@misc{hawks_cho_2024, title={Review and analysis of current solutions and trends for zero energy building (ZEB) thermal systems}, volume={189}, ISSN={["1879-0690"]}, DOI={10.1016/j.rser.2023.114028}, abstractNote={Building heating and cooling systems provide the greatest potential for energy consumption reduction in the U.S. As such, these systems have gained immense interest for decades, garnering an extensive range of solutions and strategies to provide energy-efficient thermal conditioning of the indoor environment. This study reviews and discusses this large breadth of research in combination with past and current industry trends to discover future zero-energy building thermal systems, subsystems, and control strategies in support of the Zero Energy Buildings initiative. The reviewed articles were selected from top research and industry journals, with a focus on review and high-level articles. It is clear from the articles that radiant, water-based systems are superior to convective, air-based thermal conditioning, and that passive strategies offer an immense opportunity for energy demand reduction of thermal systems. Additionally, future thermal systems must provide onsite TES in support of either renewable energy or passive strategies. Numerous theoretical gaps were discovered in the literature, mostly in the control strategies of different systems. From these gaps, opportunities are revealed that offer energy savings and solutions to large, systemic challenges. These include a nested hierarchy control structure for dual systems, short-term shallow earth TES, concrete collectors and radiators, and a ventilation system that can drive a generator for energy recovery. Taken together, these strategies and systems piece together a future building thermal system.}, journal={RENEWABLE & SUSTAINABLE ENERGY REVIEWS}, author={Hawks, M. A. and Cho, S.}, year={2024}, month={Jan} }
 @article{fang_cho_wang_he_2024, title={Sensitivity Analysis and Multi-Objective Optimization of Skylight Design in the Early Design Stage}, volume={17}, ISSN={["1996-1073"]}, DOI={10.3390/en17010219}, abstractNote={Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.}, number={1}, journal={ENERGIES}, author={Fang, Yuan and Cho, Soolyeon and Wang, Yanyu and He, Luya}, year={2024}, month={Jan} }
 @article{seo_yoon_lee_cho_2023, title={Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building}, volume={13}, ISSN={["2075-5309"]}, DOI={10.3390/buildings13061434}, abstractNote={This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.}, number={6}, journal={BUILDINGS}, author={Seo, Byeongmo and Yoon, Yeobeom and Lee, Kwang Ho and Cho, Soolyeon}, year={2023}, month={Jun} }
 @article{yoon_seo_mun_cho_2023, title={Energy savings and life cycle cost analysis of advanced double skin facade system applied to old apartments in South Korea}, volume={71}, ISSN={["2352-7102"]}, DOI={10.1016/j.jobe.2023.106535}, abstractNote={South Korea is one of the most densely populated countries in the world, and the population density in urban areas is much higher among others. Apartments are the most common form of residential buildings due to their high population density. More than 60% of domestic residential buildings are apartments, with more than 10 million households. The high population density is a problem that has long plagued South Korea because more than 45% of apartments are old apartments that are more than 20 years old. Old apartment buildings have low thermal performance that results in lots of energy consumption. Balconies, which worked as a thermal buffer, are also being removed by residents to expand indoor space. According to the green remodeling project supported by the Korean government, the typical retrofitting method is replacing external windows with the high-efficiency window in old apartments in South Korea. This paper proposes a retrofitting method using an extended double-skin façade (DSF) system that replaces exterior windows and acts as a thermal buffer. The simulation model is developed with the EnergyPlus simulation program to conduct energy performance analyses. The simulation model has been calibrated using actual experimental data collected between October 1, 2019, and January 15, 2020. Results show that the cooling, heating, and lighting energy can be reduced up to 44.1% by fully utilizing electric energy generated by solar PV panels integrated with the DSF system. The payback period is about 15 years based on the energy price provided by the utility company. Although the payback period is long, it has great potential of energy savings and CO2 emission reductions. The DSF system should be considered as a way of renovation, considering other factors such as increased real estate values and energy cost increases in the future.}, journal={JOURNAL OF BUILDING ENGINEERING}, author={Yoon, Yeobeom and Seo, Byeongmo and Mun, Junghyun and Cho, Soolyeon}, year={2023}, month={Jul} }
 @article{yoon_seo_cho_2023, title={Potential Cooling Energy Savings of Economizer Control and Artificial-Neural-Network-Based Air-Handling Unit Discharge Air Temperature Control for Commercial Building}, volume={13}, ISSN={["2075-5309"]}, DOI={10.3390/buildings13051174}, abstractNote={Heating, ventilation, and air-conditioning (HVAC) systems play a significant role in building energy consumption, accounting for around 50% of total energy usage. As a result, it is essential to explore ways to conserve energy and improve HVAC system efficiency. One such solution is the use of economizer controls, which can reduce cooling energy consumption by using the free-cooling effect. However, there are various types of economizer controls available, and their effectiveness may vary depending on the specific climate conditions. To investigate the cooling energy-saving potential of economizer controls, this study employs a dry-bulb temperature-based economizer control approach. The dry-bulb temperature-based control strategy uses the outdoor air temperature as an indicator of whether free cooling can be used instead of mechanical cooling. This study also introduces an artificial neural network (ANN) prediction model to optimize the control of the HVAC system, which can lead to additional cooling energy savings. To develop the ANN prediction model, the EnergyPlus program is used for simulation modeling, and the Python programming language is employed for model development. The results show that implementing a temperature-based economizer control strategy can lead to a reduction of 7.6% in annual cooling energy consumption. Moreover, by employing an ANN-based optimal control of discharge air temperature in air-handling units, an additional 22.1% of cooling energy savings can be achieved. In conclusion, the findings of this study demonstrate that the implementation of economizer controls, especially the dry-bulb temperature-based approach, can be an effective strategy for reducing cooling energy consumption in HVAC systems. Additionally, using ANN prediction models to optimize HVAC system controls can further increase energy savings, resulting in improved energy efficiency and reduced operating costs.}, number={5}, journal={BUILDINGS}, author={Yoon, Yeobeom and Seo, Byeongmo and Cho, Soolyeon}, year={2023}, month={Apr} }
 @article{kim_yang_entchev_cho_kang_lee_2022, title={Hybrid Solar Geothermal Heat Pump System Model Demonstration Study}, volume={9}, ISSN={["2296-598X"]}, DOI={10.3389/fenrg.2021.778501}, abstractNote={In this paper, the development and demonstration of a hybrid solar geothermal heat pump polygeneration system is presented. The poly-generation system has been designed, modeled, and simulated in TRNSYS software environment. Its performance was assessed followed by installation and demonstration at a demo site in Cheongju, Korea. The space heating and cooling load of the building is 13.8 kW in heating mode at an ambient temperature of −10.3°C and 10.6 kW in cooling mode at an ambient temperature of 32.3°C. The simulation data were compared with the field demo data using ISO 13256. The results showed that the model data compare well with the demo data both in heating and cooling modes of operation. At a source temperature of 16.7°C, the heat pump lab performance data-based COPc shows 9.9, while demonstration COPc shows 10.3, thus, representing 4.3% relative error. The heat pump source temperature decreased by 4.0°C from 20.9°C to 16.9°C due to ground heat exchanger coupling and resulted in a COPc increase by 13.3% from 8.5 to 9.8. When compared at the design conditions (outside temperature of 32.3°C), the TRSNYS model overestimated the demonstration site data by 12%, 9.3 vs. 8.1 kW with power consumption of 3.1 vs. 2.2 kW. The hybrid polygeneration system power consumption decreased by 1.2 kW when ambient temperature decreased from 35°C to 25°C.}, journal={FRONTIERS IN ENERGY RESEARCH}, author={Kim, Yu-Jin and Yang, Libing and Entchev, Evgueniy and Cho, Soolyeon and Kang, Eun-Chul and Lee, Euy-Joon}, year={2022}, month={Jan} }
