• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.11-18
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• 2020

Since the end devices of the Internet of Things (IoT) are battery operated products, careful consideration for ultra-low power (ULP) is required. The Micro Controller Unit (MCU) industry has developed very effective functions to save energy, but developers have difficulty in selecting the MCU because various operating modes are applied to reduce energy consumption by manufacturers. Therefore, this paper introduces ULPMark benchmark, a standardized benchmark method that can compare MCUs of various vendors and feature sets, and provides hardware functions for ultra-low-power operation of the two platforms that received the high evaluation scores from ULPMark. In addition, we investigated and analyzed how developers can utilize the functions for ultra low power consumption through driver APIs and detailed register control.

• ETRI Journal
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• v.39 no.2
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• pp.275-283
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• 2017

A green technology for reducing energy consumption has become a critical factor in ICT industries. However, for the telecommunications sector in particular, most network elements are not usually optimized for power efficiency. Here, we propose a novel energy-efficient packet switching method for use in an IP network for reducing unnecessary energy consumption. As a green networking approach, we first classify the network nodes into either header or member nodes. The member nodes then put the routing-related module at layer 3 to sleep under the assumption that the layer in the OSI model can operate independently. The entire set of network nodes is then partitioned into clusters consisting of one header node and multiple member nodes. Then, only the header node in a cluster conducts IP routing and its member nodes conduct packet switching using a specially designed identifier, a tag. To investigate the impact of the proposed scheme, we conducted a number of simulations using well-known real network topologies and achieved a more energy- efficient performance than that achieved in previous studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.623-625
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• 2015

Recently, the cooling water of power plant was included in RPS(Renewable Portpolio Standard) as water heat energy. Thus the trend of cooling water application is growing bigger for agriculture and fishing industry. Especially as energy consumption cost in agriculture and fishing industry is a vital element, the interests for energy efficiency is growing bigger. The advanced agriculture country like Netherlands is distributing diagnosis software to the farmers based on ICT diagnostic system for the efficient energy consumption and energy demand amounts depend on crops of cultivation in automated horticulture. Hereafter, in the preparation of the expansion of the automated agriculture domestically by the cooling water of power plant, we would like to propose the energy application case(Greenery) in the advanced countries abroad in agriculture and EMS system about the application of the cooling water in power plant.

• Journal of Power System Engineering
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• v.21 no.5
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• pp.5-12
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• 2017

With the difficulties in increasing the efficiency of conventional crank-driven compressors due to mechanical loss, compressor manufacturers have investigated new kinds of compressor such as a free piston compressor mechanism. This study investigates the energy efficiency of two different types of compressor for a household refrigerator. One is the conventional crank-driven compressor, and the other one is a linear compressor. The energy efficiencies of these compressors are evaluated. Experimental results show that the linear compressor has 10% lower power consumption than the brushless direct-current (BLDC) reciprocating compressor. The linear compressor demonstrates excellent energy efficiency by reducing the friction loss. Furthermore, a motor efficiency exceeding 90% is achieved by using a linear oscillating mechanism with a moving magnet. Additionally, the compressor stroke to piston diameter ratio of the oscillating piston in the linear compressor can be adjusted in order to modulate the cooling capacity of the compressor for improved system efficiency.

• Journal of Korean Society of Water and Wastewater
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• v.24 no.6
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• pp.683-690
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• 2010

Renewable and unutilized energy (biogas power generation, wind power, solar, small hydro-power, sewage heat source, etc.) seems to be suitable to install for the sewage treatment facilities. There are 357 sewage treatment plants in 2007. 17 plants among these have been operating for self-supporting energy by using solar power, small hydro-power and biogas in 2008. Newly built sewage treatment plant of 96,000 $m^3$/day for a newtown is expected to get up to energy consumption of 10 GWh/yr. If solar energy, small hydro-power and biogas-equipments were applied to the new treatment plant, self-supporting energy of the new sewage treatment plant will get up to 56.1%. As a results, about 2,379ton $CO_2$/yr $CO_2$ emission reduction can be expected by using renewable energy. These efforts for self-supporting energy will lead sewage treatment plant to new energy recycle center.

