• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.3
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• pp.515-524
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• 2010

Recently, many actions are taking to accelerate progress toward social consensus and implementation of Smart Grid. Smart Grid refers to a evolution of the electricity supply infrastructure that monitors, protects, and intelligently optimize the operation of the interconnected elements including various type of generators, power grid, building/home automation system and end-use consumers. The most distinguished element will be Advanced Metering Infrastructure (AMI) that will be installed to every end-use consumer's home or building and optimize the energy consumption of the end-use consumer. The key function of AMI is energy management capability that coordinates and optimally controls the various loads according to the operating condition and environments. In this study, we figure out the basic function of AMI in Smart Energy House that can be defined as a model house implementing in Smart Grid. This paper proposes the energy management algorithm that will be implemented in AMI at Smart Energy House. The paper also show how energy saving in Smart Energy House can be achieved applying the proposed algorithm to an actual house model that has mainly lighting, air-conditioning, TV loads.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.838-843
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• 2008

Cause of struggling to escape from dependency of fossil fuels, the fuel cell and the Plug-in Hybrid Electric Vehicle (PHEV) draw attention in the all of the world. Especially, the Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems have been anticipated for next generation's energy supplying system, and we can predict the PHEV will enlarge the market share in the next few years to reduce not only the air pollution in the metropolis but the fuel-expenses of commuters. This paper presents simulation results about the strategy of smart charging system for PHEV in the residential house which has 1 kW PEMFC cogeneration system. The smart charging system has a function of recommending the best time to charge the battery of PHEV by the lowest energy cost. The simulated energy cost for charging the battery based on the electricity demand data pattern in the house. The house which floor area is $132\;m^2$ (40 pyeong.). In these conditions, the annual gasoline, electricity, and total energy cost to fuel the PHEV versus Conventional Vehicle (CV) have been simulated in terms of cars' average life span in Korea.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.1
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• pp.100-109
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• 2012

In this paper, some ideas are proposed to establish the infrastructure of all-electric houses which are able to reduce primary energy consumption and $CO_2$ emission by adopting heat pump systems and induction heating cookers excluding the use of fossil fuel energy. This electrification concept is based on the consumption of only one type of energy which means electricity as secondary energy and the conventional fossil fuel energy is just consumed to generate electricity as primary energy. All-electric house is laid on the extension of the hydrogen economy in a long-term viewpoint so that the effectiveness of this new conceptual house is estimated analyzing the reduction of $CO_2$ emission. In this analysis, the balance of electricity supply and demand is considered including the construction of new power plants by renewable energy such as nuclear, IGCC and fuel cell because decarbonization is an essential element of hydrogen technology and economy and this action is accomplished in both supply and demand side of electricity. The results are able to contribute to develop various useful hydrogen policies and strategies and some detail researches are required previously to make the best application of this new conceptual house.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.89-97
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• 2012

In recent years, efficient energy management technologies are required, as environmental problems have emerged worldwide. In response to this, smart home services focused on efficient energy management technology seems to be emerging. And the integration of technology of user-oriented real-time energy monitoring and control systems is required. In this paper, we present a location-based green home service using smart phones for efficient energy management in a house. We design a green home network system to apply the green home service, and implement an integrated gateway system which connects and controls each appliance in a house. We develop appliance control services and indoor location services on smart phones, and determine whether user's occupancy of each room by measuring the location according to the variation of signal strength. In order to evaluate the performance of the energy savings, we have set up the scenarios of energy usage pattern and have compared the energy variation resulting from the application of the indoor location services with smart meters. A comparison of energy usage demonstrated that the energy saving of a house with the proposed location-based green home service was down up to 30%.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.161-167
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• 2015

This paper presented an occupancy-based energy saving system for appliance energy saving in smart house. The developed system is composed of a sensing system and a home gateway system. The sensing system is set of wireless sensor nodes which have pyroelectric infrared (PIR) sensor to detect a motion of human and set of smart plugs which measure the current using CT (current transformer) sensor and send the current to home gateway wirelessly. We measured current consumption of appliances in real time using smart plugs, and checked the occupation of residents using occupancy sensors installed on the door and room. The proposed system saves electric energy to switch off the supply power of unnecessary usages in the unoccupied spaces. Experiments conducted have shown that electric energy usage of appliances can be saved about 34% checked by using occupation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.860-863
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• 2010

