• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.491-497
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• 2015

In this paper a method of design and Implementation of RFID based control system for reducing standby power consumption at the power outlet is described. The system is composed of a RF controlled power outlet having relay and an active RFID tag communicating with the RF reader module controlling the relay. When the tag carried by human approaches to the RF reader the reader recognizes the tag and switch off the relay based on the RSSI level measurement. A low power packet prediction algorithm has been used to decrease the DC power consumption at both the tag and the RF reader. The result of experiment shows that successful operation of the relay control has been obtained while low power operation of the tag and the reader is achieved using above algorithm. Also setting the distance between the reader and the tag by controlling transmission power of the tag and adjusting the duty cycle of the packet waiting time when the reader is in idle state allows us to reduce DC power consumption at both the reader and the tag.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Proceedings of the KIEE Conference
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• summer
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• pp.3-6
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• 2004

전력시장에서 예비력, 송전망 설비 유한성, 열병합발전소 열공급, 수요예측 오차, LNG, 국내무연탄의 우선소비 정책 둥에 의해 경제급전 원칙을 완벽하게 지킬 수 없다. 이로 인하여 고연료비 발전기가 제약에 의해 운전될 수밖에 없고 이 제약발전에 의해 시장가격이 상승하고 소비자 입장에서 보면 전력구입비용이 증가하게 된다. 이 논문에서는 2002, 2003년도 국내전력계통에서 발생한 계통제약 발생현황을 지역별, 월별로 분석하고 2002년 대비 2003계통제약 증가원인을 분석하였다.

• Journal of IKEEE
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• v.22 no.3
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• pp.829-835
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• 2018

Recently, studies have been actively conducted to reduce spare power that is unnecessarily generated or wasted in existing power systems and to improve energy use efficiency. In this study, smart meter, which is one of the element technologies of smart grid, is implemented to improve the efficiency of energy use by controlling power of electric devices, and predicting trends of energy usage based on deep learning. We propose and develop an algorithm that controls the power of the electric devices by comparing the predicted power consumption with the real-time power consumption. To verify the performance of the proposed smart meter based on the deep running, we constructed the actual power consumption environment and obtained the power usage data in real time, and predicted the power consumption based on the deep learning model. We confirmed that the unnecessary power consumption can be reduced and the energy use efficiency increases through the proposed deep learning-based smart meter.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.155-160
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• 2019

In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.

• Proceedings of the KIEE Conference
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• 2008.11a
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• pp.435-438
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• 2008

우리나라 최대전력은 70년대 연도별로 36만 kW, 약 15%씩 증가하였으나, 최근 2000년대에는 연도별로 300만kW 이상, 약 6%대의 증가를 보이고 있다. 발생시간도 70년대에는 저녁시간대에 주로 발생했으나 80년대부터 최근까지는 15시에 하계 최대전력이 발생하고 있다 아울러 최근에는 기상의 변동폭 증가로 여름과 겨울의 계절성이 증폭되는 추세에 있고 이러한 최대전력 발생의 이면에는 시간별 부하패턴이 다양하게 나타나고 있다. 과거 70-80년대에는 연간이나 월간 부하패턴 모두 평균전력대비 변동폭이 크게 나타났으나 최근에는 변동폭이 상당히 작아지고 있다. 이는 최대전력에 못지않게 전력소비량이 지속적으로 증가하여 부하수준이 평준화되고, 부하율이 높아지고 있다는 것을 나타내며 연중 및 일간 피크 발생시점도 다변화되는 특징을 보이고 있다. 따라서 이러한 부하패턴 변화에 합리적으로 대응하기 위해서는 짧은 기간의 부하관리보다는 상시 수요관리인 효율향상 위주의 프로그램이 필요하고, 저렴한 전기 요금의 정상화를 통한 전력소비 감축을 통한 대응이 중요하다. 외국의 사례를 보면 우리나라 냉방 및 난방전력은 현재보다 10%p-20%p 정도 점유비가 추가적으로 상승할 개연성이 높으므로 다양한 시나리오 예측을 통한 철저한 위험관리 체계 확립이 요구된다.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.11
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• pp.369-382
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• 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 Environmental Policy
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• v.12 no.3
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• pp.97-122
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• 2013

The ARDL(Autoregressive Distributed Lag) method is employed analyzes the long-run equilibrium relationships among environmental pollution($CO_2$ emissions) per capita, income levels per capita, and energy consumption per capita. The error correction model is employed to analyze the short-term effects of income and energy consumption on $CO_2$ emissions. The Toda-Yammamoto method is employed for causal analysis among the three variables. The results show that income levels, energy consumption, and $CO_2$ emissions are cointegrated. We found the N type relationship between income and $CO_2$ emissions. Long-term elasticities of income and energy consumption with respect to $CO_2$ emission were greater than their short-term elasticities. There were a bilateral causality between energy consumption and $CO_2$ emissions. There was a unilateral causality from $CO_2$ emissions to income and from energy consumption to income not vice versa. Energy consumption can be an important variable to contribute to forecasting $CO_2$ emissions.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.236-237
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• 2010