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Measures to improve the building energy efficiency of deteriorated multi-family housing complexes (MFHCs) require trade-offs between economic and environmental feasibility, and therefore these aspects should be considered simultaneously. Towards this end, this study aimed to develop a multi-objective optimization model for determining the optimal CO 2 emission reduction (CER) strategies for MFHCs that can be used not only by experts but also by non-experts. This study used integrated multi-objective optimization with a genetic algorithm as an optimization methodology. The developed model, which considers the five optimization objectives (i.e., initial investment cost, net present value, savings-to-investment cost, CER, and marginal abatement cost) at the same time, can review a total of 31,200 scenarios combined with four energy saving measures (i.e., insulation, window, lighting, and shading systems). To verify the feasibility of the developed model, this study conducted a case study targeting 'D' MFHC in South Korea. First, the accuracy of the calibration of the energy simulation model for 'D' MFHC (coefficient of variation of the root mean square error: 12.84%; mean bias error: 0.39%) satisfied the criteria of ASHRAE Guideline 14; second, I8 (expanded polystyrene board - type 1 (No. 4))-L3 (LED lighting installed in louvered ceiling) and I8 (expanded polystyrene board - type 1 (No. 4)) - L4 (ceiling-mounted light) were determined to be the optimal CER strategy (integrated multi-objective optimization score: 0.2252). The developed model can help building owners make the optimal decision on green modeling by entering simple information (e.g., region, total floor area, etc.). • Optimization model was developed to determine the CO 2 emissions reduction strategy.. • Model considered calibration of simulation model and energy saving measures. • A total of 31,200 CO 2 emission reduction scenarios were generated. • Target Building can reduce the CO 2 emissions up to 13.14% through optimal strategy. • Model can help building owners make the optimal decision on green remodeling. [ABSTRACT FROM AUTHOR]

The government rebate has become one of the important drivers for implementing the photovoltaic (PV) system. However, it is not as effective for the multi-family housing complexes (MFHCs) in South Korea due to the constant government rebate without considering the electricity generation of the PV system by region and the electricity consumption by MFHC. Thus, this study aimed to estimate the optimal government rebate that guarantees the economic profitability of the PV system within the 4-year target payback period set by the South Korean government to encourage the installation of the PV system in MFHCs. Towards this end, based on the 16 administrative divisions in South Korea and the three types of electricity consumption, this study established 48 scenarios for analyzing the life cycle cost and life cycle CO 2 . When the annual electricity generation of the PV system decreased from 357,300 to 302,090 kWh, and when the monthly average electricity consumption per household by MFHC increased from 200 to 600 kWh, the ranges of the optimal government rebate decreased from US$1,406-1479/kW to US$649–838/kW. When the target payback period increased, the optimal government rebate, which guarantees the economic profitability of the PV system within the 4-year target payback period, decreased. This study may be useful for decision-makers (i.e., residents, construction managers, etc.), or policymakers to determine the optimal government rebate that meets the economic profitability of the PV system in terms of the life cycle cost and life cycle CO 2 . [ABSTRACT FROM AUTHOR]

The solar lease business is a recent market trend that has been introduced in many countries. An example is the U.S. solar lease payment (LP) business, which is a payment facility for leasing the solar PV system, where the customers pay a certain amount to a third-party company. In the solar lease business, the profit obtained by residences and third-party companies depends on the solar LP. Several impact factors should be simultaneously considered when estimating the solar LP that guarantees the profitability of the business for both sides. This study aimed to develop a model for determining the optimal solar LP in the solar lease business for residences and third-party companies. A genetic algorithm was utilized to solve the trade-off problem, among the many factors involved. The optimal solar LP was provided according to two categories: (i) the electricity generation rate by region; and (ii) the electricity consumption rates of multi-family housing complexes. In terms of the region, the optimal solar LP depended on the electricity generation rate, and the difference between the highest and lowest monthly solar LPs per unit was US$0.30. In terms of the electricity consumption rate, the optimal solar LP depended on the electricity consumption rate and the number of units, and the difference between the highest and lowest monthly solar LPs per unit was US$3.14. The developed model makes it possible for the government to suggest the optimal solar LP for promoting the solar lease business, and to develop a solar PV system. [ABSTRACT FROM AUTHOR]