 @article{ha_cho_kim_song_2020, title={Annual Energy Consumption Cut-Off with Cooling System Design Parameter Changes in Large Office Buildings}, volume={13}, ISSN={["1996-1073"]}, DOI={10.3390/en13082034}, abstractNote={A variety of greenhouse gas reduction scenarios have been proposed around the world to ensure sustainable developments and strengthen the global response to the climate change. To cope with this, it is urgently needed to reduce the amount of energy used for the heating, ventilating, air conditioning, and refrigerating (HVAC&R) systems in large buildings. This study discusses the reduction of cooling energy in large office buildings through the minimization of changes in components and equipment, such as heat source equipment and pumps, changes in the layout and operating methods of chilled water circulation pumps, and changes in the temperatures of chilled and condenser water. To do this, this study targeted an entire cooling system consisting of a hydronic system, a chiller, and a cooling tower, and conducted a quantitative analysis of the energy consumption and of the reduction achieved through a change in the pumping system type in the cooling system and a change in the Korean standard design and temperature of chiller and cooling tower via EnergyPlus simulations. The simulation results showed a cooling energy reduction of 103.2 MWh/yr, around 15.7%, where the primary constant-speed system (Case A) was changed to a primary variable-speed pump (Case B) in the configuration with a chilled water circulation pump. To reduce the cooling energy further, annually 142.3 MWh, around 21.7%, Case C in this study changed the outlet temperature of the chiller and temperature difference from 7 °C, 5 K to 9 °C, 9 K. Finally, when applying a change in the condenser water production temperature from 32 to 23.9 °C in accordance with ASHRAE Standard 90.1 for Case D, a cooling energy saving of 182.4 MWh/yr was observed, which is about 27.8%.}, number={8}, journal={ENERGIES}, author={Ha, Ju-wan and Cho, Soolyeon and Kim, Hwan-yong and Song, Young-hak}, year={2020}, month={Apr} }
 @article{seo_yoon_yu_cho_lee_2020, title={Comparative analysis of cooling energy performance between water-cooled VRF and conventional AHU systems in a commercial building}, volume={170}, ISSN={["1359-4311"]}, DOI={10.1016/j.applthermaleng.2020.114992}, abstractNote={With recent efforts to minimize the energy consumption of heating, ventilation and air conditioning (HVAC) systems in buildings, the variable refrigerant flow heat pump (VRF-HP) system has drawn much attention, replacing the conventional HVAC system in the market. The VRF-HP system has several advantages, such as enhancing the aesthetics of buildings, reducing installation costs and offering high technology products. Although various studies relevant to VRF-HP systems are currently in progress, the evaluation of quantitative energy based on the actual performance information of such systems is still insufficient because it is difficult to accurately reflect various factors affecting their inherent heating and cooling performance in a simulation environment during the design stage. To obtain reliable results for this study, a water-cooled VRF-HP system equipped with a direct expansion air handling unit (DX-AHU) was selected from among various types of VRF-HP systems, and a standardized heating and cooling performance curve in diverse temperatures and part-load conditions based on the heating and cooling field measurement catalog performance data was developed. This paper aims to conduct a comparative assessment of the cooling energy performance between a water-cooled VRF-HP system and a chiller based conventional AHU system through co-simulation between EnergyPlus, MATLAB, and BCVTB after an extensive validation and calibration process. The results indicate that the water-cooled VRF-HP system can reduce cooling energy by up to 15% compared with the chiller based conventional AHU system.}, journal={APPLIED THERMAL ENGINEERING}, author={Seo, Byeongmo and Yoon, Yeo Beom and Yu, Byeong Ho and Cho, Soolyeon and Lee, Kwang Ho}, year={2020}, month={Apr} }
 @article{yoon_seo_koh_cho_2020, title={Heating energy savings potential from retrofitting old apartments with an advanced double-skin facade system in cold climate}, volume={14}, ISSN={["2095-1698"]}, DOI={10.1007/s11708-020-0801-1}, number={2}, journal={FRONTIERS IN ENERGY}, author={Yoon, Yeo Beom and Seo, Byeongmo and Koh, Brian Baewon and Cho, Soolyeon}, year={2020}, month={Jun}, pages={224–240} }
 @article{decarolis_jaramillo_johnson_mccollum_trutnevyte_daniels_akin-olcum_bergerson_cho_choi_et al._2020, title={Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts}, volume={4}, ISSN={["2542-4351"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85097654384&partnerID=MN8TOARS}, DOI={10.1016/j.joule.2020.11.002}, abstractNote={The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. The authors are founding team members of a new effort to develop an Open Energy Outlook for the United States. The effort aims to apply best practices of policy-focused energy system modeling, ensure transparency, build a networked community, and work toward a common purpose: examining possible US energy system futures to inform energy and climate policy efforts. Individual author biographies can be found on the project website: https://openenergyoutlook.org/. Many nations have committed to mitigating climate change by designing and implementing policy solutions that enable deep decarbonization of their energy systems. Due to global reliance on fossil fuels, appropriate action requires fundamental and coordinated changes in the way societies generate and use energy. Policy makers face the monumental challenge of crafting effective energy and climate policy in the face of a highly uncertain future. The stakes are high because energy infrastructure often involves large, up-front investments in long-lived assets. Macro-energy system models, which are distinguished from other energy models by their energetic, temporal, and spatial scales,1Levi P.J. Kurland S.D. Carbajales-Dale M. Weyant J.P. Brandt A.R. Benson S.M. Macro-Energy Systems: Toward a New Discipline.Joule. 2019; 3: 2282-2286Abstract Full Text Full Text PDF Scopus (29) Google Scholar provide a systematic way to examine future decarbonization pathways, evaluate technology choices, test the effects and consequences of proposed policies, and explore decisions under future uncertainty. Analyses using these models yield critical insights that inform energy and climate policymaking around the world and underpin influential reports, including the World Energy Outlook by the International Energy Agency,2International Energy AgencyWorld Energy Outlook 2019.https://www.iea.org/reports/world-energy-outlook-2019Date: 2019Google Scholar the Annual Energy Outlook by the US Energy Information Administration,3US Energy Information AdministrationAnnual Energy Outlook 2020.https://www.eia.gov/outlooks/aeo/Date: 2020Google Scholar the Special Report on Global Warming of 1.5°C by the Intergovernmental Panel on Climate Change,4Hoegh-Guldberg, O., Jacob, D., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., Djalante, R., Ebi, K., Engelbrecht, F., Guiot, J., and Hijioka, Y. (2018). Impacts of 1.5 C global warming on natural and human systems. Global warming of 1.5°C. An IPCC Special Report. https://www.ipcc.ch/sr15/.Google Scholar and many others. It is an ongoing challenge for macro-energy system modeling teams to meet the universal and unprecedented policy needs associated with climate change mitigation. We envision a paradigm shift in the process of conducting model-based analysis from single-institution modeling teams to distributed, collaborative teams, allowing access to a much wider array of disciplinary and domain expertise to inform a given analysis. While some European efforts are already moving in this direction, the potential for collaborative, model-based analysis has yet to be realized. Energy system models vary considerably in their scope and complexity, and the choice of model should always be based on the research questions driving the analysis.5DeCarolis J. Daly H. Dodds P. Keppo I. Li F. McDowall W. Pye S. Strachan N. Trutnevyte E. Usher W. Winning M. Formalizing best practice for energy system optimization modelling.Appl. Energy. 