• 제목/요약/키워드: power prediction

검색결과 2,151건 처리시간 0.028초

퍼지 예측 시스템을 이용한 전력 부하 예측 (Electric Power Load Forecasting using Fuzzy Prediction System)

  • 방영근;심재선
    • 전기학회논문지
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    • 제62권11호
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    • pp.1590-1597
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    • 2013
  • Electric power is an important part in economic development. Moreover, an accurate load forecast can make a financing planning, power supply strategy and market research planned effectively. This paper used the fuzzy logic system to predict the regional electric power load. To design the fuzzy prediction system, the correlation-based clustering algorithm and TSK fuzzy model were used. Also, to improve the prediction system's capability, the moving average technique and relative increasing rate were used in the preprocessing procedure. Finally, using four regional electric power load in Taiwan, this paper verified the performance of the proposed system and demonstrated its effectiveness and usefulness.

인공신경망 기반의 풍력발전기 발전량 예측에 관한 연구 (Study on the Prediction of wind Power Generation Based on Artificial Neural Network)

  • 김세윤;김성호
    • 제어로봇시스템학회논문지
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    • 제17권11호
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    • pp.1173-1178
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    • 2011
  • The power generated by wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to predict the changing wind power. In this paper, neural network based wind power prediction scheme which uses wind speed and direction is considered. In order to get a better prediction result, compression function which can be applied to the measurement data is introduced. Empirical data obtained from wind farm located in Kunsan is considered to verify the performance of the compression function.

A Study of the Performance Prediction Models of Mobile Graphics Processing Units

  • Kim, Cheong Ghil
    • 반도체디스플레이기술학회지
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    • 제18권1호
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    • pp.123-128
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    • 2019
  • Currently mobile services are on the verge of full commercialization ahead of 5G mobile communication (5G). The first goal could be to preempt the 5G market through realistic media services utilizing VR (Virtual Reality) and AR (Augmented Reality) technologies that users can most easily experience. Basically this movement is based on the advanced development of smart devices and high quality graphics processing computing power of mobile application processors. Accordingly, the importance of mobile GPUs is emerging and the most concern issue becomes a model for predicting the power and performance for smooth operation of high quality mobile contents. In many cases, the performance of mobile GPUs has been introduced in terms of power consumption of mobile GPUs using dynamic voltage and frequency scaling and throttling functions for power consumption and heat management. This paper introduces several studies of mobile GPU performance prediction model with user-friendly methods not like conventional power centric performance prediction models.

유사 시계열 데이터 분석에 기반을 둔 교육기관의 전력 사용량 예측 기법 (Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data)

  • 문지훈;박진웅;한상훈;황인준
    • 정보과학회 논문지
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    • 제44권9호
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    • pp.954-965
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    • 2017
  • 안정적인 전력 공급은 전력 인프라의 유지 보수 및 작동에 매우 중요하며, 이를 위해 정확한 전력 사용량 예측이 요구된다. 대학 캠퍼스는 전력 사용량이 많은 곳이며, 시간과 환경에 따른 전력 사용량 변화폭이 다양하다. 이러한 이유로, 전력계통의 효율적인 운영을 위해서는 전력 사용량을 정확하게 예측할 수 있는 모델이 요구된다. 기존의 시계열 예측 기법은 학습 시점과 예측 시점 간의 차이가 클수록 예측 구간이 넓어짐으로 예측 성능이 크게 떨어진다는 단점이 있다. 본 논문은 이를 보완하려는 방안으로, 먼저 의사결정나무를 이용해 날짜, 요일, 공휴일 여부, 학기 등을 고려하여 시계열 형태가 유사한 전력 데이터를 분류한다. 다음으로 분류된 데이터 셋에 각각의 자기회귀누적이동평균모형을 구성하여, 예측 시점에서 시계열 교차검증을 적용해 대학 캠퍼스의 일간 전력 사용량 예측 기법을 제안한다. 예측의 정확성을 평가하기 위해, 성능 평가 지표를 이용하여 제안한 기법의 타당성을 검증하였다.

곡선회귀분석을 이용한 풍력발전 출력 예측에 관한 연구 (Study on the Prediction of Wind Power Outputs using Curvilinear Regression)

  • 최영도;정솔영;박범준;허진;박상호;윤기갑
    • KEPCO Journal on Electric Power and Energy
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    • 제2권4호
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    • pp.627-630
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    • 2016
  • Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

풍력발전소 소음 영향 예측식 개발에 관한 연구 (Study on a Development of the Prediction Equation of the Wind Power Plant Noise)

  • 구진회;이재원;이우석;정성수
    • 한국소음진동공학회논문집
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    • 제26권1호
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    • pp.49-54
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    • 2016
  • The wind power plants were installed in many places because of the low climate changing effects since 2000. Generally, the wind power plants located in the seaside and the mountainous area and the heights of the windmills are about 40 m~140 m above the ground level. So the noises emitted from the wind power plants propagate far away compared with other environment noise sources like trains and cars noise. Because of these reasons, the noise emitted from the wind power plant is easy to cause the additional social problems like as noise complaints. Under the situation, the ministry of environment has established the guideline to evaluate the environmental effects for the wind power plant. According to the guideline, the noise of the wind power plant has to meet 55 dB(A) at daytime and 45 dB(A) at night in the residential area, which is regulated in the noise and vibration management law. But, it is difficult to estimate the noise emitted from the wind power plant because of the absence of the prediction model of the wind power plant noise. Therefore, the noise prediction model for wind power plants using the regression analysis method is developed in this study. For the development of the model, the sound pressure levels of the wind power plants in Jeju island are measured and the correlations between the sound pressure levels are analyzed. Finally, the prediction equation of the wind power plant noise using by regression analysis method derived. The prediction equation for the wind power plant noise proposed in this study can be useful to evaluate the environmental effects in any wind power plant development district.

