• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.1
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• pp.82-87
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• 2005

The purpose of this study is to suggest the characteristics and actual state of energy consumption by the analysis of energy consumption data in an office building. This study examines and analyzes daily and monthly energy consumption of an office building located in Seoul, Korea regarding type of load and business classification within a building. The results are as follows. 1) Energy consumption of office building for each type of load show similar consumption patterns, regardless of seasons such as cooling period and heating period. 2) Out of all annual energy consumption, consumption for lighting took about $43\;\%,$ general electric Power about $23\;\%,$ emergency power $25\;\%,$ computer center $5\;\%$ and cooling power $4\;\%,$ showing that the consumption for lighting was highest, and the percentage of energy consumption for cooling power for operation of cooling facilities took the lowest percentage. 3) Annual gas consumption used for heating and hot water supply were $38,\;36\;\%$ for officetel and office respectively, and $26\;\%$ for arcade. 4) Electricity consumptions used for cooling power for each use of building, office and officetel recorded in July and August of cooling seasons. Even though it shows different patterns for each month, energy consumption showed unique pattern throughout the cooling seasons.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.193-200
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• 2016

Research and development of energy self-consumption introducing photovoltaic and energy storage system at home is very active. This system can manage the home energy in which it charges the electricity generated during the day and uses it during high electricity bills. However, it not yet made up the residential real-time pricing in Korea but it can reduce electricity usage to a certain target on the progressive. In order to introduce the home photovoltaic, it requires PCS(Power Conditioning System). This converts the direct current into alternating current by the electricity generated and used to perform charging and discharging of the energy storage system. The market for self-consumption smart home system is currently increasing because the interests of the general public about solar power, energy storage systems increased. The result of this study is installed on the room environment and the effect was analyzed on the assumption of real-time pricing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.123-129
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• 2014

This paper suggests that it provides a electricity management system for wasting electricity, from power demand growth environments. This Energy management system based on ICT(Information & Communication Technology) can control Smart Power Outlet connecting to this system with Web Browser and Android phone, anytime, anywhere. Through analysis of acquisition data from them, this proposed system can monitor and control power consumption efficiently. This system was organized mesh network of Smart Power Outlet, gateway by wireless Zigbee, and ESS(Energy Saving System) by TCP/IP beyond existing limit of communication distance and space.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.4
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• pp.63-71
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• 2014

As power consumption increases, more power utilities are required to satisfy the demand and consequently results in tremendous cost to build the utilities. Another issue in construction of power utilities to meet the peak demand is an inefficiency caused by surplus power during non-peak time. Therefore, most power company considers power demand management with time-based electricity rate policy which applies different rate over time. This paper considers an optimal machine operation problem under the time-based electricity rates. In TOC (Theory of Constraints), the production capacities of all machines are limited to one of the bottleneck machine to minimize the WIP (work in process). In the situation, other machines except the bottleneck are able to stop their operations without any throughput loss of the whole manufacturing line for saving power utility cost. To consider this problem three integer programming models are introduced. The three models include (1) line shutdown, (2) block shutdown, and (3) individual machine shutdown. We demonstrate the effectiveness of the proposed IP models through diverse experiments, by comparing with a TOC-based machine operation planning considered as a current model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.253-255
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• 2003

Electricity is the prime commodity in mobile device, e.g. smart phone, PDA, MP3 player and etc. This strict restriction on power consumption requirement of the mobile device puts unique demand in designing hardware and software components of the device. In this paper, we address the issue of minimizing the power consumption in retrieving the continuous media data from the disk drive for real-time playback purpose. Different from the legacy text based data, real-time multimedia playback requires that the storage supplies the data block continuous fashion. This may put immense burden on the power scarce environment since the disk Is required to be active for the entire playback duration. We develop elaborate algorithm which carefully analyzes the power consumption profile of the disk drive and which establishes the data retrieval schedule for the given playback. It computes the amount of data blocks to read, the length of active and standby period. According to our simulation result, the ARM algorithm exhibits superior performance in continuous media retrieval from the aspect of power consumption to legacy playback scheme.

• Journal of Electrical Engineering and Technology
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• v.2 no.1
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• pp.42-49
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• 2007

For electricity markets to function in a truly competitive and efficient manner, it is not enough to focus solely on improving the efficiencies of power supply. To recognize price-responsive load as a reliability resource, the customer must be provided with price signals and an instrument to respond to these signals, preferably automatically. This paper attempts to develop the Windows-based load management system in competitive electricity markets, allowing the user to monitor the current energy consumption or billing information, to analyze the historical data, and to implement the consumption strategy for cost savings with nine possible scenarios adopted. Finally, this modeling framework will serve as a template containing the basic concepts that any load management system should address.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.5
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• pp.207-217
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• 2018

Recently, the importance of energy demand management, particularly peak load control, has been increasing due to the policy changes of the Second Energy Basic Plan. Even though the installation of distributed generation systems such as Photovoltaic and energy storage systems (ESS) are encouraged, high initial installation costs make it difficult to expand their supply. In this study, the power consumption of a university building was measured in real time and the measured power consumption data was used to calculate the optimal installation capacity of the Photovoltaic and ESS, respectively. In order to calculate the optimal capacity, it is necessary to analyze the operation methods of the Photovoltaic and ESS while considering the KEPCO electricity billing system, power consumption patterns of the building, installation costs of the Photovoltaic and ESS, estimated savings on electric charges, and life time. In this study, the power consumption of the university building with a daily power consumption of approximately 200kWh and a peak power of approximately 20kW was measured per minute. An economic analysis conducted using these measured data showed that the optimal capacity was approximately 30kW for Photovoltaic and approximately 7kWh for ESS.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.