2017; 194: 184-198Crossref Scopus (159) Google Scholar Here, we focus attention on employing macro-energy system models that cover the whole energy system and are used to inform policy at scales ranging from national to global. In this broadest macro-scale context, the boundaries of the modeled systems present numerous challenges for modeling deep decarbonization pathways. First, many supply- and demand-side technologies at varying stages of development could help decarbonize energy systems. Many of these technologies are novel (e.g., direct air capture and hydrogen-based steel production), have rapidly changing costs (e.g., solar photovoltaics, lithium-ion batteries, and electrolyzers), or have location-specific attributes (e.g., heat pumps and wind farms). These qualities make the projection of technology cost and performance characteristics over the multi-decade timescale of deep decarbonization very challenging. Second, the many decision makers across the energy system, each with their own objectives and preferences, make it difficult to model technology uptake, behavioral change, and public acceptance. Third, there is a need for modeling with high spatiotemporal resolution and multiple years of weather data in order to properly represent high penetrations of renewables with energy storage and other options for flexibility, since the modeled spatial variation in resource availability and temporal variation in supply and demand can have a significant impact on results. Fourth, policy-relevant insights should account for key underlying uncertainties affecting the modeled energy system. Neglecting any of these four challenges can lead to oversimplified model representations of the energy system with misleading conclusions; yet, including them increases model complexity, data requirements, and computational burden. Resolving this tension, given available resources, is difficult. Addressing the technical challenges of modeling decarbonization pathways requires considerable coordination of effort and broad domain expertise. When the effort is centralized at a single institution, institutional and governance structures can limit its effectiveness. Energy system modeling efforts housed within a single research group can suffer from a limited breadth of expertise. At the other extreme, some of the oldest and most established energy system models have been produced by government agencies and intergovernmental organizations that have the scale to draw on deep internal expertise across the energy system, but model-based analyses produced by these organizations can be subject to political considerations that limit the range of technologies or policies they will consider. In addition, commercial modeling efforts often rely on proprietary models and data that are not available to the broader expert community or interested stakeholders and therefore result in outcomes that cannot be easily reproduced and scientifically verified. To help address these shortcomings, distributed modeling teams can utilize existing open-source models, datasets, and tools to conduct collaborative, model-based analysis. Open-source efforts in the macro-energy space have proliferated over the last decade, and the resultant models, tools, and datasets serve as an important foundation for distributed modeling efforts because they enable transparency, accessibility, and replicability among team members and with the broader modeling community. Distributed efforts focused on model-based analysis allow for the flexible arrangement of teams to conduct different macro-energy modeling exercises, with each team configured to meet project-specific research objectives. The flexible arrangement of teams, in turn, means that specific modeling efforts can include participants with different disciplinary backgrounds and domain expertise who contribute to the diversity of ideas that can be explored in the analysis. The collective consideration of those ideas better reflects the system being modeled. For example, participants with a background in public policy, public administration, or economics can assist with the formulation, execution, and interpretation of more realistic policy scenarios, informed by debates and discussions in their respective communities. Modeling teams with collectively broad expertise across a range of issues and disciplines permit a more comprehensive analysis of the technical, social, economic, and policy features of deep decarbonization pathways, which are difficult to encode in models. In fact, all team members need not write code—the purposeful inclusion of non-modelers can lead to new insights and approaches associated with the model-based analysis.6Trutnevyte E. Hirt F.L. Bauer N. Cherp A. Hawkes A. Edelenbosch O.Y. Pedde S. van Vuuren D.P. Societal transformations in models for energy and climate policy: The ambitious next step.One Earth. 2019; 1: 423-433Abstract Full Text Full Text PDF Scopus (52) Google Scholar Diverse teams participating across the full project life cycle—from the formulation of key research questions, to the decision on how to represent a particular concept quantitatively, and then to the interpretation of model results as policy-relevant insights—can more effectively capture and assimilate novel ideas compared to conventional system modeling approaches that seek feedback at the end of the project or at discrete points during the project life cycle. These insights and ideas can range widely and may include the identification and proper use of a new dataset, a new model feature that captures a system dynamic critical to the issue under analysis, or the use of more efficient algorithms or methods that improve computational performance. Modeling teams that lack the appropriate depth and breadth are less able to effectively search, select, and incorporate new ideas from the broader macro-energy idea space into the analysis. Model parsimony should also be a design objective in order to avoid needless complexity,5DeCarolis J. Daly H. Dodds P. Keppo I. Li F. McDowall W. Pye S. Strachan N. Trutnevyte E. Usher W. Winning M. Formalizing best practice for energy system optimization modelling.Appl. Energy. 2017; 194: 184-198Crossref Scopus (159) Google Scholar and thus, distributed modeling teams must judiciously filter new ideas for incorporation into the analysis. Furthermore, the expanding scope enabled by distributed teams must be balanced with limited time, funding, and computational resources. The European Union is already pioneering a distributed and collaborative approach under the €80 billion Horizon 2020 research and innovation program. Projects such as SET-NAV (https://www.set-nav.eu/), openENTRANCE (https://openentrance.eu/), SENTINEL (https://sentinel.energy/), Spine (http://www.spine-model.org/), and EMP-E (http://www.energymodellingplatform.eu/) involve large teams variously working to integrate different models into larger frameworks, solicit input from a wide array of stakeholders, and perform model-based analysis that informs European energy and climate policy. The European Union is uniquely positioned to lead such efforts, given its ambitious energy-climate policy portfolio, well-funded scientific research programs, and ambitions for pan-national integration. While many other nations and regions—including the US—cannot easily replicate the top-down European approach without a significant change in policy priorities, we nonetheless assert that it is possible for researchers to organize similar efforts from the bottom up by leveraging existing resources within the scientific community. While distributed efforts focused on model-based analysis present unique logistical challenges, they also provide the flexibility to organize teams that capture diverse domain expertise and disciplinary approaches. All of the necessary elements exist to coordinate distributed model-based analysis: open-source energy models, well-established software development tools, a wide range of collaborative communication tools, and an increasing number of publicly available datasets on which to build. First, the open energy modeling initiative (“openmod”), an active and vibrant community of energy modelers committed to open-source practices, has cataloged a large array of open-source models7Openmod InitiativeOpen Models.https://wiki.openmod-initiative.org/wiki/Open_ModelsDate: 2020Google Scholar and helped to promulgate best practice standards for model developers that include licensing, documentation, reproducibility, and user support.8DeCarolis J.F. Hunter K. Sreepathi S. The case for repeatable analysis with energy economy optimization models.Energy Econ. 2012; 34: 1845-1853Crossref Scopus (82) Google Scholar, 9Pfenninger S. Hirth L. Schlecht I. Schmid E. Wiese F. Brown T. Davis C. Gidden M. Heinrichs H. Heuberger C. Hilpert S. Opening the black box of energy modelling: Strategies and lessons learned.Energy Strategy Reviews. 2018; 19: 63-71Crossref Scopus (129) Google Scholar, 10Pfenninger S. DeCarolis J. Hirth L. Quoilin S. Staffell I. The importance of open data and software: Is energy research lagging behind?.Energy Policy. 2017; 101: 211-215Crossref Scopus (192) Google Scholar, 11Morrison R. Energy system modeling: Public transparency, scientific reproducibility, and open development.Energy Strategy Reviews. 2018; 20: 49-63Crossref Scopus (52) Google Scholar Second, many energy modelers are using modern software development tools, which enable distributed control of code and data, with changes archived in publicly accessible web repositories. Third, a variety of communication options, including traditional email, cloud-based collaboration platforms, and videoconferencing software, make it possible for distributed teams to collaborate on highly technical issues in near-real time and at low cost. These modes of communication have indeed become an increasingly familiar part of our lives given how the coronavirus disease (COVID-19) pandemic has disrupted normal meeting patterns. In addition, social media represents a particularly effective way to crowdsource new ideas and approaches from the broader stakeholder community. Fourth, the volume of available data to populate energy models has grown over time and can be used to better parameterize models. The challenge, however, is that modelers are not aware of all relevant datasets, particularly those curated outside of the energy modeling community, nor do they always understand the underlying assumptions and limitations. Diversity in expertise among the modeling team can help ensure the proper identification and use of such datasets. In the long run, by using open-source tools and drawing on the expertise of non-modelers who are typically disconnected from the modeling process, distributed modeling teams may counteract the “incumbency advantage” of “long-lived and dominant” energy models12Strachan N. Fais B. Daly H. Reinventing the energy modelling–policy interface.Nat. Energy. 