and min-redundancy (MRMR) and Maximal information coefficient (MIC) are employed respectively to provide feature subsets for the DBN model. The results indicate that both MIC and MRMR have excellent capabilities for attribute filtering, yet MRMR is more prominent for considering the redundancies among features. In addition, compared with the traditional SVM, the proposed DBN model performs better for the higher accuracy, especially for the fast calculation speed.
jaue2021-078: Deep reinforcement learning TD3 method for optimal operation of distributed energy system
Distributed energy system (DES) has attracted more and more attention because of its characteristics of efficiency, reliability, flexibility and environmental protection, and has become an important direction of energy development in China in the future. The economy of a distributed energy system depends on its operation strategy and scheduling. This paper proposes a distributed energy system operation optimization based on deep reinforcement learning method. In detail, the optimization problem is transformed into a Markov decision process, combined with the theory of reinforcement learning, and the Twin Delayed Deep Deterministic policy gradient (TD3) algorithm of deep reinforcement learning (DRL) is applied to optimize a distributed energy system combining solar photovoltaic generation and battery storage. The historical data of energy consumption and solar radiation are provided for training the deep reinforcement learning model. After training, the model can make a quick optimization decision in a very short time. The proposed method is compared with the operation results obtained by using GUROBI solution software and other two DRL methods, and the results show that the proposed method has a good adaptability to the operation optimization of distributed energy systems.
jaue2021-077: Utilization of virtual energy storage based on time-of-use tariffs
With the share of renewable energy generation increasing year by year, demand-side flexibility resources become more important. In this article, two metrics are defined to illustrate the valley-filling capacity and peak regulation capacity of building virtual energy storage in terms of vally-filling power (Pvally-filling) and peak-shaving power (Ppeakshaving), respectively. The article uses the building energy simulation software EnergyPlus to simulate a three-story office building and quantifies the available capacity of the building's virtual energy storage by setting schedules to realize the utilization of the building's virtual energy storage. The electricity consumption and electricity bill reduction after considering the implementation of the building virtual energy storage strategy are also calculated based on the time-of-use tariff setting schedule. The simulation results show that the demand response event of VES1 is consistent with the first demand response event of VES2, where the valley-filling power is 23.62 kW and the peak-shaving power is 17 kW. the second demand response event of VES2 has a valley-filling power of 33.1 kW and a peak-shaving power of 23.4 kW. The economic index is 1.2% for VES1 and 2.8% for VES2. 2.8%.
jaue2021-076: Deep-learning-based fault diagnosis method for centrifugal chillers under imbalanced dataset condition
Data-driven fault diagnosis technology for energy-supplying systems can quickly and accurately detect and diagnose the system faults, effectively avoiding energy waste and keeping system healthy. Due to the low probability of system faults, the training dataset becomes imbalanced, which will significantly influence the diagnosis accuracy. This paper proposes a novel method based on deep learning for centrifugal chillers fault diagnosis, which firstly converts the system operating data into Gramian Angular Field (GAF) images and uses Convolutional Neural Network (CNN) to classify constructed images according to different operating conditions. In order to solve the imbalanced dataset problem, this paper adopts conditional Wasserstein Generation Adversarial Network with gradient penalty (CWGAN-GP) method to expand the fewer samples to balance the training dataset. The result shows that the proposed method has 89.80% diagnosis accuracy for typical faults of centrifugal chillers，much higher than general machine learning methods. After balancing the training dataset through the method of generating fewer samples, the diagnostic accuracy will be obviously improved, reaching more than 96%, which is more suitable for actual engineering situation.
jaue2021-075: A deep transfer learning model for short-term building energy consumption prediction based on Bayesian optimization
As the foundation of building energy management, efficient and accurate building energy prediction plays a vital role in the operation and optimization of energy system. In recent years, load forecasting models with deep neural networks have gained great attention due to their powerful mapping ability to complex nonlinear relationships. However, these models require a multitude of historical data from predicted buildings for training, which are difficult to acquire for newly constructed buildings or old buildings lacking measurement data. In order to assist in energy consumption prediction of target buildings with insufficient data, this paper proposes a deep transfer learning energy consumption prediction model using long short-term memory (LSTM). LSTM is used to extract the temporal features of energy consumption data of the source and target buildings. The transfer learning model trained by the source building data can assist in predicting the energy consumption of the target buildings. In addition, Bayesi an optimization is used to adjust the hyperparameters to further improve the prediction accuracy of the target building. The experimental results demonstrate that the proposed model achieves better performance than the benchmark models used for comparison.