Wireless sensor networks consist of small, autonomous devices with wireless networking capabilities. In order to further increase the applicability in real world applications, minimizing energy consumption is one of the most critical issues. Therefore, accurate energy model is required for the evaluation of wireless sensor networks. In this paper, we analyze the energy consumption for wireless sensor networks. To estimate the lifetime of sensor node, we have measured the energy characteristics of sensor node based on Telosb platforms running TinyOS. Based on the proposed model, the estimated lifetime of a battery powered sensor node can use about 6.925 months for 10 times motion detection per hour.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.6
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• pp.1151-1158
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• 2017

The Effluent Heat Pipe integral with the heater is a device that recreates unused thermal energy from the plant in winter, and thus reuses unused energy before releasing the exhaust heat. Through the establishment of facility horticulture and glass greenhouses, we identified the problems of our agricultural heaters, and we proposed efficient agricultural efficiency and smart control systems for optimum agricultural efficiency and smart house.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.27-39
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• 2022

The smart farm is recognized as a solution for future farmers having positive effects on the sustainability of the poultry industry. Intelligent microclimate control can be a key technology for broiler production which is extremely vulnerable to abnormal indoor air temperatures. Furthermore, better control of indoor microclimate can be achieved by accurate prediction of indoor air temperature. This study developed predictive models for internal air temperature in a mechanically-ventilated broiler house based on the data measured during three rearing periods, which were different in seasonal climate and ventilation operation. Three machine learning models and a mechanistic model based on thermal energy balance were used for the prediction. The results indicated that the all models gave good predictions for 1-minute future air temperature showing the coefficient of determination greater than 0.99 and the root-mean-square-error smaller than 0.306℃. However, for 1-hour future air temperature, only the mechanistic model showed good accuracy with the coefficient of determination of 0.934 and the root-mean-square-error of 0.841℃. Since the mechanistic model was based on the mathematical descriptions of the heat transfer processes that occurred in the broiler house, it showed better prediction performances compared to the black-box machine learning models. Therefore, it was proven to be useful for intelligent microclimate control which would be developed in future studies.

• Journal of Climate Change Research
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• v.7 no.3
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• pp.217-229
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• 2016

Increased evidences reveal that the global climate change adversely affect on the environment. Smart grid system is one of the ways to reduce greenhouse gas emissions in the electricity generation sector. Since 2013, Korea Electric Power Corporation (KEPCO) has installed smart grid station in KEPCO office buildings. The goal of this paper is two folds. One is to quantify the reduction in greenhouse gas emissions through smart grid stations installed in KEPCO office buildings as a part of pilot project. Among components of smart grid stations, this research focused on the photovoltaic power system (PV) and energy storage system (ESS). The other is to estimate the reduction in greenhouse gas emissions when PV is applied on individual houses. Results show that greenhouse gas emissions reduce 5.8~11.3% of the emissions generated through the electricity usage after PV is applied in KEPCO office buildings. The greenhouse gas emissions reduction from ESS is not apparent. When PV of 200~500 W is installed in individual houses, annual greenhouse gas emission reduction in 2016 is expected to be approximately $2.2{\sim}5.4million\;tCO_2-eq$, equivalent to 6~15% of greenhouse gas emissions through the electricity usage in the house hold sector. The saving of annual electricity cost in the individual house through PV of 200 W and 500 W is expected to be 47~179 thous and KRW and 123~451 thousand KRW, respectively. Results analyzed in this study show the environmental effect of the smart grid station. In addition, the results can be further used as guidance in implementing similar projects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1123-1146
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• 2023

In South Korea, there have been many studies on efficient building-energy management using renewable energy facilities in single zero-energy houses or buildings. However, such management was limited due to spatial and economic problems. To realize a smart zero-energy city, studying efficient energy integration for the entire city, not just for a single house or building, is necessary. Therefore, this study was conducted in the eco-friendly energy town of Chungbuk Innovation City. Chungbuk successfully realized energy independence by converging new and renewable energy facilities for the first time in South Korea. This study analyzes energy data collected from public buildings in that town every minute for a year. We propose a smart city building-energy management model based on the results that combine various renewable energy sources with grid power. Supervised learning can determine when it is best to sell surplus electricity, or unsupervised learning can be used if there is a particular pattern or rule for energy use. However, it is more appropriate to use reinforcement learning to maximize rewards in an environment with numerous variables that change every moment. Therefore, we propose a power distribution algorithm based on reinforcement learning that considers the sales of Energy Storage System power from surplus renewable energy. Finally, we confirm through economic analysis that a 10% saving is possible from this efficiency.