2016; 1: 1-3Crossref Google Scholar by helping redefine the way energy models operate. We view this approach as a critical element in the reinvention of the modeling-policy interface.12Strachan N. Fais B. Daly H. Reinventing the energy modelling–policy interface.Nat. Energy. 2016; 1: 1-3Crossref Google Scholar As with any new approach, there will be attendant challenges. Macro-energy modeling efforts face the same funding and coordination challenges confronted by other large scientific endeavors. Funding challenges are more logistically difficult with teams spanning multiple institutions. There is no single solution: financial arrangements will necessarily be a product of the funding agency, team composition, and objectives of the analysis. While there may be circumstances where funding can be equitably distributed among all participants, there might be other times when one or two lead organization(s) take the bulk of the responsibility, with smaller support grants and in-kind contributions from other members of the distributed team. Furthermore, funding need not always be a requirement for participation: limited but strategic input from a broad constellation of team members delivered at the right time in the process can have a large, positive impact on the direction of the project. While the Stanford Energy Modeling Forum (https://emf.stanford.edu/) is focused on inter-model comparison, its long-term success demonstrates that participants are willing to contribute their time, often without financial compensation, in return for the opportunity to collaborate with others and produce new scholarly research. Another challenge is the incentive structure within academia. It takes significant upfront effort to establish a common language and align project goals among team members from different academic disciplines. In addition, receiving credit for work completed is an important aspect of scholarly work. Credit often takes the form of co-authorship on reports and journal articles, and it is important to track the contributions of team members to ensure their efforts are recognized in an appropriate way, commensurate with their own institutional and disciplinary incentive structures. Furthermore, academic institutions should formally recognize the effort required to develop the open-source models, tools, and datasets that underpin the model-based analysis. The CRediT taxonomy, used by this publisher (https://www.cell.com/pb/assets/raw/shared/guidelines/CRediT-taxonomy.pdf), provides an excellent way to track the various contributions to distributed macro-energy modeling efforts. New modeling efforts that leverage these emerging opportunities can fulfill a unique niche within the global energy modeling community. We have begun to see the benefits of such an approach in our own effort to develop an Open Energy Outlook for the US (https://openenergyoutlook.org/). In addition to using an open-source modeling platform to perform the analysis (https://temoacloud.com/), we have established an interdisciplinary and inter-sectoral team of experts who are working collaboratively on the project with a unified vision. Our international team involves a number of experts drawn from academia, non-profits, and government labs and includes both experienced macro-energy system modelers and domain experts. Funding is distributed across two institutions that have primary responsibility for the deliverables, while participants from the remaining 20+ institutions make in-kind contributions of their time to the effort. Our project has a fraction of the funding associated with the large European efforts referenced above, and thus relies heavily on our collective interest in the project objectives and the opportunity to collaboratively produce scholarly work. Because participants are already working in related areas, they are able to leverage ongoing research activities and resources for this project. Our current team is meant to be a starting point for this long-term effort. Just as open-source tools foster collaborative development, democratization of the team building process can ensure a greater diversity of perspectives and make the effort more adaptable to new challenges. To this end, we are currently working on a formal and open nomination process for team membership. In addition, we are building a broader network of contributors to the project, and have sought input through a variety of online outlets, including social media, virtual workshops, and mailing lists. While still in the early stages, the project has already benefited from the diverse perspectives of the participants. For example, the electricity experts have pushed for a novel approach to increase the model’s temporal resolution while maintaining computational tractability and also identified opportunities to leverage existing open-source tools (https://github.com/gschivley/PowerGenome) and datasets (https://github.com/catalyst-cooperative/pudl). Likewise, the building experts are pushing the project to consider building thermodynamics more explicitly in order to better represent building thermal performance. The value here is bidirectional: systems modelers gain more familiarity with tools and data within particular sectors, while domain experts gain a better understanding of how their expertise can influence long-term energy scenarios. If done well, such an approach allows us to rethink and redefine common modeling approaches, potentially leading to innovative methods that result in new insights that are rigorously grounded by careful consideration of how the energy system—and all its myriad connections and feedbacks—is modeled. We would like to thank the Alfred P. Sloan Foundation for supporting this work. We also thank the two anonymous reviewers whose detailed and insightful feedback significantly strengthened the manuscript. Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling EffortsDeCarolis et al.JouleFebruary 17, 2021In Brief(Joule 4, 2523–2531; December 16, 2020) Full-Text PDF Open Access}, number={12}, journal={JOULE}, publisher={Elsevier BV}, author={DeCarolis, Joseph F. and Jaramillo, Paulina and Johnson, Jeremiah X. and McCollum, David L. and Trutnevyte, Evelina and Daniels, David C. and Akin-Olcum, Gokce and Bergerson, Joule and Cho, Soolyeon and Choi, Joon-Ho and et al.}, year={2020}, month={Dec}, pages={2523–2526} }
 @article{kim_nam_bae_cho_2020, title={Study on the Performance of Multiple Sources and Multiple Uses Heat Pump System in Three Different Cities}, volume={13}, ISSN={["1996-1073"]}, DOI={10.3390/en13195211}, abstractNote={Various efforts have been made worldwide to reduce energy use for heating, ventilation, and air-conditioning (HVAC) systems and lower carbon dioxide (CO2) emissions. Research and development are essential to ensuring the efficient use of renewable energy systems. This study proposes a multiple sources and multiple uses heat pump (MMHP) system that can efficiently respond to heating, cooling, and domestic hot water (DHW) loads using multiple natural heat sources. The MMHP system uses ground and air heat as its primary heat sources and solar heat for heat storage operations and ground temperature recovery. For the efficient use of each heat source, it also determines the heat source required for operation by comparing the heat source temperatures in the same time zone. A model for predicting the heat source temperatures, electricity use, and coefficient of performance (COP) was constructed through simulation. To analyze the efficiency of the proposed system by comparing the existing air source heat pump with ground source heat pump systems, a performance analysis was conducted by setting regional and system configurations as case conditions. The results demonstrate that the electricity use of the MMHP system was 13–19% and 1–3% lower than those of air source heat pump (ASHP) and ground source (GSHP) systems, respectively. In addition, the MMHP system was the most favorable in regions with a low heating load.}, number={19}, journal={ENERGIES}, author={Kim, Hongkyo and Nam, Yujin and Bae, Sangmu and Cho, Soolyeon}, year={2020}, month={Oct} }
 @article{kim_park_cho_song_2019, title={A Study on Utility of Retrofit that Minimizes the Replacement of Heat-Source System in Large Offices}, volume={12}, ISSN={["1996-1073"]}, DOI={10.3390/en12224309}, abstractNote={In a general building retrofit process, the reinforcement of insulation performance or air-tightness in walls and windows are conducted to reduce the maximum cooling and heating load of buildings. A heat source consists of heat-source equipment and water-pipe systems, which are replaced with high-efficient specification materials. Most of them are simply replaced with the same capacity as the previous heating equipment. This study aims to investigate matters required for decision making in a retrofit plan, such as conducting an investigation of the maximum load reduction in buildings obtained by the retrofit and the capacity of heat-source equipment that reflects the reduction, and the re-use or replacement of the water-pipe system, etc. in advance. This study verified that when the capacity of heat-source equipment was reduced, the pipe diameter of the water-pipe system was also decreased, but if existing pipes were re-used, the transportation power of the pump was reduced due to the reduction in flow velocity. The changes in maximum cooling and heating load through retrofit were quantitatively verified compared to that of the initial design of the building based on previous study results, and flow rates of cool and hot water were determined by re-calculating the capacity of the heat-source equipment. Using the results, the water-pipe system was re-designed, and the annual transportation power of the pump was calculated through simulations. The calculation results verified that the transportation power decreased by up to approximately 10% when oversized pipes were re-used from the existing water-pipe system. Additionally, when the capacity of the heat-source equipment was decreased, reasonable measures considering remodeling, construction duration, and cost were derived.}, number={22}, journal={ENERGIES}, author={Kim, Hyemi and Park, Kyung-soon and Cho, Soolyeon and Song, Young-hak}, year={2019}, month={Nov} }