jaue2021-074: Effects of Residential Area Design Elements on Air Temperature in Xi'an Based on Field Measurement
In recent years, as the urban heat island effect has intensified, it has become a hot topic of concern for cities around the world. The development of cities influences the change of urban climate, and as residential land is the main type of urban land use, the process of urbanization inevitably leads to the development of a large amount of residential land. The development and design methods of residential areas affect the micro-climate changes in residential areas, thereby further affecting the climate of the city. The temperature in the residential area is a key indicator for evaluating outdoor thermal comfort. In this study, the environmental monitoring of a residential area in Xi'an City was used to collect the air temperature for a period of one day. Geographic information system models and multiple regression models were established. The results show that the temperature is affected by a variety of factors. Every time the building occupancy rate increases by 0.1, the whole-day average temperature, daily average temperature and nighttime average temperature will decrease 0.39°C, 0.63°C and 0.19°C respectively. Livable. This study can provide guidance for future settlement development and design.
jaue2021-073: Study on Introduction of a Woody Biomass CHP to an Evacuation Center in a Mountainous Area
There have been many large-scale earthquakes in Japan. It took a long time for lifelines to be restored in the disaster areas, so that sanitation deteriorated in evacuation centers. In recent years, Japan has been introducing a small Combined Heat and Power (CHP) fueled by woody biomass, and the introduction of this system to evacuation centers will provide heat and electricity in times of disaster and help improve sanitation in evacuation centers. In this study, we suggest the introduction of a biomass CHP to an evacuation center in a mountainous area in order to make the energy independent in times of disaster and verified the effectiveness of the system in Kumano City, Mie Prefecture, both in daily life and in times of disaster. As a result of verifying the effect of the daily system, when four units with an electrical output of 45 kW and a thermal output of 105 kW were operated in parallel, the initial investment was recovered and LCCO2 was reduced by more than 90%. It was also found that the operation of a single biomass CHP unit met the electricity demand of the evacuation center, and the surplus electricity is used to charge EVs to meet the outlet demand at other evacuation centers in the region. In addition, the heat was sufficient for evacuees to use the showers in the evacuation center.
jaue2021-072: A Study on The Effects of Electric Vehicle Expansion on Electric Load Profile of Apartments in South Korea
As concerns about climate change and carbon emissions increase, electric vehicles are becoming increasingly popular. Consequently, the demand for electricity for charging electric vehicles at home will increase accordingly. The electric load profile of apartments may be altered due to an unprecedented demand for electricity, leading to problems such as grid unstability and increased uncertainty regarding electric load peak. Therefore, in this study, the effect of the expansion of electric vehicles on the electric load profile in apartment houses was examined using statistical data. In consequence, if electric vehicles constitute 30% of all vehicles, the apartment's electric load consumption can increase by up to 108.36%, with an average increase of 40.32%.
jaue2021-071: A Short-term Electric Load Forecasting Method Based on Dual-stage Attention Mechanism and Long Short-Term Memory Neural Network
Short-term electric load forecasting is the basis for reasonable dispatch and smooth operation of power grids. In order to improve the accuracy of load forecasting, a dual-stage attention mechanism was introduced to long shortterm memory (LSTM) as the predictive model, to extract association rules between input features and time dependencies between history time points. The feature attention mechanism was used for mining the relationship between the target parameter and other state information automatically to correct prediction result appropriately, which also overcomes the limitation by the preset threshold in traditional association rule mining algorithm. The temporal attention mechanism independently selects the key time points of historical information, and further enhances the information expression of key time points on the basis of the LSTM time series model, and improves the stability of the prediction effect for a longer period of time. Taking the load data of a school as an actual calculation example, the results show that this model is more accurate than other models. Furthermore, by using feature attention mechanism, the model can track how input features influence outputs at each step of inference.