Solar Renewable Energy Certificates (SREC) in the United States (U.S.) have become an important driver for promoting the solar photovoltaic (PV) system. However, the SREC price volatility is considered the major barrier for installing the solar PV system due to the uncertainty of the revenue from selling the SREC. To address this challenge, this study aimed to establish a base price for SREC in order to encourage the installation of residential solar PV systems in the U.S. Toward this end, this study conducted the life cycle cost analysis to establish the minimum SREC prices required for reaching the target payback period of the residential solar PV system in the U.S. In addition, this study conducted the comparative analysis from the perspectives of residents and state governments by considering the SREC prices. This study was conducted in four steps: (i) target selection; (ii) data collection; (iii) defining assumptions; and (iv) establishing the minimum SREC prices. The results showed that Massachusetts and New Jersey are the most superior state from the perspective of both residents and state governments. This study can help both residents and state governments to get financial advice for installing solar PV system and improving the SREC-related policies. [ABSTRACT FROM AUTHOR]

Although the global new renewable energy (NRE) market has shown remarkable growth during the past decade, NRE policies and incentive schemes should continue to support NRE market and to achieve NRE targets from the long-term perspective. In particular, solar policies and incentive schemes still remain vital to the deployment of the residential solar photovoltaic (PV) system in the United States (U.S.) due to its high initial investment cost. The previous study conducted an economic and environmental assessment of the residential solar PV system considering state solar incentives in the U.S.; however, it did not suggest any improvement scenarios for state solar incentives. To solve this limitation, this study aimed to conduct an economic impact analysis of state solar incentives for improving financial performance of residential solar PV systems in the U.S. Towards this end, this study established 16 scenarios for improving state solar incentives and quantified cash incentive rates for each scenario which can satisfy the threshold performance levels in the U.S. As a result, this study could suggest more suitable tax incentives and cash incentive rates for achieving the economic feasibility of the residential solar PV system in each target city of the U.S. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) system has been highlighted as a sustainable clean energy source. To successfully implement the PV system in a real project, several impact factors should be simultaneously considered. This study aimed to develop an integrated multi-objective optimization (iMOO) model for determining the optimal solution in implementing the rooftop PV system. This study was conducted in six steps: (i) establishment of database; (ii) generation of the installation scenarios in the rooftop PV system; (iii) energy simulation using the software program ׳ RETScreen ׳; (iv) economic and environmental assessment from the life cycle perspective; (v) establishment of the iMOO process using a genetic algorithm; and (vi) systemization of the iMOO model using a Microsoft-Excel-based VBA . Two criteria were used to assess the robustness and reliability of the developed model. In terms of effectiveness, the optimal solution was determined from a total of 399,883,120 (=91×49×19×80×59) possible scenarios by comprehensively considering various factors. In terms of efficiency, it was concluded that the time required for determining the optimal solution was 150 s. The developed model makes it possible for final decision-maker such as construction managers or contractors to determine the optimal solution in implementing the rooftop PV system in the early design phase. [ABSTRACT FROM AUTHOR]

This study aimed to develop an integrated multi-objective optimization ( iMOO ) model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target (CERT) (27.0%) for South Korea׳s residential building sector. This study was conducted in six steps: (i) analysis on the energy consumption patterns of the multi-family housing complexes (MFHCs) by cluster; (ii) establishment of the energy retrofit models for the target MFHC through energy simulation; (iii) scale conversion from the target MFHC to the standard MFHCs by cluster; (iv) life-cycle economic and environmental assessment on the energy retrofit models of the standard MFHCs by cluster; (v) trend analysis of the energy saving effects for the energy retrofit models of the standard MFHCs by cluster; and (vi) establishment of the low-carbon scenario 2020 for achieving the national CERT by using an iMOO model. The results showed that if the optimal low-carbon scenario 2020 is to be implemented as of 2012, the energy-saving effect as of 2020 would be 27.20%. This was determined as follows: (i) the initial investment cost was $1,410,252, 102.5% of the optimized value (optimization goal A); (ii) NPV 40 (net present value at year 40) was $18,954,210, 71.9% of the optimized value (optimization goal B); and (iii) SIR 40 (saving to investment ratio at year 40) was 5.03, 74.2% of the optimized value (optimization goal C). The proposed model may be useful for policymakers in determining the optimal low-carbon scenario 2020 for achieving the national CERT under the budget limits, from the macro perspective. [ABSTRACT FROM AUTHOR]