 @article{seo_yoon_mun_cho_2019, title={Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings}, volume={12}, ISSN={["1996-1073"]}, DOI={10.3390/en12244754}, abstractNote={Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.}, number={24}, journal={ENERGIES}, author={Seo, Byeongmo and Yoon, Yeo Beom and Mun, Jung Hyun and Cho, Soolyeon}, year={2019}, month={Dec} }
 @article{cho_ray_im_honari_ahn_2019, title={Application priority of GSHP systems in the climate conditions of the United States}, volume={13}, ISSN={["1756-2201"]}, DOI={10.1080/17512549.2017.1325403}, abstractNote={ABSTRACT Building energy-performance simulation programs are powerful tools for many aspects of feasibility studies regarding ground source heat pump (GSHP). However, the understanding of the limitations of the energy modelling programs, their capability of predicting energy performance early in the design process, and the complicated functionality of these programs makes the software programs harder to use and less practical. The interactive tool developed in this study seeks to provide analysis information in a straightforward manner that is inexpensive, convenient, and sophisticated. This tool uses an inclusive approach to assess the feasibility of GSHPs by prescreening critical factors such as climate conditions, ground temperatures, energy use, and cost savings. It is interactive and enables the user to do a feasibility analysis with a weighting factor for each feasibility criterion based on the user’s preference and interests. The application of the tool explains feasibility scores of 15 representative cities in various climatic conditions across the US. Results for commercial buildings show that the GSHP systems are more feasible in cold and dry, cool and humid, and very cold areas than warm and dry, very hot and humid, and mixed marine areas, and that most feasibility levels are located on good and moderate.}, number={1}, journal={ADVANCES IN BUILDING ENERGY RESEARCH}, author={Cho, Soolyeon and Ray, Saurabh and Im, Piljae and Honari, Hamed and Ahn, Jonghoon}, year={2019}, pages={1–17} }
 @article{fang_cho_2019, title={Design optimization of building geometry and fenestration for daylighting and energy performance}, volume={191}, ISBN={0038-092X}, DOI={10.1016/j.solener.2019.08.039}, abstractNote={With the increasing demand for sustainable design and green buildings, building performance is having a greater influence on design decisions. Design decisions on building envelop, especially on building geometry, window and skylight size and placement are essential in the early design stage. This research proposes a building performance optimization process that can help designers simultaneously evaluate the daylighting and energy performance of numerous design options and generate optimized design. The proposed method utilizes parametric design, building simulation modeling, and genetic algorithms. A case study of a small office building is conducted to test and verify the effectiveness of the optimization process. The geometry of the case study building is optimized in three different climates, Miami, Atlanta, and Chicago. After the optimization, the daylighting performance metric UDI is increased by 38.7%, 31.6%, and 28.8%, and the energy performance metric EUI is decreased by 20.2%, 18.5%, and 17.9% compared to average performance values. Sensitivity analysis is performed to analyze the relationship between design variables and performance metrics. The skylight width and length are the most important variables for all locations, while the influence of the other variables varies greatly.}, journal={SOLAR ENERGY}, author={Fang, Yuan and Cho, Soolyeon}, year={2019}, month={Oct}, pages={7–18} }
 @article{rossi_oliveira favretto_grassi_decarolis_cho_hill_soares chvatal_ranjithan_2019, title={Metamodels to assess the thermal performance of naturally ventilated, low-cost houses in Brazil}, volume={204}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2019.109457}, abstractNote={Building performance simulation [BPS] tools are important in all design stages. However, barriers such as time, resources, and expertise inhibit their use in the early design stages. This study aims to develop, as part of decision-support framework, metamodels to assess the thermal discomfort in a naturally ventilated Brazilian low-cost house during early design. The metamodels predict the degree-hours of discomfort by heat and/or by cold as a function of design parameters for three Brazilian cities: Curitiba, São Paulo, and Manaus. The key design parameters, related with passive design strategies, are building orientation, shading devices position and dimensions, thermal properties of the walls and roof, window-to-wall ratio, and effective window ventilation area. The method consists of three main stages: (i) baseline model development; (ii) Monte Carlo simulation; (iii) multivariate regression. Overall, the metamodels showed R2 values higher than 0.95 for all climates, except the ones predicting discomfort by heat for Curitiba (R2 =0.61) and São Paulo (R2 =0.75). The proposed metamodels can quickly and accurately assess the thermal performance of naturally ventilated low-cost houses. They can be used to guide professionals during the early design stages, and for educational purposes in building design pedagogy.}, journal={ENERGY AND BUILDINGS}, author={Rossi, Michele Marta and Oliveira Favretto, Ana Paula and Grassi, Camila and DeCarolis, Joseph and Cho, Soolyeon and Hill, David and Soares Chvatal, Karin Maria and Ranjithan, Ranji}, year={2019}, month={Dec} }
 @article{kwon_lee_cho_2019, title={Numerical Study of Balancing between Indoor Building Energy and Outdoor Thermal Comfort with a Flexible Building Element}, volume={11}, ISSN={["2071-1050"]}, DOI={10.3390/su11236654}, abstractNote={This study analyzed the environmental role of a flexible canopy as a microclimate modifier in balancing indoor energy demands and outdoor thermal comfort. Flexible building elements are often installed in traditional buildings, depending on the local climate in southern Europe. The architectural performance of a canopy was analyzed using several environmental software packages (Ecotect, Rayman, WinAir, DaySim, and EDSL TAS). Coupling methods were applied to determine the environmental influence of the attached building element, a canopy with fixed and operable panes in different orientations and locations. The results showed that the flexible canopy played a crucial role in reducing indoor energy demands (heating and electricity for lighting) and increasing outdoor thermal comfort under the canopy area. Outdoor thermally comfortable conditions ranging between 13 and 29 °C in the canopy space could be enhanced by 56.3% over the entire year by manipulating a flexible canopy, compared with a fixed canopy with 90% transparency in London. The flexible canopy with higher transparency helped increase outdoor thermal comfort in Glasgow, while one with lower transparency showed better performance during summer in London. The findings of this research will help broaden the range of architectural elements used in buildings.}, number={23}, journal={SUSTAINABILITY}, author={Kwon, Choul Woong and Lee, Kang Jun and Cho, Soolyeon}, year={2019}, month={Dec} }
 @article{yoon_seo_koh_cho_2019, title={Performance analysis of a double-skin facade system installed at different floor levels of high-rise apartment building}, volume={26}, ISSN={["2352-7102"]}, DOI={10.1016/j.jobe.2019.100900}, abstractNote={This paper introduces a Double Skin Façade (DSF) system that can be installed in existing apartments in South Korea as a replacement of poorly performing old balcony windows. The DSF system can bring thermal benefits, especially in heating dominant climate areas. The DSF system works as a thermal buffer area and passive heating system. The goal of this research is to evaluate the thermal performance of a DSF system installed in apartments at different floor levels. A typical 25-story apartment building is used as a case study to test the thermal performance of a DSF system in different floors. Heating energy savings are the focus since the location, Seoul, is a heating dominant climate area. The main parameters are temperature, wind speed, and pressure differences at different floor levels. A thermal simulation model for a Base-Case is developed and calibrated to measured data gathered from a real-scale DSF system physical model. Two other simulation models are developed on top of the Base-Case model to compare performances of the DSF system installed in apartments at different floor levels. Results show that the first floor apartment unit consumes the least heating energy and the 25th floor the most, as expected. The outside air temperature difference between the first floor and the 25th floor was about 0.4 °C. The results also show the largest heating energy savings of 30% in the 21st floor with the installation of the DSF system.}, journal={JOURNAL OF BUILDING ENGINEERING}, author={Yoon, Yeo Beom and Seo, Byeongmo and Koh, Brian Baewon and Cho, Soolyeon}, year={2019}, month={Nov} }