jaue2021-070: Evaluation of Carbon Reduction Potential through Improvement of Heat Transmission Coefficient at University Buildings
The warming caused by human activities is showing a trend of increasing the temperature by 0.2℃ per decade. So, countries around the world have declared for ‘2050 carbon neutrality’ to control global temperature rise. The greenhouse gas emissions of university campus buildings, the subject of this study, account for 12.6% of the building's greenhouse gas emissions. If the exterior material of the university building is improved, the potential for carbon emission reduction will be great. This study is aimed at the College of Engineering’s building of KUniversity located in Daegu. The status of completion and remodeling was identified, and the heat transmittance coefficient for the year was summarized. When heat transmission coefficient is applied, the energy demand, required amount, and carbon emission were calculated. Through the improvement of the H.T.C., the demand for heating energy decreased, but the demand for cooling energy increased. The annual CO2 reduction of building A is 1,010.62 tCO2eq, which is 1.21% of the existing amount. Building B can reduce 2.74% and 3,115.20 tCO2eq of the existing amount. If the latest heat transmission coefficient standard is applied to the College of Engineering, it will be possible to reduce 19,363.43 tCO2eq/year.
jaue2021-069: The Impact of Environmental Education on Tourists' Responsible Environmental Behavior in Seaside Scenic Spots
In recent years, coastal cities have gradually become the first choice for tourists. From 2015 to 2019, the growth rate of tourists in coastal cities was about 10%. While many tourists bring economic benefits, they also bring a lot of environmental pollution. Tourists' responsible environmental behavior is not only affected by their own psychological factors, but also by environmental factors in the scenic spot. Environmental education in tourist destinations is an important situational factor that affects the responsible environmental behavior of tourists and has important research value. The author combines the theory of planned behavior. Analysis method using structural equation model. The following conclusions are drawn: (1) Environ mental education in tourist destinations is a situational factor that affects tourism REB. It has a significant positive impact on tourists' REBI and REB. (2) Personal factors such as ATEB, SN, PBC have a strong positive impact on tourists' REBI. Visitors' REBI has a very significant positive impact on REB. These conclusions can provide feasible suggestions for the layout of coastal scenic spots. Provide new ideas for improving environmental quality.
jaue2021-068: The Benefit of Aggregated Control of Distributed Battery Energy Storage System
Proper control strategies and real-time dispatch of batteries are expected to play a promising role in smart grid optimization to accelerate the cooperation between distributed renewable energy sources (RES) and the public grid. In this study, we try to aggregate the private battery system to explore the aggregated effect of public grid's peak shaving potential. Meanwhile, we attempt to quantify the benefits of the battery controls and management and try to make suggestions about the battery's optimal capacity for present-day market-based systems under different targeted PV capacity. Results indicate that the proposed algorithm will support the PV battery system to participate great peak shaving effect of the public grid. Further analysis indicates the intermittent output will more easily impact the power system with the increase of PV size. A PV battery system with an SB-to-PV ratio greater than 85% can alleviate this impact and improve the power system's equipment utilization.
jaue2021-067: Feasibility and Efficiency Evaluation of Virtual Power Plants —— A case study in Higashida Area, Japan
As most of the distributed energy resources are available in urban areas, there are increasing interests in assessing the potential to develop urban Virtual Power Plant (VPP). This paper aims to analyze the feasibility of VPPs through the construction of local renewable energy and energy storage technology in Higashida area. The return on investment was carried out through payback period and life cycle cost analysis. The economic performance of the power plant side and the demand side by changing the investment from power plant side and the corresponding electricity price on the demand side were analyzed. Result verify that total capacity formed by VPPs is 13,771kW, and power plant side participate in VPP investment can effectively improve the energy self -sufficiency rate and economic benefits of the entire region.
jaue2021-066: Experimental Study on IoT-based Estimation of Building Infiltration Rate using Tracer Gas Method
One of the main parameters of assessing building energy performance is infiltration rate. Typically, infiltration rates are set to normative values based on statistical data or inferred using blower door test. The normative value, however, shows error depending on the construction quality and the year of construction. Also, expertise and expensive equipment are needed for the blower door test. So, in this paper, IoT -based device to estimate building infiltration rate using tracer gas method (CO2) and process of data acquisition and handling are proposed. Several IoT sensors in a room are used to measure CO2 from various locations. Data is sent to a server after it has been measured. The server checks the uniformity of CO2 concentration and then calculates the infiltration rate. The proposed approach was tested in a university classroom. The test showed that the infiltration rate can be inferred using the proposed approach without expertise and with a lower cost.