Abstract: With the world's attention focused on climate change, the United Nations Framework Convention on Climate Change provides the basis for global action to encourage sustainable development. A wide variety of measures are being taken in South Korea in line with this trend, but new and renewable energy (NRE) have been highlighted as sustainable energy sources. This study aims to assess the economic and environmental effects of the use of NRE for selecting the optimum NRE system in educational facilities. Towards this end, the following were done: (i) selection of facility and its applicable NRE system type; (ii) calculation of the energy generation by the NRE systems via energy simulation; (iii) life cycle cost analysis for the economic evaluation on the NRE systems; (iv) life cycle assessment for the environmental evaluation on the NRE systems; (v) using the net present value and the savings-to-investment ratio, comprehensive evaluation of the economic and environmental effects on the NRE systems. The results of this study can be used to (i) determine which NRE system is most appropriate for educational facilities; (ii) calculate the payback period for a certain investment; (iii) decide which location is proper for the implementation of an NRE system considering the characteristics of the regional climate; and (iv) select energy- and cost-efficient elementary schools where the NRE system can be applied. [Copyright &y& Elsevier]

Abstract: Recently, many countries have been making an effort to reduce their carbon dioxide (CO2) emission, and as part of such effort, the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Kyoto Protocol in 1997. South Korea is very likely to be included in the second batch of countries that must reduce their greenhouse gas emission after the end of the implementation of the Kyoto Protocol in 2012. Reducing the country’s CO2 emission, however, can have an impact on the economy. Therefore, in this study, the correlations between South Korea’s economic growth, CO2 emission, and energy consumption were analyzed. The analysis period was from Q1 1991 to Q4 2011, and the analysis methods that were used were regression analysis for the relational analysis among the various overall indices, and the Markov switching model for a more detailed analysis. The results of the analyses showed that South Korea’s economic growth and CO2 emission were coincidental. The correlation analysis of the country’s economic growth and energy consumption showed a significant correlation between economic growth and fossil fuels, which emit CO2, such as coal in the industrial sector, petroleum products in the industrial and transportation sectors, and liquefied natural gas (LNG) in the residential/ commercial and industrial sectors. It is expected that the results of this study will pave the way for the conduct of various researches on controlling the country’s CO2 emission management, and for suggestions for such to be given, such as policies for reducing the energy consumption in each sector, using the methodology proposed in this study. [Copyright &y& Elsevier]

This study analyzed the status of greenhouse gas (GHG) emissions of residential buildings in South Korea from 2015 to 2019, based on energy consumption data for all residential buildings in the country from the Korea National Building Energy Database. The analysis revealed that new residential buildings had a lower GHG emission intensity than old residential buildings and that strengthening the Building Energy Conservation Code (BECC) contributes to the reduction of GHG emissions from heating energy use in residential buildings. The GHG emission intensity for heating energy use of apartments to which the latest standard (0.21 W/m2·K U-value of exterior walls) was applied was 35.8% lower than that of apartments to which the insulation standard was not applied. However, despite the strengthening of the BECC, the GHG emission intensity for baseload and cooling energy use of apartments increased. This means that the BECC does not contribute to reducing GHG emissions from baseload and cooling energy use. Moreover, as the number of residential buildings continues to increase, their consolidated GHG emissions also increase. Since heating energy consumption is significantly influenced by the weather, GHG emissions from heating energy use can vary depending on the weather. For instance, GHG emissions from heating energy use can increase in extremely cold winter despite the strengthening of the BECC. Therefore, a policy is needed for strengthening the BECC as well as for enhancing building occupants' understanding of GHG emissions reduction in buildings and for promoting the occupants' behavior change. • Greenhouse gas emission data of residential buildings from 2015 to 2019 was analyzed. • Residential buildings increasing every year have led to an increase in GHG emission. • Due to strengthening BECC, GHG emission intensity for heating energy use decreased. • Despite BECC, GHG emission intensity for baseload and cooling energy use increased. • Results show the need for policies to change occupants' behavior to achieve CERTs. [ABSTRACT FROM AUTHOR]

This study proposed an optimal trading price of electricity by considering electricity billing system and corresponding government policies in South Korea. To this end, this study calculated the maximum and minimum trading prices of electricity by defining the profit structure from the viewpoint of the energy consumer and prosumer, based on which the optimal trading price of electricity was derived from a genetic algorithm (GA) and Pareto optimal solution. The main results of this study can be summarized as follows. First, from the perspective of the energy prosumer, the lower the self-use rate and the higher the monthly electricity usage, the higher the optimal trading price of electricity, and the higher the number of tradable-energy consumers in the monthly electricity usage was, the greater the scope of the optimal trading price of electricity. Second, the higher the monthly electricity usage, the higher the optimal trading price of electricity, and the higher the number of tradable-energy prosumers in the monthly electricity usage, the greater the scope of the optimal trading price of electricity. The results of this study can be used to establish an energy prosumer's electricity usage strategy, improve the electricity billing system based on the optimal trading price of electricity, and establish a policy on the subsidy to be offered for the installation of solar photovoltaic (PV) panels. • Optimal P2P electricity trading price is proposed for both prosumers and consumers. • The optimal P2P electricity trading price can be derived by polynomial interpolation. • P2P electricity trading is available for trading up to US￠26.18/kWh. • Polynomial interpolation was used to derive the optimal P2P electricity trading price. • User can find the optimal P2P electricity trading price according to the condition. [ABSTRACT FROM AUTHOR]