• The Journal of Bigdata
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• v.6 no.1
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• pp.127-132
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• 2021

Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.

• Journal of Communications and Networks
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• v.18 no.3
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• pp.327-332
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• 2016

The routing protocol for low-power and lossy networks (RPL) is an internet protocol based routing protocol developed and standardized by IETF in 2012 to support a wide range of applications for low-power and lossy-networks (LLNs). In LLNs consisting of resource-constrained devices, the energy consumption of battery powered sensing devices during network operations can greatly impact network lifetime. In the case of inefficient route selection, the energy depletion from even a few nodes in the network can damage network integrity and reliability by creating holes in the network. In this paper, a composite energy-aware node metric ($RER_{BDI}$) is proposed for RPL; this metric uses both the residual energy ratio (RER) of the nodes and their battery discharge index. This composite metric helps avoid overburdening power depleted network nodes during packet routing from the source towards the destination oriented directed acyclic graph root node. Additionally, an objective function is defined for RPL, which combines the node metric $RER_{BDI}$ and the expected transmission count (ETX) link quality metric; this helps to improve the overall network packet delivery ratio. The COOJA simulator is used to evaluate the performance of the proposed scheme. The simulations show encouraging results for the proposed scheme in terms of network lifetime, packet delivery ratio and energy consumption, when compared to the most popular schemes for RPL like ETX, hop-count and RER.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.6
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• pp.113-119
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• 2014

For the robust design of Motor Driven Power Steering (MDPS) systems, it is important to consider energy efficiency from every aspect such as system configuration and current flow, etc. If design optimization is not considered, it has many problems on a vehicle. For example, when evaluating steering test, particularly the Catch-up test which turning the steering wheel left or right quickly, steering effort should be increased rapidly. Also a vehicle might have poor fuel efficiency. In this study, it is calculated energy consumption for each component of the steering system and analyzed factors of energy consumption. As a result, this paper redefines a method to estimate steering input torque using characteristics of vehicle electric components and then conducts an analysis of contribution for the Catch-up.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.93-99
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• 2019

In this paper, we propose a smart meter that uses GRU model, which is one of artificial neural networks, for the efficient energy management. We collected power consumption data that train GRU model through the proposed smart meter. The implemented smart meter has automatic power measurement and real-time observation function and load control function through power consumption prediction. We determined a reference value to control the load by using Root Mean Squared Error (RMS), which is one of performance evaluation indexes, with 20% margin. We confirmed that the smart meter with automatic load control increases the efficiency of energy management.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.64-72
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• 2016

In this paper, we utilize a Gaussian process to predict the power consumption in the air-conditioning system. As the power consumption in the air-conditioning system takes a form of a time-series and the prediction of the power consumption becomes very important from the perspective of the efficient energy management, it is worth to investigate the time-series model for the prediction of the power consumption. To this end, we apply the Gaussian process to predict the power consumption, in which the Gaussian process provides a prior probability to every possible function and higher probabilities are given to functions that are more likely consistent with the empirical data. We also discuss how to estimate the hyper-parameters, which are parameters in the covariance function of the Gaussian process model. We estimated the hyper-parameters with two different methods (marginal likelihood and leave-one-out cross validation) and obtained a model that pertinently describes the data and the results are more or less independent of the estimation method of hyper-parameters. We validated the prediction results by the error analysis of the mean relative error and the mean absolute error. The mean relative error analysis showed that about 3.4% of the predicted value came from the error, and the mean absolute error analysis confirmed that the error in within the standard deviation of the predicted value. We also adopt the non-parametric Wilcoxon's sign-rank test to assess the fitness of the proposed model and found that the null hypothesis of uniformity was accepted under the significance level of 5%. These results can be applied to a more elaborate control of the power consumption in the air-conditioning system.