jaue2021-065: Review on indoor thermal comfort of AC system and natural ventilation in the university classroom
Indoor thermal comfort in classrooms is essential for students’ health and learning performance. Air Conditioning (AC) unit system is normally used in summer season to control indoor thermal comfort. Yet continuous use results in high energy consumption on campus. Advocating to the global environment and energy consumption issues, this study aims to review indoor thermal comfort in classroom with two different ventilation and cooling method: with cooling system and with natural ventilation whether both methods have significant differences. In this study, the indoor thermal comfort surveys and parameter measurements conducted in summer season 2018 and 2021 in The University of Kitakyushu. The method used in this study is by distributing questionnaires to students and by air temperature, relative humidity, wind velocity, globe temperature, and PMV measurements. The empirical findings which compared questionnaires and measurement results are discussed. Estimation of WBGT is also calculated in both cooling system and natural ventilation to review the heat stress index in the classroom. The research results are expected to become useful for energy saving implementation in educational buildings and/or for ventilation method reference for indoor thermal comfort in the university classrooms.
jaue2021-064: Study on display method about environmental performance of house on website
In recent years, environmental performance evaluation systems (EPE) for buildings have been used, but there have been few studies on how to display information on environmental performance when selling houses. Therefore, this study aims to understand the current status of information display methods for EPE of houses for sale on housing information websites. To begin with, we will limit the sites and regions to be surveyed, survey the number of certified cases of each EPE and the number of properties hit when searched on the sites, and group them. Next, we will change the survey method for each group and investigate how the EPE is displayed. The results show that the number of hit properties differs depending on the search method, even for the same EPE, indicating that some properties are difficult to hit depending on the display method. It was also found that some properties do not display the information even if they have obtained certification, and that the display of all EPE is less than that of basic information. These indicated that EPE is not fully utilized as an attraction of houses at the time of sale.
jaue2021-063: Driving factors of carbon emissions from the construction industry in Zhejiang province, China
Global warming caused by greenhouse gas (GHG) emissions has become a major problem to be solved by all countries. The construction industry accounts for 36% of GHG emissions from all industries. China is the world's largest carbon emitter, and the construction industry in Zhejiang province is developing rapidly, accounting for a high proportion of the total emissions. In this study, carbon emission coefficient method and input-output model were used to calculate the carbon emission of construction industry (CECI) in Zhejiang Province, and Logarithmic Mean Divisia Index (LMDI) model was used to explore the driving factors of CECI.The results show that: (1) 2017 is the turning point of CECI, and the growth turns to decrease;(2) Construction industry accounts for a large proportion of indirect carbon emissions, energy consumption and building material production are important sources of CECI; (3) Area factor is an important factor affecting the increase and decrease of carbon emissions; (4) Reducing carbon emissions should focus on improving material technology, saving energy, reducing the number of new buildings and other aspects.This paper can provide ideas for policy makers to formulate carbon policies to reduce emissions and promote regional lowcarbon development.
jaue2021-062: A Study on Electricity Consumption and its influence factors of All-Electric Housing Complexes in Kitakyushu City Smart Community
CO2 emissions from the household sector are rapidly increasing due to the proliferation of home appliances, changes in lifestyles that demand convenience and comfort, and an increase in the number of households. Furthermore, the Great East Japan Earthquake of 2011 brought nuclear power generation to a halt, and the increase in CO2 emissions from electric energy has become a problem.In recent years, the use of all-electric homes, which use electricity for all of their energy consumption, has spread rapidly from the standpoint of safety and comfort, and against the backdrop of the deregulation of electric power retailing and other factors, the rate of adoption is expected to increase further in the future. In previous studies on energy consumption and CO2 emissions in homes, Murakami et al. conducted a nationwide survey and questionnaire survey of energy consumption by house type and use, and Ando et al. 4) conducted a survey of all-electric detached houses in the Hiroshima area to determine the energy consumption of all-electric houses. The results of the survey have been used to analyze the factors affecting the overall household electricity consumption of all-electric detached houses. Thus, a lot of knowledge has been obtained on energy consumption and CO2 emissions in housing. However, there is no study on all-electric housing complexes, and the usage of electricity in all-electric housing has not been clarified since the Great East Japan Earthquake. The purpose of this study is to build a database on energy consumption in all-electric housing, which is expected to increase in the future, and to identify factors affecting household electricity consumption. We surveyed 13 households in an all-electric apartment complex located in Yahata-Higashi Ward, Kitakyushu City, from August 5, 2015, to February 1, 2016, to measure electricity consumption in their households during the summer, midterm, and winter seasons. Based on the measurement data obtained from the survey, the characteristics of electricity consumption in each household were clarified, and the effects of household attributes, seasonal variations in electricity consumption, electricity consumption for various purposes, and the percentage of households at home on electricity consumption in each household were analyzed.