 @article{gaballa_cho_2019, title={Prediction of hourly solar radiation using temperature and humidity for real-time building energy simulation}, volume={1343}, ISSN={["1742-6596"]}, DOI={10.1088/1742-6596/1343/1/012049}, abstractNote={Abstract}, journal={CLIMATE RESILIENT CITIES - ENERGY EFFICIENCY & RENEWABLES IN THE DIGITAL ERA (CISBAT 2019)}, author={Gaballa, Hany and Cho, Soolyeon}, year={2019} }
 @article{ahn_chung_cho_2018, title={Energy cost analysis of an intelligent building network adopting heat trading concept in a district heating model}, volume={151}, ISSN={["1873-6785"]}, DOI={10.1016/j.energy.2018.01.040}, abstractNote={With the help of advanced technologies, district heating systems were gradually improved to increase fuel use efficiency and reduce environmental impacts. Several control strategies focused on the performance improvement of energy generation or distribution in the network. However, most strategies overlooked zone-scaled thermal comfort related to occupant characteristics, and there was lack of studies combined with heat trading concepts. This research presents energy cost analysis of an intelligent network model and heat trading to mitigate increases of energy use and thermal dissatisfaction in a district model. Advanced thermal control algorithms provide optimized supply air conditions responding to occupant characteristics in different buildings. A distribution model contributes to the reduction of energy consumption, and heat trading effects are analyzed to maximize energy cost savings in a district model. In comparison with a conventional model, an intelligent controller improves thermal comfort by about 16.2% for a clinic, 2.4% for an office, and 7.1% for a residential building, respectively. Also, a distribution model with heat trading concept saves total energy costs by about 24.7%, theoretically. In conclusion, the model has advantages that it properly responds to occupant characteristics to mitigate thermal dissatisfaction, and it has a great potential to reduce total energy costs.}, journal={ENERGY}, author={Ahn, Jonghoon and Chung, Dae Hun and Cho, Soolyeon}, year={2018}, month={May}, pages={11–25} }
 @article{park_cho_ahn_2018, title={Improving the quality of building spaces that are planned mainly on loads rather than residents: Human comfort and energy savings for warehouses}, volume={178}, ISSN={["1872-6178"]}, DOI={10.1016/j.enbuild.2018.08.007}, abstractNote={The US Census Bureau reported the information that over ten thousand large commercial warehouses were being operated, and that over five hundred thousand workers were employed in related services in 2008. According to the US Bureau of Labor Statistics, the number of persons employed increased as nearly nine hundred thousand in November 2016, which implies the fact that demands of warehouses will increase in keeping with the growth of logistics industry. However, the necessity for energy savings was recognized as less important because the average energy use intensity of warehouses in the US was 55% and 70% less than those of office buildings and retail stores, respectively. Also, the improvement of indoor thermal environment for workers was often overlooked in comparison with safety, speed, and space efficiency. This research proposes a study for a warehouse building to mitigate both thermal dissatisfaction and energy use through the network based real-time analysis. In order to optimize heating and cooling supply, an algorithm for simultaneous control of the amount of air and its temperature is designed, and a neural network model that learns the algorithm is generated. Also, an inner algorithm for thermal comfort analyses real-time temperature levels and rectifies the model's control signals to mitigate thermal dissatisfaction. By comparing results, this research concludes advantages of a neural network model with estimating thermal comfort. The model reduces thermal dissatisfaction by 21.2% and saves energy use by 6.4% in comparison with the conventional thermostat on/off controller equipped in most buildings. Without compromising thermal comfort for workers, the proposed model that consists of two independent structures for optimizing supply air and estimating thermal comfort can contribute to the improvement of thermal performance for warehouses.}, journal={ENERGY AND BUILDINGS}, author={Park, Sung-yong and Cho, Soolyeon and Ahn, Jonghoon}, year={2018}, month={Nov}, pages={38–48} }
 @article{ahn_chung_cho_2018, title={Network-based energy supply optimal system in the condition where both heating and cooling are required simultaneously in a swing season}, volume={10}, ISSN={["1756-6932"]}, DOI={10.1080/17508975.2017.1328657}, abstractNote={ABSTRACT Recent building control models have adopted advanced algorithms to replace conventional controllers to improve the performance of plant or system levels. However, most models used to optimize fuel use or fan speed have several disadvantages to respond to, accurately and promptly, zone-scaled level. This paper proposes network-based controllers utilizing Artificial Neural Network (ANN) in space cooling and heating through the simultaneous control of the amount of supply air and its temperature. Two controllers are developed to evaluate the optimum of supply air conditions for a swing season that requires both moderate heating and cooling and they are compared to conventional thermostat on/off model by using the total control errors and energy consumption for operating damper and resistance coil. The result describes the advantage of the ANN simultaneous control that control errors reduce by 61.5% and energy consumption decreases by 3.5% in comparison with on/off controller. The ANN controller effectively optimizes the supply air conditions to reduce control errors and energy consumption, as they relate to human comfort and energy savings in a swing season.}, number={1}, journal={INTELLIGENT BUILDINGS INTERNATIONAL}, author={Ahn, Jonghoon and Chung, Dae Hun and Cho, Soolyeon}, year={2018}, pages={42–57} }
 @article{boahen_lee_cho_choi_2017, title={A Study on the Evaluation of the Annual Energy Consumption for a Geothermal Heat Pump System with Open Loop and Closed Loop Ground Heat Exchangers}, volume={25}, ISSN={["2010-1333"]}, DOI={10.1142/s2010132517500249}, abstractNote={ Heating and cooling systems contribute greatly to the energy consumption and CO2 emissions of many countries. Ground source heat pumps (GSHP) are promising energy saving systems for residential, commercial or industrial heating or cooling purposes. A method to estimate the energy consumption and CO2 emission of GSHPs is therefore very eminent. This paper reviews the methodology to calculate the energy consumption and CO2 emission of GSHPs. The discussed methodology is then used to compare the energy consumption and CO2 emission of an open-loop and closed-loop GSHP using data from field test. It is observed that the open-loop GSHP saves 28% energy and reduces CO2 by 28% than the closed-loop GSHP in the cooling season. When used for both cooling and heating purposes in the year, the open-loop GSHP saves about 6% energy and reduces about 6% of CO2 emission than the closed-loop GSHP. }, number={3}, journal={INTERNATIONAL JOURNAL OF AIR-CONDITIONING AND REFRIGERATION}, author={Boahen, Samuel and Lee, Kwang Ho and Cho, Soolyeon and Choi, Jong Min}, year={2017}, month={Sep} }
 @article{ahn_cho_chung_2017, title={Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands}, volume={190}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2016.12.155}, abstractNote={This paper presents hybrid control approaches for heating air supply in response to changes in demand by using the Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) fitting models. Since early 2000’s, some advanced computing and statistical tools were introduced to replace conventional control models in improving control and energy efficiency. Among the tools, the FIS and ANN algorithms were used to define complex interactions between inputs and outputs, and were able to facilitate control models to predict or evaluate precise thermal performance. This paper introduces the FIS and ANN control schemes for simultaneously controlling the amount of supply air and its temperature. Input and output data derived from the FIS results generate and validate the ANN model, and both models are compared to the typical thermostat on/off baseline control to evaluate conditions of supply air for a heating season. The differences between the set-point and actual room temperature and their sums indicate control efficiency, and the heat gains into a room and their sums define the energy consumption level. This paper concludes that the simultaneous control of mass and temperature maintains the desired room temperature in a highly efficient manner. Sensitive controls may have a disadvantage in terms of energy consumption, but the ANN controller can minimize energy consumption in comparison with simple thermostat on/off controller. The results also confirm the effectiveness of simultaneous control of mass and temperature using an ANN algorithm corresponding to intermittent or unpredicted changes in thermal demands.}, journal={APPLIED ENERGY}, author={Ahn, Jonghoon and Cho, Soolyeon and Chung, Dae Hun}, year={2017}, month={Mar}, pages={222–231} }