To reduce CO 2 emissions in the building sector, South Korea uses an operational rating system, an indicator for evaluating CO 2 emission performance. To conduct a reasonable operational rating, it is necessary to develop a rational and reliable CO 2 emission (CE) benchmark for buildings. The conventional CE benchmarks, however, have limitations accounting for regional differences of multi-family housing complexes (MFHCs). Thus, a separate CE benchmark is required for each region for improving the rationale and reliability of the conventional CE benchmarks. To solve this problem, a data-driven approach for establishing a CE benchmark using data mining techniques was applied in this study. Data on a total of 1,212 MFHCs were established, and a total of 11 CE benchmarks (central region: 7; southern region: 4) for MFHCs were established based on the decision tree. The developed CE benchmarks were then validated using statistical methods (Mann-Whitney test, Kruskal-Wallis test, etc.). Compared to the average operational rating based on conventional CE benchmarks, the average operational rating based on the newly developed CE benchmarks decreased by 1.85% in the central region, and increased by 5.19% in the southern region, respectively. This means that the unreliability and irrationality of the conventional operational rating system (ORS) can be solved by the established ORS. The established ORS, based on the newly developed CE benchmarks, can help policymakers select and manage MFHCs with poor CE performance. • The CO 2 emissions benchmarks were developed considering regional differences. • A total of 1,212 buildings were classified into 11 groups using a decision tree. • The CO 2 emission benchmarks were validated using statistical methods. • New operational rating system solved the irrationality and unreliability problems. • This study can help policymakers manage the building with poor CO 2 emissions. [ABSTRACT FROM AUTHOR]

Given the importance of occupant satisfaction with thermal comfort in hotel accommodations, previous studies have assessed thermal comfort using either local or global standards. In a hotel catering to diverse guests, a single standard may constrain the formulation of an optimal strategy for improving occupant satisfaction. To address this challenge, this study empirically investigates occupant-centric thermal comfort in hotel guestrooms located in a hot and humid climatic region. To this end, a living-lab-based case study was conducted on a hotel guestroom in Hong Kong, wherein two types of thermal comfort improvement strategies were devised from both local and global perspectives. First, to improve thermal comfort in line with the local standard, it was determined that an ancillary fan coil unit (FCU) energy consumption of 6.0 kW h (approximately 34 % of the existing energy consumption) was required. Second, to improve thermal comfort in line with the global standard, an ancillary FCU energy consumption of 9.4 kW h (approximately 53 % of the existing energy consumption) was necessary. Consequently, in a hot and humid climatic region, the distribution of thermal comfort may vary depending on the type of guest (e.g., domestic or foreign), and ancillary cooling energy expenditure may be incurred to enhance a guest's thermal comfort. The proposed approach is expected to contribute to the creation of occupant-tailored thermal comfort by considering an individual's thermal acclimation to a specific climatic region from both local and global perspectives. [Display omitted] • An integrated approach was proposed to enhance thermal comfort in hotel guestroom. • Diverse standards for thermal comfort and energy saving must be taken into account. • Occupant-centric thermal comfort can be improved in terms of thermal acclimation. • Ancillary cooling energy of 34 % may be incurred for acclimatized domestic guest. • Ancillary cooling energy of 53 % may be incurred for non-acclimatized foreign guest. [ABSTRACT FROM AUTHOR]

