• Smart Media Journal
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• v.13 no.1
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• pp.18-23
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• 2024

Power efficiency management in smart farms is important due to its link to climate change. As climate change negatively impacts agriculture, future agriculture is expected to utilize smart farms to minimize climate impacts, but smart farms' power consumption may exacerbate the climate crisis due to the current electricity production system. Therefore, it is essential to efficiently manage and optimize the power usage of smart farms. In this study, we propose a system that monitors the power usage of smart farm equipment in real time and predicts the power usage one hour later using GRU. CT sensors are installed to collect power usage data, which are analyzed to detect and prevent abnormal patterns, and combined with IoT technology to efficiently manage and monitor the overall power usage. This helps to optimize power usage, improve energy efficiency, and reduce carbon emissions. The system is expected to improve not only the energy management of smart farms, but also the overall efficiency of energy use.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.57-66
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• 2023

Currently, a new energy system is emerging that implements consumption reduction by improving energy efficiency. Accordingly, as smart grids spread, the rate system by timing is expanding. The rate system by timing is a rate system that applies different rates by season/hour to pay according to usage. In this study, external factors such as temperature/day/time/season are considered and the time series prediction model, LSTM, is used to predict energy power usage data. Based on this energy usage prediction model, energy usage charges are reduced by analyzing usage patterns for each device and transferring power energy from the maximum load time to the light load time. In order to analyze the usage pattern for each device, a clustering technique is used to learn and classify the usage pattern of the device by time. In summary, this study predicts usage and usage fees based on the user's power data usage, analyzes usage patterns by device, and provides customized demand transfer services based on analysis, resulting in cost reduction for users.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.52-59
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• 2020

Currently, the domestic electricity rates for houses are charged by applying a progressive level according to monthly electricity usage. Electricity rates rise sharply wWhen the amount of electricity used is large, electricity rates rise sharply. The standardized electricity rate progressive system has limitations in that it lacks consideration of the consumers' power usage patterns and limits consumers' their options. Accordingly, the Ministry of Trade, Industry and Energy and the Korea Electric Power Corporation have been demonstrating the basis of a rate system for housing, which is a method of charging electricity according to the amount of electricity used by season and time. In this paper, 10 electricity usage patterns were derived through from AMI data analysis for 5 five years of 362 apartment complexes located in metropolitan cities. The patterns were, and then applied to the existing domestic electricity rate and time-by-time rates applied to demonstrations, and by time-by-time rates in the US and Australia. The effect of the optional rate by pattern was compared and analyzed. As a result, it was confirmed that benefits occurred in five5 patterns compared to existing rate plans, and the electricity rates increased in 5 five patterns, and t. This phenomenon shows the same phenomenon withis the same as the overseas rates, including domestic rates being demonstrated.

• 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 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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.10
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• pp.69-74
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• 2014

With the increasing interest of energy efficiency, several buildings and factories begin to monitor energy usages with a built-in energy management system. However, the built-in energy monitoring system does not reflect the dynamics of buildings and factories energy usage. To overcome the latter, we deploy several sensors to monitor the dynamics of buildings energy usage patterns. In this paper, we are proposing a framework of a sensor based energy monitoring system. Based on our limited experiments, we can monitor power usages by a person, device and time period. As a result, we can plan a better energy usage and improve energy efficiency by the monitored energy usage profile data.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.9-15
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• 2010

Recently, Smart Grid is a emerging topic in power and communication industry. 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. In order to successful implementation of Smart Grid, energy management function will be the key factor that coordinates and optimally controls the various loads according to the operating condition and environments, and the load patterns in residential and commercial building will be required as fundamental element for load management. In this study, we collects many types of energy usage data of electric appliances, analyze their load curves, and make the general load patterns for electrical appliance.

• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.429-453
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• 2016

This study analyzes circulation patterns of books with checkout transaction count by 11 subject areas, 5 positions, and 5 divisions with a Use Factor developed by Bonn in an Academic Library. 20% of the loan books occupies more than half of circulation and these are regarded as core collection. It proposes a 'Loan books 20/50 rule' that 20% core collection accounts for 50% of its circulation. It analyzes the proportion of core collection from the aspect of each subject area with a use factor, monthly change trend and loan period. It also defines 'book usage' considering checkout frequency of each title and loan period. Circulation patterns of core collection are compared and analyzed in terms of both checkout frequency and book usage. Core collection occupies about more than half of both total checkout transactions and total book usages and they all show a Power Law distribution.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1001-1009
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• 2015

In Smart Grid, smart meter sends our electric signal data to the main server of power supply in real-time. However, the more efficient the management of power loads become, the more likely the user's pattern of usage leaks. This paper points out the threat of privacy and the need of security measures in smart device environment by showing that it's possible to identify the appliances and the specific usage patterns of users from the smart meter's data. Learning algorithm PCA is used to reduce the dimension of the feature space and k-NN Classifier to infer appliances and states of them. Accuracy is validated with 10-fold Cross Validation.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.3-8
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• 2010

Recently, There have been many attempts to optimize energy usage in buildings and houses using Information Technology(IT) and the typical implementation can be found in Intelligent Building and Zero Energy Building. These kinds of buildings need to forecast the building loads, estimate the capacity requirement for power supply, and decide the capacity of the main transformer of the building. Currently, the capacity of the main transformer has been decided just using typical load estimation method not considering the load characteristics and patterns. In this paper, we propose a new load estimation method considering the load characteristics and patterns of the builiding. The proposed method was applied to actual mail center and verified the feasibility of application to actual design of buildings.