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    결측치 보간 알고리즘을 적용한 앙상블 기반의 태양광 발전량 예측 시스템 = Ensemble-based Solar Power Prediction System Using Missing Value Interpolation Algorithm

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    https://www.riss.kr/link?id=A108736046

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expanding. In particular, the share of solar and wind energy among new and renewable energies is rapidly increasing. However, uncertainty and volatility are inherent in renewable energy due to the characteristics of power generation that depend on natural conditions. This leads to a problem in which errors occur in the prediction of the amount of reserve energy required to secure the amount and cost of renewable energy generation. In this paper, we propose an ensemble-based solar power generation prediction system applying missing value interpolation algorithm. It predicts the amount of solar power generation by using weather forecast data from the Korea Meteorological Administration, and provides visualization and scheduling functions for the amount of power generation and predicted amount through a web page.
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    Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expandi...

    Environmental problems such as global warming due to excessive use of fossil fuels are becoming serious. In order to solve this problem, the supply of new and renewable energy is being activated, and the new and renewable energy market is also expanding. In particular, the share of solar and wind energy among new and renewable energies is rapidly increasing. However, uncertainty and volatility are inherent in renewable energy due to the characteristics of power generation that depend on natural conditions. This leads to a problem in which errors occur in the prediction of the amount of reserve energy required to secure the amount and cost of renewable energy generation. In this paper, we propose an ensemble-based solar power generation prediction system applying missing value interpolation algorithm. It predicts the amount of solar power generation by using weather forecast data from the Korea Meteorological Administration, and provides visualization and scheduling functions for the amount of power generation and predicted amount through a web page.

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    참고문헌 (Reference)

    1 박우근 ; 김지수 ; 임승민 ; 김철환, "태양광 발전의 출력 예측 불확실성 및 간헐성 보상 방법에 관한 연구" 대한전기학회 70 (70): 961-968, 2021

    2 정하영 ; 홍석훈 ; 전재성 ; 임수창 ; 김종찬 ; 박철영, "태양광 발전량 데이터의 시계열 모델 적용을 위한 결측치 보간 방법 연구" 한국멀티미디어학회 24 (24): 1251-1260, 2021

    3 박재현 ; 이진오 ; 강상조 ; 강민수, "결측치 처리: 어떤 방법이 최선인가?" 한국체육학회 44 (44): 385-398, 2005

    4 T. Chen, "XGBoost: A Scalable Tree Boosting System" 785-794, 2016

    5 권승일 ; 강상길, "XGBoost-LSTM 앙상블을 이용한 태양광 발전량 예측" 한국지능시스템학회 31 (31): 475-479, 2021

    6 IEA, "World Energy Outlook 2022"

    7 L. Fan, "To Predict the Power Generation based on Machine Learning Method" 2310 (2310): 012084-, 2022

    8 D.H. Shin, "Random Forests-Based Meteorological Data Imputation Method for PV Generation Forecasting" 547-548, 2021

    9 A. Nielsen, "Practical Time Series Analysis:Prediction with Statistics and Machine Learning" O’Reilly Media 2019

    10 D.J. Stekhoven, "MissForest—Non-Parametric Missing Value Imputation for Mixed-Type Data" 28 (28): 112-118, 2021

    1 박우근 ; 김지수 ; 임승민 ; 김철환, "태양광 발전의 출력 예측 불확실성 및 간헐성 보상 방법에 관한 연구" 대한전기학회 70 (70): 961-968, 2021

    2 정하영 ; 홍석훈 ; 전재성 ; 임수창 ; 김종찬 ; 박철영, "태양광 발전량 데이터의 시계열 모델 적용을 위한 결측치 보간 방법 연구" 한국멀티미디어학회 24 (24): 1251-1260, 2021

    3 박재현 ; 이진오 ; 강상조 ; 강민수, "결측치 처리: 어떤 방법이 최선인가?" 한국체육학회 44 (44): 385-398, 2005

    4 T. Chen, "XGBoost: A Scalable Tree Boosting System" 785-794, 2016

    5 권승일 ; 강상길, "XGBoost-LSTM 앙상블을 이용한 태양광 발전량 예측" 한국지능시스템학회 31 (31): 475-479, 2021

    6 IEA, "World Energy Outlook 2022"

    7 L. Fan, "To Predict the Power Generation based on Machine Learning Method" 2310 (2310): 012084-, 2022

    8 D.H. Shin, "Random Forests-Based Meteorological Data Imputation Method for PV Generation Forecasting" 547-548, 2021

    9 A. Nielsen, "Practical Time Series Analysis:Prediction with Statistics and Machine Learning" O’Reilly Media 2019

    10 D.J. Stekhoven, "MissForest—Non-Parametric Missing Value Imputation for Mixed-Type Data" 28 (28): 112-118, 2021

    11 S. Hochreiter, "Long Short-Term memory" 9 (9): 1735-1780, 1997

    12 G. Ke, "LightGBM: A Highly Efficient Gradient Boosting Decision Tree"

    13 "Korea Meteorological Administration"

    14 "Korea East-West Power Co"

    15 P. Cunningham, "K-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)"

    16 S.W. Park, "Interpolation Method and Its Reliability Evaluation of Wind Speed Data in Writing Typical Weather Data" 759-762, 2017

    17 E.Y. Park, "Do it! Jump to Flask: From Python Web Development to Distribution!" Easy Publishing 2020

    18 N. M. Noor, "Comparison of Linear Interpolation Method and Mean Method to Replace the Missing Values in Environmental Data Set" 803 : 278-281, 2014

    19 "Analysis of International New and Renewable EnergyPolicy and Market"

    20 S.H. Choi, "A Study on Solar Power Forecasting Ensemble Model for Increasing Flexibility in Power System" Sangmyung University General Graduate School 2021

    21 A. Mohammed, "A Comprehensive Review on Ensemble Deep Learning:Opportunities and Challenges" 35 (35): 757-774, 2023

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