Industrial decarbonization is a significant challenge to national net zero goals. Combined heat and power (CHP) technologies provide a means by which industrial facilities can achieve greater energy efficiency and decrease carbon emissions through cogenerating electric power and process heat. Regions possessing sufficient population density and manufacturing capacity have significant technical potential for industrial CHP development and can experience economic and environmental benefits. However, CHP implementation costs and the complexity of projects represent significant barriers to deployment with successful implementation often relying on government incentives to achieve economic feasibility. In this research, a framework is developed to evaluate the economic feasibility and environmental viability of various CHP technical potential under the various market penetration scenarios. A case study for a region in the United Sates is presented to highlight the efficacy of the framework. Projected CHP generating capacity of the region is calculated using commercial and industrial specific capacity factors. Comprehensive CHP engineering and cost data are integrated with regional economic transaction data. Then, comparative CHP application analysis is performed to determine which configuration could have the greatest regional economic and environmental impacts. Results indicated that investment in industrial topping cycle CHP to achieve the maximum technical potential generating capacity would have the greatest regional impacts resulting in $20 billion of economic outputs, creating 89,000 jobs, and avoiding approximately 3.2 million metric tons of carbon dioxide emissions. Results from this and similar research may be used by policymakers and administrators to assess potential investments in industrial decarbonization technologies and pathways. [Display omitted] • Significant CHP technical potential for industrial and commercial applications. • Comprehensive CHP engineering and cost data integrated with regional economic data. • Comparative CHP application analysis performed to determine the greatest regional impact. • Industrial CHP applications present a significant source of decarbonization potential. [ABSTRACT FROM AUTHOR]

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities. • Industrial energy data from 45 manufacturing facilities are obtained from on-site energy audits. • A supervised ML model is developed to facilitate predicting industrial energy consumption. • Basis function model presented a grounding in the physical nature of industrial energy systems. • Enables energy engineers or plant personnel to construct a more descriptive energy model. [ABSTRACT FROM AUTHOR]

Advancements in various technologies have made it possible to recycle end-of-life batteries from electric vehicles (EV) into a stationary energy storage system (ESS) within residential buildings. As a result, promoting a circular economy between buildings and means of transportation has emerged as a major concern. Therefore, this study aimed to quantitatively assess the environmental impacts (life -cycle carbon Carbon dioxide (CO 2) emissions) of ESS utilizing used batteries instead of new batteries from the life cycle perspective of lithium-ion batteries (LIBs) considering the uncertainty in energy communities. To this end, a probabilistic life cycle assessment (LCA) was performed using a Monte Carlo simulation of the energy community of South Korea. The results of this study demonstrated that reusing batteries as ESS in buildings could further improve the overall environmental sustainability of the ESS compared to using new batteries. As a result, when reused batteries were utilized, annual carbon emissions decreased by 2.8 %–18.5 % according to battery usage purposes. More specifically, it was shown that battery reuse can reduce greenhouse gas emissions and environmental impacts. To more rationally evaluate life-cycle CO 2 emissions from a resource circulation perspective, the usage purpose and efficiency of the battery should be considered during the entire battery life cycle (transportation and building sectors). These findings are expected to provide valuable insights to policymakers, industrial sectors, and research institutes related to battery reuse and to help guide the future of transportation and building sectors in a sustainable direction from a resource circulation perspective. • This study assesses the environmental impact of using used lithium-ion batteries. • A probabilistic life cycle assessment was conducted using Monte Carlo simulation. • Reuse of expired electric vehicle batteries can improve environmental sustainability. • Battery usage purpose with efficiency should be considered during entire lifecycle. • This study can contribute to crafting rational environmental impact policies. [ABSTRACT FROM AUTHOR]

The quest for sustainable energy solutions has led humanity beyond Earth, venturing into space. Earth-based solar power (EBSP) systems face challenges due to the planet's rotation, atmospheric environments, and weather conditions that can obstruct sunlight. In contrast, space-based solar power (SBSP) systems enable the utilization of 99 % of solar energy 24 h a day. However, while SBSP has shown the potential to produce a more stable power supply and sustainability, it is still in the early stages of its evolution, requiring further research and development. Previous studies have predominantly focused on specific SBSP technologies, limiting the evaluation of its potential. To overcome major challenges for high initial costs, technological complexities, and environmental impacts, this study proposes original achievements such as a comprehensive review of SBSP and innovative approaches to mitigate environmental impacts. Therefore, this study aims to propose a "Sustainable Vision" for SBSP in future smart grids. To achieve this, the study comprehensively reviews current technologies and considerations in relation to SBSP applications across its life cycle (i.e., manufacturing, transportation, installation, operation & maintenance, and end-of-life management stages). Furthermore, this study analyzes SBSP from techno-economic and socio-environmental perspectives, ultimately discussing its feasibility and applicability for sustainability in smart grids. This study contributes to providing a comprehensive overview and outlook on the feasibility of SBSP (i.e., technical challenges, economic potential, and socio-political insights). The interdisciplinary approach adopted in this study will help to enhance the holistic understanding of SBSP for sustainability, thereby fostering collaboration and decision-making among experts from various fields. [Display omitted] • SBSP technologies are a promising alternative that overcomes Earth-bound limitations. • This study proposed a sustainable vision for SBSP through a comprehensive review. • Current SBSP technologies and considerations were investigated across their life cycle. • The feasibility and applicability of SBSP were discussed from various perspectives. • This study facilitates sustainable decision-making in the space and energy industries. [ABSTRACT FROM AUTHOR]

