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Interconnectivity and interoperability are very important features in the development of integrated energy management systems for industrial facilities. A simple and common strategy for exchanging energy-related information among the entities in a facility is currently lacking. To this end, the purpose of this study is to present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for industrial customers. Additionally, we developed and implemented an IoT-based energy-management platform based on a common information model and open communication protocols, which takes advantage of integrated energy supply networks to deploy DR energy management in an industrial facility. The experimental results of this study demonstrate that the proposed platform can not only improve the interconnectivity of the entities in industrial energy management systems but also reduce the energy costs of industrial facilities.

Interconnectivity and interoperability are very important features in the development of integrated energy management systems for industrial facilities. A simple and common strategy for exchanging energy-related information among the entities in a facility is currently lacking. To this end, the purpose of this study is to present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for industrial customers. Additionally, we developed and implemented an IoT-based energy-management platform based on a common information model and open communication protocols, which takes advantage of integrated energy supply networks to deploy DR energy management in an industrial facility. The experimental results of this study demonstrate that the proposed platform can not only improve the interconnectivity of the entities in industrial energy management systems but also reduce the energy costs of industrial facilities.

Abstract Energy harvesting in natural environment has attracted a great deal of attention to generate stable and continuous electrical energy. In this work, we proposed an advanced pyroelectric energy harvesting system by using form-stable phase change material (PCM) composites. The PCM composite connected pyro-electrode generated electrical polarization due to the change of external environment. Polyethylene glycol (PEG) and 1-tetradecanol (1-TD) composites with different phase transition field induced the temperature difference during light-on/-off process. Poly(vinylidene difluoride) (PVDF) was utilized for pyroelectric energy harvesting. The PVDF based pyro-electrode was applied changing the conditions of solar light irradiation and heat air flow. The PCM composites controlled the temperature fluctuation effectively and generated stable output electrical voltage and current. Numerical simulation was carried out to provided in-depth insight into the underlying physics of the system. We envisage that the developed thermal energy harvesting system can pave a way towards high-throughput and sustainable energy harvesting.

Abstract Energy harvesting in natural environment has attracted a great deal of attention to generate stable and continuous electrical energy. In this work, we proposed an advanced pyroelectric energy harvesting system by using form-stable phase change material (PCM) composites. The PCM composite connected pyro-electrode generated electrical polarization due to the change of external environment. Polyethylene glycol (PEG) and 1-tetradecanol (1-TD) composites with different phase transition field induced the temperature difference during light-on/-off process. Poly(vinylidene difluoride) (PVDF) was utilized for pyroelectric energy harvesting. The PVDF based pyro-electrode was applied changing the conditions of solar light irradiation and heat air flow. The PCM composites controlled the temperature fluctuation effectively and generated stable output electrical voltage and current. Numerical simulation was carried out to provided in-depth insight into the underlying physics of the system. We envisage that the developed thermal energy harvesting system can pave a way towards high-throughput and sustainable energy harvesting.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Abstract The South Korean government has been actively promoting an educational-facility improvement program as part of its energy-saving efforts. This research seeks to develop a decision support model for selecting the facility expected to be effective in generating energy savings and making the facility improvement program more effective. In this research, project characteristics and electric-energy consumption data for the year 2009 were collected from 6282 elementary schools located in seven metropolitan cities in South Korea. In this research, the following were carried out: (i) a group of educational facilities was established based on electric-energy consumption, using a decision tree; (ii) a number of similar projects were retrieved from the same group of facilities, using case-based reasoning; and (iii) the accuracy of prediction was improved, using the combination of genetic algorithms, the artificial neural network, and multiple regression analysis. The results of this research can be useful for the following purposes: (i) preliminary research on the systematic and continuous management of educational facilities’ electric-energy consumption; (ii) basic research on electric-energy consumption prediction based on the project characteristics; and (iii) practical research for selecting an optimum facility that can more effectively apply an educational-facility improvement program as a decision support model.

Abstract Because of the increased need for virtual analysis during the vehicle manufacturing processes, more stringent optimization methods are required for the simulation field. Owing to the use of big data from engine testing, 1D analysis can provide more powerful approaches in the conceptual design phase for car makers. In this study, system-level optimization of dual-loop EGR was performed at both the engine and vehicle level, whereas our previous research had been performed at the engine level. Depending on the virtually developed engine and vehicle models and control scheme from our previous works, improved driving capabilities could be observed under light-duty diesel vehicle systems under the world-harmonized light-vehicles test procedure (WLTP). The numerical model was extended through two steps. The first step includes model conversion from the base engine model with an HP EGR system to a virtual engine model with a dual-loop EGR system. The second step represents mode extension from the virtual engine model with a dual-loop EGR system to a vehicle model with a dual-loop EGR system. Optimizing the dominant parameters and using design of experiment (DoE)-based multi-objective Pareto optimization methods in each step, fuel economy could be improved by approximately, 1.5% and the deNOX rate was approximately 5% that of the conventional NEDC. It is implied that the dual-loop EGR system and gear strategy could improve vehicle performance under difficult driving conditions.