• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.250-252
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• 2019

국내의 전력 산업은 부정확한 전력수요 예측으로 전력부족과 공급과잉의 주기적 반복이 발생하여 전력 과생산, 에너지 낭비, 전력 과소비와 요금 체납 등의 문제가 발생하고 있다. 이를 해결하기 위해 본 논문에서는 LSTM 알고리즘을 사용하여 전력사용량 예측하고, 정량의 전력을 선구입 할 수 있도록 설계하였다. 제안하는 시스템은 스마트그리드 환경과 인공지능으로 정량의 전기를 구입 할 수 없는 기존의 전력 산업 문제를 보완하여 소비자의 전기요금 절감과 에너지 절약이 가능하다.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.185-185
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• 2016

일반적인 송 배수시스템의 운영은 지대가 높은 곳에 위치한 배수지(tank)에 용수를 저장한 후, 자연유하에 의해 수요절점으로 용수를 공급한다. 이때 배수지에 용수를 송수하기 위한 펌프장 운영에서 많은 전기에너지가 소모된다. 일반적으로 송수펌프의 운영은 다년간의 운영자료를 기반으로 운영자의 판단에 의해 이루어지거나, SCADA(Supervisory Control and Data Acquisition)시스템을 통해 관측되는 배수지 수위를 기준으로 펌프 작동여부를 결정하고 있다. 본 연구에서는 이러한 기존 펌프운영방법을 개선하고 좀 더 효율적인 운영방법을 모색하기 위해 실시간 송수펌프 최적운영 모형을 개발하였다. 최적화 기법으로는 유전자 알고리즘(genetic algorithm)을 사용하였으며, 다양한 제약조건(operational constraints)을 적용하고 급수지역의 24시간 용수사용량을 미리 예측하여 실제 시스템의 운영형태와 근접하게 반영하였다. 또한 최적화 과정에서 상수관망해석 프로그램(EPANET)을 연계하여 수요절점의 수압조건 및 시스템의 운영상황을 모의하였다. 개발된 모형을 국내 P시의 광역상수도 시스템에 실제 적용하였으며, 현장 실시간 운영 데이터를 입수하여 전력사용량, 배수지수위, 이산화탄소 발생량 등을 비교, 분석하였다. 개발 모형을 이용하여 펌프운영을 실시하였을 경우, 기존의 운영방식과 비교하여 경제적/환경적으로 뚜렷한 개선 효과를 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.629-631
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• 2015

Recently, the actual demand for electricity usage to out of demand forecasting demand appears to be based on the power of Government to address the insecurity is there are a lot of efforts on a more efficient energy management. In 2011, the first major outage, blackout since the current rate of no more than 10% of our power plants, such as power supply and demand crisis is being repeated. In addition, energy management systems, the demand for care and social areas are being expanded. In this paper, Building power supply and wired/wireless router and to optimize the DDNS (Dynamic Domain Name Service) for remote control and monitoring device for electric consumption Presonal Energy Management System offers a way to implement it. In the future, remote control and access the user's can minimize the settings for additional research is needed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.3
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• pp.59-64
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• 2020

In the Paris Climate Agreement, Korea submitted an ambitious goal of reducing the greenhouse gas emission forecast (BAU) by 37% by 2030. And as one of the countermeasures, a smart grid, an intelligent power grid, was presented. In order to apply the smart grid, EMS(Energy Management System) needs to be installed and operated in various fields, and the supply is delayed due to the lack of awareness of users and the limitations of system ROI. Therefore, recently, various data analysis and control technologies have been proposed to increase the efficiency of the installed EMS. In this study, we present a measurement control algorithm that analyzes and predicts big data collected by IoT using a SARIMA model to check and operate energy consumption of public sports facilities.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.