 @article{ahn_cho_2017, title={Anti-logic or common sense that can hinder machine's energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments}, volume={204}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2017.06.079}, abstractNote={In spite of the remarkable development of technology, most studies for building energy controls to evaluate or estimate the energy performance have not accurately reflected actual building’s energy consumption patterns. For this issue, several techniques, such as simulation and calibration, comprehensive survey system, smart metering, and commissioning, have been attempted. However, in most studies, some factors in thermal systems derived from occupant behavior were perceived as fixed objects, and the factors were converted into simple numbers as parts of inputs into simulation templates. There was lack of studies on considerations that unpredictable responses derived from human anti-logic or common sense could deteriorate energy efficiency in theoretical analyses even though the systems were properly operated. This research proposes integrated energy supply models based on artificial intelligence responding to anti-logic or common sense that can reduce machine’s energy saving effects. By use of design scenarios assuming some unusual situations, a decision making model determines the extent to which the cause of the abnormal situations are associated with the occupant behavior. After the five-step phases in the decision making model, the actual outputs of the energy supply model for the buildings are determined, and the reciprocal communication between the thermal and decision making models mitigates thermal dissatisfaction and energy inefficiency. Comparative analysis describes the decision making model’s effectiveness that it improves thermal comfort levels by about 2.5% for an office building and about 10.2% for residential buildings, and that it reduces annual energy consumption by about 17.4% for an office building and about 25.7% for residential buildings. As a consequence, the integrated energy control model has advantages that it noticeably improves thermal comfort and energy efficiency, and that it properly respond to abnormal and abrupt indoor situations derived from human anti-logic or common sense.}, journal={APPLIED ENERGY}, author={Ahn, Jonghoon and Cho, Soolyeon}, year={2017}, month={Oct}, pages={117–130} }
 @article{ahn_cho_2017, title={Dead-band vs. machine-learning control systems: Analysis of control benefits and energy efficiency}, volume={12}, ISSN={["2352-7102"]}, DOI={10.1016/j.jobe.2017.04.014}, abstractNote={In residential and commercial buildings, thermostat controllers have been typically utilized to maintain room temperature near desired set-point. Recently, advanced computing and statistical technologies, such as Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) algorithms, were introduced to complement the control performance for optimal energy use in building thermal systems. However, most schemes were developed to control fuel use or fan motor speed in a plant or a system, and showed some disadvantages to immediately respond to sensitive changes in thermal demands for a zone scaled level. This paper introduces heating energy models capable of controlling the amount of supply air and its temperature simultaneously, and the FIS and ANN algorithms are developed to control the optimal supply air conditions for a heating season. Both the FIS and ANN models are compared to thermostat controllers with 4-step dead-band setups from normal to sensitive levels. The sum of errors, caused by the difference between desired set-point and controlled room temperatures, and the amount of energy supply are used to define control precision and energy efficiency of the control models. From the simulation results, the machine-learning based ANN controller averagely reduces control errors by 88% and mitigates increases in energy consumption by 2% in comparison with thermostat on/off controllers. The control system can be effective when various sensitive settings are required as a type of buildings and rooms without an excessive increase in energy use.}, journal={JOURNAL OF BUILDING ENGINEERING}, author={Ahn, Jonghoon and Cho, Soolyeon}, year={2017}, month={Jul}, pages={17–25} }
 @article{ahn_cho_2017, title={Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system}, volume={124}, ISSN={["1873-684X"]}, DOI={10.1016/j.buildenv.2017.07.040}, abstractNote={District heating systems were gradually improved with the development of generation, storage, distribution technologies, and the demands continued to expand significantly. The percentage of houses supplied by district heating systems were fast grown up, and it was reported that the global market for the systems would expand by about 6% in the period between 2016 and 2024. However, most studies for district heating models focused on fuel use in plants, energy distribution, and carbon reduction. Many simulations adopting computing technologies dealt with mechanical performances in the systems. Also, recent statistical analyses overlooked zone-scaled thermal comfort directly affecting users' workability in buildings. This research proposes an intelligent controller to improve thermal comfort and reduce peak energy demands in a district heating system. An artificial intelligence based model with temperature and thermal comfort detectors optimizes supply air conditions to maintain desired room temperature responding to users' characteristics in four different building types. The model reduces peak demands for cooling and heating to optimize plant and distribution capacity. Comparative analyses describe the model's effectiveness that it improves thermal comfort level by 27%, and that it reduces peak energy demands by 30% in comparison with a conventional on/off controller. The model has an advantage that it properly responds to temperature changes with high performance to mitigate thermal dissatisfaction and energy loss. In spite of the sensitive controls to ensure human comfort, it is confirmed that the model can contribute to design optimization for energy supply systems in urban scaled models.}, journal={BUILDING AND ENVIRONMENT}, author={Ahn, Jonghoon and Cho, Soolyeon}, year={2017}, month={Nov}, pages={57–68} }
 @article{cho_ray_im_honari_ahn_2017, title={Methodology for energy strategy to prescreen the feasibility of Ground Source Heat Pump systems in residential and commercial buildings in the United States}, volume={18}, ISSN={2211-467X}, url={http://dx.doi.org/10.1016/J.ESR.2017.09.012}, DOI={10.1016/J.ESR.2017.09.012}, abstractNote={Geothermal resources have potential to reduce dependence on fossil fuels. The viability of geothermal heat pumps or ground source heat pumps (GSHPs) is significant as a potential alternative energy source with substantial savings potential. While the prospect of these systems is promising for energy efficiency, careful feasibility analysis is required before implementation. This paper presents the results of evaluation of the application feasibility for GSHPs in buildings across seven climate zones in three United States regions. A comprehensive methodology is developed to measure the integrated feasibility of GSHPs using compiled data for energy use intensity, energy cost and design parameters. Four different feasibility metrics are utilized: ground temperature, outdoor weather condition, energy savings potential, and cost benefits. For each metric, a corresponding feasibility score system is developed. The defined integrated feasibility score classifies the locations into five different feasibility levels ranging from Fair (0–20), Moderate (21–40), Good (41–60), High (61–80), and Very High (81–100). Conclusions show the GSHP feasibility level is High for 3 sites, Good for 8 sites and Moderate for 4 sites. Through the methodology, it is possible to develop a practical energy strategy for more economic and sustainable GSHP systems at an early design stage in the various viewpoints of geometries, climate conditions, operational factors, and energy costs.}, journal={Energy Strategy Reviews}, publisher={Elsevier BV}, author={Cho, Soolyeon and Ray, Saurabh and Im, Piljae and Honari, Hamed and Ahn, Jonghoon}, year={2017}, month={Dec}, pages={53–62} }