철도인프라용 태양광발전시스템 확대를 위한 기상정보 활용 발전량 예측 비교 연구 (Comparative Study to Predict Power Generation using Meteorological Information for Expansion of Photovoltaic Power Generation System for Railway Infrastructure)

  • 유복종;박찬배;이주
    • 한국철도학회논문집
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    • 제20권4호
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    • pp.474-481
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    • 2017
  • 국내에서 태양광 발전설비 설계 시 설계 단계에서의 태양광발전소의 발전량 예측은 국내 현장임에도 불구하고 PVsyst, PVWatts 등 해외 발전량 예측 프로그램과 해외 기상 자료를 이용하여 발전량을 예측하는 경우가 대부분을 차지하고 있는 실정이다. 본 논문에서는 기상정보를 활용한 발전량 예측 비교 연구를 위하여 현재 운영중인 2개 지역의 국내 태양광발전소를 대상지로 선정하였다. 발전량 예측 프로그램인 PVsyst를 활용하여 Meteonorm 7.1과 NASA-SSE의 해외 기상정보를 이용한 발전량 예측값과 국내 기상청 (Korea Meteorology Administration) 기상정보를 활용한 발전량 예측 정확성을 비교하였다. 추가적으로, 기상자료 비교 분석을 통한 발전량 예측 개선 방안을 연구하고, 최종적으로 실제 발전량과의 비교 분석을 통해 기후요소가 고려된 태양광 발전량 예측 수정 모델을 제시하였다.

발전 설비 지속 가능 운영 기술 연구 (A Study of the Sustainable Operation Technologies in the Power Plant Facilities)

  • 이창열;박길주;김태환;구영현;이성일
    • 한국재난정보학회 논문집
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    • 제16권4호
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    • pp.842-848
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    • 2020
  • 연구목적: 노후화된 발전기의 지속 가능한 운영을 위하여 효율적이며, 안전한 운영이 중요하다. 효율적 운영이란 경제적 관점이며, 안전한 운영은 발전 설비의 치명적 사고 발생에 대한 발생 이전의 사전 조치를 말한다. 그러므로 발전기의 지속가능 운영 모니터링을 위하여 관련된 센서 설치와 이를 기반으로 지속 가능에 대한 예측할 수 있는 모델에 대한 연구가 필요하다. 연구방법: 전기와 열에 대한 수요 예측, 엔진의 성능과 이상을 탐지하는 예측, 그리고 재 난 안전에 대한 예측 모델을 제시하였다. 이를 위하여 필요한 센서를 정의하였으며, 이를 기반으로 예측 모델을 각각 개발하여 수행하였다. 연구결과: 수요 예측 모델은 기존의 79%에서 90% 이상으로 예측 정확도를 향상시켰으며, 다른 2개 모델도 시스템의 지속가능한 안정적 운영을 지원하였다. 결론: 노후화된 발전설비의 지속가능 운영을 지원하기 위한 3가지 종류의 예측 모델을 개발하고 이를 제이비주식회사의 발전 설비에 실제 적용하여 운영하고 있다.

일반화 가법모형을 이용한 태양광 발전량 예측 알고리즘 (Solar Power Generation Prediction Algorithm Using the Generalized Additive Model)

  • 윤상희;홍석훈;전재성;임수창;김종찬;박철영
    • 한국멀티미디어학회논문지
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    • 제25권11호
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    • pp.1572-1581
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    • 2022
  • Energy conversion to renewable energy is being promoted to solve the recently serious environmental pollution problem. Solar energy is one of the promising natural renewable energy sources. Compared to other energy sources, it is receiving great attention because it has less ecological impact and is sustainable. It is important to predict power generation at a future time in order to maximize the output of solar energy and ensure the stability and variability of power. In this paper, solar power generation data and sensor data were used. Using the PCC(Pearson Correlation Coefficient) analysis method, factors with a large correlation with power generation were derived and applied to the GAM(Generalized Additive Model). And the prediction accuracy of the power generation prediction model was judged. It aims to derive efficient solar power generation in the future and improve power generation performance.

Identification of Correlative Transmission Lines for Stability Prediction

  • Cho, Yoon-Sung;Gilsoo Jang;Kwon, Sae-Hyuk;Yanchun Wang
    • KIEE International Transactions on Power Engineering
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    • 제11A권4호
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    • pp.15-20
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    • 2001
  • Power system stability is correlated with system structure, disturbances and operating conditions, and power flows on transmission lines are closely related with those conditions. This paper proposes a methodology to identify correlative power flows for power system transient and small-signal stability prediction. In transient stability sense, the Critical Clearing Time is used to select some dominant contingencies, and Transient Stability Prediction index is proposed for the quantitative comparison. For small-signal stability discusses a methodology to identify crucial transmission lines for stability prediction by introducing a sensitivity factor based on eigenvalue sensitivity technique. On-line monitoring of the selected lines enables to predict system stability in real-time. Also, a procedure to make a priority list of monitored transmission lines is proposed. The procedure is applied to a test system, and it shows capabilities of the proposed method.

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