This study was conducted to ascertain how the use of recycled materials instead of virgin materials affect the installation of renewable energy systems for the energy transition of buildings. LCA was used to estimate the life cycle energy use and GHG emissions of building and renewable energy systems with and without using recycled materials. For the case study, a public building and four representative photovoltaic (PV) systems were selected. National recycling standards and processes were considered for suggested recycled materials. In general, replacing virgin material with recycled counterpart reduced embodied GHG emissions more than embodied energy. However, due to high carbon intensity of operation energy and building materials without available counterparts, using recycled materials reduced the life cycle energy use and GHG emissions of the case building by 4.9% and 3.3%. Among four PV systems, using recycled materials was most significant in the single crystalline-silicon (sC-Si) PV system, 44.5% reduction of energy use and 41.3% reduction of GHG emissions. Therefore, when recycled materials are used for achieving the energy transition of the case building with the sC-Si PV system, the PV system requirements could be reduced by a maximum of 9.6% for energy payback and 4.9% for GHG payback. This study demonstrated that the use of recycled materials is effective at reducing the embodied energy and GHG emissions of PV systems as well as buildings. To achieve carbon-neutral buildings, particularly, the use of recycled materials based on a thorough LCA should be considered in addition to renewable energy system. • Analyzing recycled materials' impact on building energy conservation and transition. • Life cycle assessment for building and PV system was conducted through a case study. • National recycling standard and process were applied for practical recycled material. • Impact of recycled materials was determined by carbon intensity of material and grid. • This study can help to consider an important factor for a carbon-neutral buildings. [ABSTRACT FROM AUTHOR]

In this study, an oversampling-based method for optimally predicting complaints due to environmental pollutants from construction sites was developed using imbalanced empirical data. Based on the empirical database related to such environmental complaints, several environmental-complaint prediction models were generated. These took into consideration input data, oversampling, and machine learning. An optimal prediction model with the best performance was determined using an integrated multi-objective optimization algorithm. To confirm the feasibility of the developed oversampling-based method, the optimal prediction model was determined among 210 other generated prediction models in the case study. By transforming imbalanced data on such environmental complaints into balanced data through oversampling, the prediction models overcame limitations (biased results) of imbalanced-data prediction, and recorded an 8–23% performance improvement. The optimal prediction model predicted imbalanced environmental complaints more efficiently and accurately than the previous other prediction models: up to 66.5% performance improvement. Ultimately, the optimal prediction model is expected to serve as a starting point for an environmental-complaint management system creating sustainable communities by minimizing the damage caused by environmental complaints to people living near construction sites and concerned construction companies. • An oversampling-based method for predicting environmental complaints was developed. • The feasibility of the developed method was determined through a case study. • 8–23% performance improvement was achieved using the oversampling-based method. • The prediction of the optimal prediction models improved up to 66.5%. • The developed model can be used as a decision support tool minimizing the damage. [ABSTRACT FROM AUTHOR]

Since a traditional centralized control system (e.g., building energy management system) with a fixed schedule and manual control is not appropriate to irregularly occupied rooms, it is expected to have a large amount of energy saving potential in operating the HVAC system. To overcome this challenge, this study aimed to develop a novel operation approach for the energy efficiency improvement of the HVAC system in office spaces. The real-time indoor environmental indicators were collected and analyzed to evaluate the current operation status of the HVAC system as well as to propose a novel control strategy in two ways. The significant findings can be illustrated as follows. First, it could be stated that occupants would tend to establish a lower set-point temperature for a cooler indoor environment. To solve this issue, a basic control strategy was proposed to detect the anomaly detection of the HVAC system and to automatically adjust the indoor temperature within a preferred range. Second, it could be evaluated that the HVAC system would be kept operating since occupants would forget to turn off the HVAC system after the meetings. To solve this issue, an advanced control strategy was proposed to operate the automatic on/off control of the HVAC system by considering the indoor temperature and CO 2 concentration in real time. The proposed strategies can contribute to a large amount of energy savings in operating the HVAC system of irregularly occupied spaces. • The proposed strategy can achieve the energy savings in operating the HVAC system. • A novel operation strategy is developed through real-time big data analytics. • The real-time indoor temperature and CO2 concentration is considered as a proxy. • Basic approach is aiming for the automatic adjustment of the set-point temperature. • Advanced approach is aiming for the automatic on/off control of the HVAC system. [ABSTRACT FROM AUTHOR]

