• 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.

• Annual Conference of KIPS
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• 2016.04a
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• pp.766-769
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• 2016

산업시설 가운데 화력발전소에 스마트 부하관제 시스템을 이용하여 메탈 조명을 LED 조명으로 대체하여 전력을 절감하였다. LED 조명의 빛의 세기를 조절 할 수 있는 디밍을 통해서 사용자의 다양한 요구사항을 반영할 수 있다. 수요 예측은 전력 사용량이 많은 시간대의 전력사용량을 분산시키는 시스템이다. 수요 예측 스케줄에 의해서 목표전력량에 도달하기 위해 설정된 시간대에 디밍으로 LED 조명의 전력량을 조절하여, 기존 LED 조명 대비 전력량을 27.8% 절감하였다. 외부에 눈과 비가 오거나 밤에 실내가 어두워져서 작업 환경에 영향을 미치는 경우가 있다. 실시간으로 조도를 측정하여 작업 환경이 원활한 기준조도에 맞도록 LED 조명을 디밍 하여 작업환경을 개선하였다.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• Proceedings of the KIEE Conference
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• 2008.09a
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• pp.83-86
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• 2008

국내 한국진력에서 관리 운영하고 있는 전자식 전력량계는 약 55만 여대이며, 이중 약 15만 여대가 고안용 전자식 전력량계로 2000년대 초반부터 원격검침을 통해 전력사용량을 측정하고 고객 측에 대해서는 역률과 누적전력량 등의 기타 부가서비스를 제공하고 있다. 전력량계와 원격통신을 이용한 전력부가서비스는 전력회사의 입장에서는 고객의 부하사용에 대한 다양한 정보를 효율적으로 수집하여 피크부하 제어 등의 수요관리를 효과적으로 시행할 수 있고 고객의 입장에서는 과거는 물론 미래의 사용량을 예측하고 기타 전력사용패턴 등을 관찰함으로서 고객의 능동적인 참여로 전력사용량을 절약하는 효과를 가져 올 수 있다. 현 시점에서의 국내 전력부가서비스의 활용수준은 초기단계로 매우 기본적인 정보에 한해서 활용이 되고 있다. 이에 본 논문에서는 기존의 국내외 관련 연구사례와 적용사례, 그리고 시행현황의 조사 분석을 통해 향후의 국내 전력부가서비스 적용전망과 발전방향을 모색해 보고자 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.253-255
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• 2016

본 논문에서는 H.264의 화면 내 예측 모드를 적용한 변형된 선택 보간법을 채택함으로써 고화질 DMB 시청시 사용자의 모바일 데이터 사용량과 전력사용량을 줄이는 것을 목적으로 하는 시스템을 제안한다. 스마트 DMB는 T-DMB망으로 전송되는 QVGA급 영상(기본영상)에 모바일 데이터망을 통해 전송되는 추가정보를 더하여 화질을 개선하는 방식이다. 제안 방법은 원본 고화질 영상과 T-DMB망으로 전송되는 기본영상의 오차를 효과적으로 줄이면 사용자 시스템의 부하와 데이터 사용량을 줄이면서 화질을 유지할 수 있다.

• Annual Conference of KIPS
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• 2019.10a
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• pp.817-819
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• 2019

다양한 학습 모델이 발전하고 있는 지금, 학습을 통한 다양한 시도가 진행되고 있다. 이중 에너지 분야에서 많은 연구가 진행 중에 있으며, 대표적으로 BEMS(Building energy Management System)를 볼 수 있다. BEMS의 경우 건물을 기준으로 건물에서 생성되는 다양한 DATA를 이용하여, 에너지 예측 및 제어하는 다양한 기술이 발전해가고 있다. 하지만 FEMS(Factory Energy Management System)에 관련된 연구는 많이 발전하지 못했으며, 이는 BEMS와 FEAMS의 차이에서 비롯된다. 본 연구에서는 실제 공장에서 수집한 DATA를 기반으로 하여, 전력량 예측을 하였으며 예측을 위한 기술로 시계열 DATA 분석 방법인 LSTM 알고리즘을 이용하여 진행하였다.

• Journal of Advanced Technology Convergence
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• v.2 no.4
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• pp.43-48
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• 2023

Globally, there is a collaborative effort to achieve global carbon neutrality in response to climate change. In the case of South Korea, greenhouse gas emissions are rapidly increasing, presenting an urgent situation that requires resolution. In this context, this study developed a thermal energy collection device named a 'steam trap' and created an AI model capable of predicting future electricity usage by collecting energy usage data through steam traps. The average accuracy of electricity usage prediction with this AI model was 96.7%, demonstrating high precision. Consequently, the AI model enables the prediction and management of days with high electricity consumption and identifies which facilities contribute to elevated power usage. Future research aims to optimize energy consumption efficiency through efficient equipment operation using anomaly detection in steam traps and standardizing energy management systems, with the ultimate goal of reducing greenhouse gas emissions.

• 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 the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8576-8584
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• 2015

Load forecasting is needed to make supply and demand plan for a stable supply of electricity. It is also necessary for optimal operational plan of the power system planning. In particular, in order to ensure stable power supply, long-term load forecasting is important. And regional load forecasting is important for tightening supply stability. Regional load forecasting is known to be an essential process for the optimal state composition and maintenance of the electric power system network including transmission lines and substations to meet the load required for the area. Therefore, in this paper we propose a forecasting method using SARIMA during the 12 months (long-term/mid-term) load forecasting by 16 regions of the South Korea.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.