jaue2021-061: Embodied Energy and Carbon of Medium and High-Rise Rusunawa in Indonesia
Housing development in the urban area, especially in big cities, is constrained by difficulties to find affordable price lands. Vertical housing in the urban area becomes a recommended option to provide low-cost residences for low-income people. Substantial medium and high-rise rented simple apartments (Rusunawa) were developed for low-income people through the central government of Indonesia's program. This study conducts an assessment of embodied energy and carbon of five and ten storey Rusunawa buildings to represent the medium and high-rise Rusunawa buildings in Indonesia. Analysis of embodied energy and carbon used the building materials life cycle inventory database of the related previous research. Rusunawa building materials were classified according to technical performance and embodied performance. The influence of building height, structural elements, and non-structural elements against the embodied energy and carbon of medium and high-rise Rusunawa buildings have been identified. Building material selection has been performed based on the classification of technical performance related to its embodied performance.
jaue2021-060: Expanded metal daylight performance based on the standard type JIS G3351
Having a longstanding reputation as an industrialized building material, expanded metal mesh is one of the favorable materials used by the designer to enhance architectural features. The matured and standards of this material promoted by National Association of Architectural Metal Manufacturers (NAAMM) along with Expanded Metal Manufacturers Association (EMMA), has been proven to contribute to three out of seven of the USGBC LEED’s key areas, which are: Energy and Atmosphere, Indoor Environmental Quality, and Materials and Resources. Despite the use of expanded metal as complementary architectural features become popular, a scarce number of research has been done in investigating daylight performance according to the usage of expanded metal as a solar shade. This research investigates Useful Daylight Illuminance (UDI), Daylight Glare Possibility (DGP), and the aperture as optical openness of expanded metal according to the 27 standardized types of JIS G3351 given a context of Winter solstice in Kitakyushu, Japan, as the analysis data input. Grasshopper has been used to be the main parametric modeling platform, while Ladybug and Honeybee were used to undergo daylight simulation. The results of this research are expected to give positive feedback to the manufacturing industry and designers in the decisionmaking phase during the architectural design processes.
jaue2021-059: Creating a disaster response manual using renewable energy facilities in schools
The Cabinet Office has stated that it is necessary to cooperate with local residents who will be evacuated by creating and operating a safety guide. However, about half of the schools have not prepared a disaster response manual. On the other hand, the number of school facilities with solar and other renewable energy equipment has been increasing since the Great East Japan Earthquake, and this has been helpful in dealing with power outages and other disasters associated with large-scale disasters. However, because the operation of solar power generation equipment and other facilities requires specialized knowledge, they are often not used effectively in times of disaster. In order for evacuees who do not have specialized knowledge to use such equipment in the event of a disaster, it is essential to develop a disaster response manual for schools that can be understood and used by general evacuees.
jaue2021-058: The characteristic for the cluster of occupants through the clustering of Thermal Environment and Thermal Comfort
The Thermal Comfort of subjective occupants is clustered with the Indoor Thermal Environment data in this research. The purpose is to analyze the cluster derived since then. The K-means clustering approach is used in this research. It has been clustered after verification of 3 times for the validity of k value (the number of cluster) based on the preceding research in this research to supplement these disadvantages. The result is as follows; The optimal number of cluster has been derived through the preceding research. As a result of analyzing with the Thermal Comfort Index based on this, the characteristic between clusters could be identified clearly. Therefore, derived 3 clusters have been arranged as the sensitive, normal, insensitive cluster according to the characteristic.
jaue2021-057: Validation of a hybrid approach on energy model for heating load in residential houses
Energy modeling is familiar with its function to estimate energy consumption in buildings and provide appropriate design options for energy efficiency plans. In addition to conventional approaches of energy performance and energy simulation, it is potential to combine physical-based energy modeling with energy monitoring data, household characteristics, and occupancy rates to comprehensively capture occupant’s energy-related behavior. This paper presents the application of an integrating method of a hybrid model combining multiple-interactions between actual data and energy simulation data. The results of the modeling samples show the goodness of fit with a Pearson coefficient of determination falling to 67% to 91% probability of the energy model data. Specific air conditioning set-points were found through the matching performance when testing different simulated patterns. The study emphasizes the crucial role of investigating the occupant’s “staying-home” schedule to detect energy waste at home, and emerges ways to cut down the unused energy without significant effects on the inhabitant's lifestyle. The validated models can be used to provide suitable solutions for suggesting residential energy-saving decisions based on the combined modeling application of the building energy modeling.