Installing the battery energy storage system (BESS) and optimizing its schedule to effectively address the intermittency and volatility of photovoltaic (PV) systems has emerged as a critical research challenge. Nonetheless, some existing studies still have limitations in terms of the efficiency of the BESS scheduling due to the lack of comprehensive consideration of diverse user objectives. As a response to this gap, this study aimed to develop a reinforcement learning (RL)-based optimal scheduling model to better reflect the continuous behaviors in the complex real world. To this end, focused on residential buildings connected to the grid and equipped with a BESS and PV system, its optimal scheduling models were developed using four algorithms from among the various RL techniques according to training methods. The results of the case study showed that the developed RL-based optimal scheduling model using Proximal Policy Optimization (PPO) can be applied to effectively operate the BESS with a PV system, considering possible uncertainties in the real world. The case study demonstrated the effectiveness and feasibility of the developed RL-based optimal scheduling model. Compared to other algorithms, the PPO-based RL model has better decision-making for optimal BESS scheduling strategies to maximize their self-sufficiency rate and economic profits by coping with changing variables in the real world. Therefore, the RL-based BESS scheduling model will offer an optimal solution, specifically tailored for use within a virtual power plant, where numerous buildings continuously share electricity. [Display omitted] • Reinforcement learning-based scheduling model of battery energy storage system was developed. • Multi-objective optimization for the scheduling of battery energy storage was conducted. • Proximal policy optimization-based scheduling model outperformed the other three algorithms. • The developed model can provide an optimal solution for prosumers in microgrid. [ABSTRACT FROM AUTHOR]

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems. • This study proposes a framework for sizing and real-time scheduling of the BESS. • Reinforcement learning is used to conduct multi-objective optimization in this study. • A case study conducts at different battery ownership scenarios in energy communities. • Sharing a battery for economic gain proves more beneficial than individual ownership. • This study facilitates determination of the optimal schedule and capacity for BESS. [ABSTRACT FROM AUTHOR]

Despite the recent market growth and price reduction of technologies for a battery energy storage system (BESS), many technological, operational, and managerial challenges still need to be overcome to improve the system's feasibility and commercialization. Many studies suggested various solutions for applying BESS to the grid system, yet the existing literature is dispersed across different scopes, scales, and levels. Previous studies also lack a comprehensive overview of different application strategies of BESS within the urban landscape. Therefore, this study aimed to broaden our perspective and implement the concept of "BESS-based energy sharing" for carbon-neutrality at the urban level. Towards this end, this study investigated the key factors and main technologies for BESS application in the city through a comprehensive literature review. These results indicated that the limitations of current application technologies of the BESS and energy sharing using BESS should be overcome in the carbon-neutral city. As a result, this study proposed future direction of the BESS application strategies towards smart energy sharing in cities, as follows: (i) bi-directional interactions across hierarchical levels of the city; (ii) integrated management for high connectivity of distributed generation; and (iii) development of a BESS-based energy management system. This study would allow scholars, researchers, practitioners, and policymakers to better understand the energy sharing mechanism within the city and provide systematic guidelines and pathways towards smart energy sharing with the advanced energy infrastructure using BESS from a bi-directional perspective across different hierarchical levels of the city. • Existing literature related to BESS was reviewed towards BESS-based energy sharing. • Key factors to be considered for BESS application in the city were investigated. • Main technologies used in BESS applied to cities were investigated and compared. • Current BESS application technologies were lacked to implement a carbon-neutral city. • Future directions of BESS application and new energy sharing approach were proposed. [ABSTRACT FROM AUTHOR]

This study focuses on the energy performance of low-rise residential buildings and on the technological improvements required to improve their performance. As most low-rise residential buildings in Korea are old, they are being renovated to improve their energy efficiency. However, as renovations are expensive, the government intends to apply low-cost energy-efficient technologies that are currently in use as well as quantify their performances. To this end, low-rise residential buildings in villages were selected for the survey; among these, five buildings were evaluated for the application of energy-efficient technologies. Based on the survey, which included information on the actual energy consumption, the reliability of the simulation analysis was verified, and the effects of the technologies were evaluated. The survey focused, in particular, on the airtight performance of the implemented technologies, the efficiency of the heating equipment, and the insulation performance. Moreover, the performances were validated quantitatively by actual measurements. As a result, the energy saving for each technology were evaluated, and the corresponding annual savings were calculated. Finally, the payback period of the investment was evaluated using economic analysis. • Energy consumption bills for 107 households were investigated. • Energy technology performance was evaluated through actual measurements. • Actual results and simulation results showed significant trend similarity. • As a result of applying new energy technologies, 63% of energy was saved. • As a result of economic evaluation, a recovery period of at least 16 years occurred. [ABSTRACT FROM AUTHOR]

