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XGBoost 기반의 2단계 확률적 일사량 예측과 태양광 예측 알고리즘의 성능 검증
이유림(Yurim Lee),김현진(Hyunjin Kim),이다한(Dahan Lee),이채정(Chaijung Lee),이두희(Duehee Lee) 대한전기학회 2019 전기학회논문지 Vol.68 No.12
We propose the novel solar power forecasting algorithm by using the Extreme Gradient Boosting (XGBoost) machine based on the 2-stage forecasting structure. Our algorithm is implemented to solve three problems. First, the solar power is linearly proportional to the solar irradiation on a target solar panel, but it is hard to obtain the target solar irradiation. Therefore, in the first stage, we predict the target solar irradiation by using the XGBoost based on numerical weather prediction, which is measured on a different location but modified for the target location. Second, the forecasting errors on the predicted solar irradiation can be transferred to the second stage when the predicted solar irradiation is used to predict the solar power. We forecast the conditional error distribution of predicted irradiation by collecting forecasting errors, and we sample solar irradiation scenarios, which are converted to the solar power scenarios. Then, the final point forecast of solar power is estimated by calculating the median of scenarios so that we can improve the forecasting accuracy. Third, in this process, the quality of numerical weather prediction deteriorates as the target hour is farther. Therefore, we build forecasting models for each target hour in parallel to minimize the forecasting accuracy deterioration from the quality deterioration. Finally, we verify our proposed algorithm by participating in the solar power forecasting competition hosted by KPX.
가변 임피던스 매칭 네트워크를 이용한 영상 감시 Disposable IoT용 광대역 CMOS RF 에너지 하베스터
이동구(Dong-gu Lee),이두희(Duehee Lee),권구덕(Kuduck Kwon) 대한전기학회 2019 전기학회논문지 Vol.68 No.2
This paper presents a CMOS RF-to-DC converter for video surveillance disposable IoT applications. It widely harvests RF energy of 3G/4G cellular low-band frequency range by employing a tunable impedance matching network. The proposed converter consists of the differential-drive cross-coupled rectifier and the matching network with a 4-bit capacitor array. The proposed converter is designed using 130-nm standard CMOS process. The designed energy harvester can rectify the RF signals from 700 MHz to 900 MHz. It has a peak RF-to-DC conversion efficiency of 72.25%, 64.97%, and 66.28% at 700 MHz, 800 MHz, and 900 MHz with a load resistance of 10kΩ, respectively.
Gradient Boosting Machine과 Laplace Distribution 기반의 확률적 전력수요 예측 알고리즘 연구
박세준(Sejun Park),김현진(Hyunjin Kim),이두희(Duehee Lee) 대한전기학회 2021 전기학회논문지 Vol.70 No.11
In this paper, we develop three probabilistic electric load forecasting approaches: two parametric approaches and one non-parametric approach. In the parametric approach, we design the probability of load forecasts as the Laplace distribution since the empirical distribution of load forecasts has a shape of Laplace distribution. We also design the probability of load forecasts as the Gaussian distribution, since it has been widely used in other studies. We compare the forecasting accuracy of two distributions. The means of distributions are estimated by using the gradient boosting machine (GBM), and the standard deviations of distributions are estimated by analyzing forecasting errors through the cross validation. In the non-parametric approach, we find the probability of load forecasts by using the quantile regression (QR). Finally, we compare the forecasting accuracy of parametric and non-parametric approaches by measuring the accuracy on the pinball loss function. A parametric approach based on the Laplace distribution and GBM is the most accurate approach.