전압, 전류데이터를 이용한 선형회귀모델의 태양광발전량 예측

• Published in: 한국태양에너지학회 논문집 Vol. 41; no. 5; pp. 47 - 58
• Main Authors: 이용규(Lee Yong Kyu), 신우균(Shin Woo-Gyun), 주영철(Ju Young-Chul), 황혜미(Hwang Hye-Mi), 강기환(Kang Gi-Hwan), 고석환(Ko Suk-Whan), 장효식(Chang Hyo-Sik)
• Format: Journal Article
• Language: Korean
• Published: 한국태양에너지학회 01.10.2021
• Subjects: 기타공학; 머신 러닝(Machine Learning); 태양광발전 추정(Photovoltaic power Estimation); 전류(Current); 전압(Voltage); 선형회귀분석법(Linear Regression Analysis)
• ISSN: 1598-6411; 2508-3562
• DOI: 10.7836/kses.2021.41.5.047

PV systems have the disadvantages of large fluctuations in power and of not being controllable due to external factors. In addition, small-scale PV plants rarely receive maintenance after installation. Managers thus need a monitoring system that predicts the power of the PV plant in order to maintain performance and facilitate O&M. Recently, methods using big data to predict PV plant power have been applied. In this paper, power was predicted through learning based on PV plant field data. Furthermore, the error of the estimated power was analyzed through accuracy evaluations, RMSE, and R2 analysis. As the learning method, linear regression analysis was applied among machine learning models. Existing linear regression models can immediately estimate power by learning irradiation data as input variables and power data as output variables. However, if the PV system malfunctions, the accuracy of the estimated power generation decreases. In this paper, in order to address this problem, power was estimated by learning irradiation data as input variables and voltage and current data as output variables rather than directly estimating the power. As a result, the RMSE of the proposed linear regression equation was 15.9235kw, yielding a better power estimate than the existing method (16.4241kw). KCI Citation Count: 1