 @article{ahn_chung_cho_2017, title={Performance analysis of space heating smart control models for energy and control effectiveness in five different climate zones}, volume={115}, ISSN={["1873-684X"]}, DOI={10.1016/j.buildenv.2017.01.028}, abstractNote={This paper compares smart control models for heating supply air among five different climate conditions to discuss the effectiveness of machine learning tools in terms of control and energy efficiency. A thermostat on/off control is typically used to maintain room temperature at a desired level. Advanced computing technologies have recently been introduced to complement the conventional on/off controls to improve control efficiency in heating systems. However, these methods, which were mostly utilized to control fuel amount or fan motor speed, lacked the capability to promptly respond to various outdoor temperature conditions as climate zones requiring refined control strategies to reduce environmental impacts. This paper proposes intelligent controls of mass and temperature simultaneously for heating air supply. The Fuzzy Inference System (FIS) and Artificial Neural Network (ANN) algorithms are utilized to develop six control models, and the models are tested to evaluate both control and energy efficiency during the winter season in five climate zones (from climate zone 2 through 6; i.e., Houston, Dallas, Raleigh, Chicago, and Detroit, respectively). Results include the energy consumption, control errors, and control signals in comparison to the baseline on/off control, which confirms the fact that the ANN simultaneous controls of mass and temperature is more effective than the other controllers for control accuracy and energy savings by 71.3% and 0.3%, respectively. The effectiveness of the ANN controller can contribute to maintaining room temperature accompanying the reduction of energy consumption, which is directly related to improve human comfort and reduce environmental impacts in various climate zones.}, journal={BUILDING AND ENVIRONMENT}, author={Ahn, Jonghoon and Chung, Dae Hun and Cho, Soolyeon}, year={2017}, month={Apr}, pages={316–331} }
 @article{ahn_cho_chung_2016, title={Development of a statistical analysis model to benchmark the energy use intensity of subway stations}, volume={179}, ISSN={["1872-9118"]}, DOI={10.1016/j.apenergy.2016.06.065}, abstractNote={This paper presents an Energy Use Intensity (EUI) indicator model for energy benchmarking subway stations. Among the mass transportation systems, a subway, in terms of its rapidity, punctuality, and efficiency, has been preferred in metropolitan area and recently spotlighted as it mitigates environmental impacts to global warming. Of its several advantages, a subway’s carbon footprint is negligible, which directly contributes to energy savings. Therefore, demands of subway systems have increased. However, subway stations have rarely been included in most energy performance studies and surveys. Due to a lack of information and design complexity, most designers are not able to do optimal design practices. A statistical model was developed in this study using the benchmark process for 157 actual subway stations in Seoul, South Korea. It includes measured data, utility bills, simulation results, and regression modeling. This adjusted EUI benchmark model was developed using characteristics of subway stations and a statistical validation process. The effectiveness of the model is tested and verified by comparing between measured EUI and adjusted normalized EUI (EUInorm) of actual subway stations. This paper includes the test results of EUI indicator model to benchmark energy performance and assesses existing subway station.}, journal={APPLIED ENERGY}, author={Ahn, Jonghoon and Cho, Soolyeon and Chung, Dae Hun}, year={2016}, month={Oct}, pages={488–496} }
 @misc{martinez-molina_tort-ausina_cho_vivancos_2016, title={Energy efficiency and thermal comfort in historic buildings: A review}, volume={61}, ISSN={["1879-0690"]}, DOI={10.1016/j.rser.2016.03.018}, abstractNote={In recent years, energy efficiency and thermal comfort in historic buildings have become high-interest topics among scholars. Research has demonstrated that retrofitting buildings to current energy efficiency and thermal comfort standards is essential for improving sustainability and energy performance and for maintaining built heritage of historic structures. This study is an extensive overview of the literature surrounding this topic. This paper summarizes the different methods and techniques that have been used around the world to achieve performance refurbishments. Articles are organized based on the different building types used as case studies (residential, religious, academic and palace, museums, libraries and theaters, urban areas, and others). The results reveal that residential, religious and museum building types, especially from the last two centuries, have been most often used as case studies. Moreover, Europe, particularly Italy, is leading the research. The aim of this note is to demonstrate the feasibility of maintaining built heritage values of historic buildings while achieving significant improvements in their energy efficiency and thermal comfort.}, journal={RENEWABLE & SUSTAINABLE ENERGY REVIEWS}, author={Martinez-Molina, Antonio and Tort-Ausina, Isabel and Cho, Soolyeon and Vivancos, Jose-Luis}, year={2016}, month={Aug}, pages={70–85} }
 @article{cho_kang_lee_2015, title={Energy savings analysis of fuel-cell microgeneration systems with ground source heat pumps in load-sharing buildings}, volume={10}, ISSN={["1748-1325"]}, DOI={10.1093/ijlct/ctu009}, abstractNote={This paper presents the potential energy savings of implementing a combination of fuel-cell microgeneration (FCMG) systems and ground source heat pump (GSHP) systems in load-sharing buildings. The energy modeling and simulation technology is used to evaluate the effectiveness of FCMG systems in buildings. There are a number of simulation programs to evaluate the performance of buildings with various electric, mechanical and thermal systems. However, it is still a challenge to model and simulate the FCMG systems using the existing whole-building simulation programs. This paper first overviews the current technology of simulation modeling of FCMG, and then presents the results of energy savings analyses obtained from the FCMG systems.}, number={4}, journal={INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES}, author={Cho, Soolyeon and Kang, Eun Chul and Lee, Euy Joon}, year={2015}, month={Dec}, pages={405–411} }
 @article{cho_lee_kang_lee_2013, title={Energy simulation modeling and savings analysis of load sharing between house and office}, volume={54}, ISSN={["0960-1481"]}, DOI={10.1016/j.renene.2012.08.058}, abstractNote={Abstract This paper presents the potential benefits of the thermal load sharing between the two different building types such as residential house and commercial office building through the process of energy simulation modeling. Both the house and office simulation models have the same geometries with the conditioned spaces of 200 m2 each for the weather conditions of Seoul, South Korea. This study shows and analyzes the thermal energy demand and consumption results simulated from the four different scenarios using the EnergyPlus V6.0 thermal simulation program; i.e., Case-1) a house with conventional heating and cooling systems, Case-2) an office with conventional heating and cooling systems, and Case-3) a simple sum of the two cases (i.g., Case-2 + Case-3), and Case-4) a load sharing model that provides heating and cooling to both the house and the office using combined HVAC systems. This paper evaluates the thermal energy consumption patterns and potential benefits of the load sharing system compared to the conventional systems. The optimal system configurations of the load sharing systems are proposed. In conclusion, this paper discusses the potential issues and challenges for implementing the load sharing systems as well as the possible solutions for these issues.}, journal={RENEWABLE ENERGY}, author={Cho, Soolyeon and Lee, Kwang Ho and Kang, Eun Chul and Lee, Euy Joon}, year={2013}, month={Jun}, pages={70–77} }
 @article{claridge_turner_liu_deng_wei_culp_chen_cho_2004, title={Is Commissioning Once Enough?}, volume={101}, ISSN={0199-8595 1546-0118}, url={http://dx.doi.org/10.1080/01998590409509270}, DOI={10.1080/01998590409509270}, abstractNote={ABSTRACT Over the last decade, the Energy Systems Laboratory has developed a commissioning process called Continuous Commissioning®. This process is used to resolve operating problems, improve comfort, optimize energy use, and sometimes to recommend retrofits. The process has produced average energy savings of about 20 percent without significant capital investment in over 150 large buildings in which it has been implemented. Payback has virtually always been under 3 years, with two years or less in most buildings. This article describes the process and presents a case study that explicitly shows the value of follow-up consumption tracking and commissioning. Examination of 20 building-years of heating and cooling consumption data from commissioned buildings found an overall increase in heating and cooling of 12.1 percent over two years. Almost 75 percent of this increase was caused by significant component failures and/or control changes that did not compromise comfort, but caused large changes in consump...}, number={4}, journal={Energy Engineering}, publisher={Informa UK Limited}, author={Claridge, David E. and Turner, W. D. and Liu, Mingsheng and Deng, Song and Wei, Guanghua and Culp, Charles and Chen, Hui and Cho, SoolYeon}, year={2004}, month={Jul}, pages={7–19} }