Over the past decades, various tools that can perform building life cycle assessment (LCA) as well as life cycle cost (LCC) or CO 2 analysis, have been developed. Even though these developed tools should be effectively evaluated and improved to encourage the continuous use of such tools, no research has been conducted on this matter. In this regard, this study sought to propose a framework for evaluating a building LCA tool from both the developer's and user's perspectives. In the developer evaluation process, experts evaluate if the design and implementation status (i.e., content status) are appropriate, and determine the design and implementation problems (i.e., content problems) based on six evaluation criteria, through content evaluation. In the user evaluation process, the users and evaluators determine the usability problems based on six usability attributes, through usability evaluation. The developer and user evaluation results are then interpreted through Satisfaction-Importance (S–I) analysis and Severity-Priority (S–P) analysis to prioritize the area of improvement and to determine the improvement strategy. To verify the proposed framework, a case study was conducted on an actual building LCA tool. The evaluation results showed that the problems corresponding to the assessment method and result should be preferentially improved in terms of content, while those corresponding to learnability, efficiency, and errors should be preferentially improved in terms of usability. Therefore, it is expected that the utilization of the proposed framework can effectively evaluate and improve various conventional building LCA tools in a reasonable way. • An evaluation framework for building life cycle assessment tool was proposed. • Contents and usability were evaluated and interpreted for sustainability of the tool. • Identified problems were prioritized to effectively manage the improvement strategy. • For the case study, assessment method and learnability should be improved first. • Proposed framework contributes to dissemination and proliferation of such tools. [ABSTRACT FROM AUTHOR]

An unprecedented global lockdown has been implemented for controlling the spread of COVID-19 in many countries. These actions are reducing the number of coronics, but with the prolonged COVID-19 outbreak, the restrictions on the activities of people are having a significant impact on all industries. Accordingly, this study aimed to statistically analyze changes in building energy consumption under the COVID-19 pandemic in South Korea, as well as identify the relationship between COVID-19 and building energy consumption according to the building use type. As a result, the average rate of changes in electricity and gas energy consumption decreased −4.46% and −10.35%, respectively, compared to the previous year. The energy consumption in most facilities has tended to decrease while energy consumption in residential facilities increased during COVID-19. The rate of change in building energy consumption had a significantly positive correlation with COVID-19 related factors in various facilities (e.g., neighborhood, religious, educational, and research facilities). Significant findings of this study that social distancing by the COVID-19 outbreak, has changed energy consumption according to building use type indicates the need for new energy systems to effectively manage the energy demand at the community level in the Post COVID-19 era. • Social distancing by COVID-19 impacts on building energy consumption in South Korea. • Total energy consumption during COVID-19 decreased compared to the previous year. • Energy consumption in most facilities has tended to decrease during COVID-19. • Energy consumption in residential facilities has tended to increase during COVID-19. • This study can help manage the energy demand at the community level in Post COVID-19. [ABSTRACT FROM AUTHOR]

In recent years, smart cities have emerged with energy conservation systems for managing energy in cities as well as buildings. Although many studies on energy conservation systems of smart homes have already been conducted, energy management at the city level is still a challenge due to the various building types and complex infrastructure. Therefore, this paper investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through a qualitative review. Based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers, this study suggests that the following innovative solutions be suitably applied to advanced energy conservation systems in sustainable smart cities: (i) construction of infrastructure for advanced energy conservation systems, and (ii) adoption of a new strategy for energy trading in distributed energy systems. Especially, to reflect consumer behavior and energy in sustainable smart cities, the following responses to future research challenges according to the "bottom-up approach (smart home level to smart city level)" are proposed: (i) development of real-time energy monitoring, diagnostics and controlling technologies; (ii) application of intelligent energy management technologies; and (iii) implementation of integrated energy network technologies at the city level. This paper is expected to play a leading role as a knowledge-based systematic guide for future research on the implementation of energy conservation systems in sustainable smart cities. Image 1 • In terms of energy technology, a connection between smart home and city is lacked. • Through quantitative review, research themes on smart home and city are identified. • Through qualitative review, barriers of progression to smart cities identified. • Bottom-up approach for energy conservation system in smart city is proposed. [ABSTRACT FROM AUTHOR]