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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.11
- 수록면
- 1,633 - 1,639 (7page)
- DOI
- 10.5370/KIEE.2021.70.11.1633
이용수
초록· 키워드
오류제보하기
Due to the intermittent nature of Renewable Energy Sources (RES) and complexed pattern of time series data, increasing the forecasting accuracy by conventional methods and single-learner based Machine Learning (ML) algorithms is becoming increasingly challenging. Therefore, appropriate combination of ML schemes and data processing techniques is very important to increase the prediction accuracy of RES. This paper presents an intelligent Photovoltaic (PV) forecasting model using an ensemble learner approach based on a combination of bagging, boosting, and stacking algorithms and a Support Vector Regression with Kernel Linear (SVRL) meta-learner. The proposed approach pre-processes time series data using Correlation Matrix Analysis (Corr) and Principal Component Analysis (PCA). In level 0 of the STACK model, seven hybrid models are used as base learners and their individual predictions are used as input for the SVRL meta-learner in level 1. To evaluate the performance of our model, data collected from a 350KW 3rd PV power plant in Gyeongnam, South Korea, were used for simulations, and results were compared with bagging, boosting and bagging-boosting algorithms used separately, and results show higher forecasting accuracy of the proposed algorithm.
목차
Abstract
1. Introduction
2. The Proposed Intelligent PV Power Forecasting Model
3. Performance Evaluation
4. Results and Discussion
5. Conclusion
References
참고문헌 (23)
참고문헌 신청
Behera, M.K. / 2018 / Solar photovoltaic power forecasting using optimized modified extreme learning machine technique / Engineering Science and Technology, an International Journal 21:428~438
Dawan, P. / 2020 / Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model / Energies 13:351
Zhang, Y. / 2020 / Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis / Renewable Energy 149:577~586
Semero, Y.K. / 2018 / Pv power forecasting using an integrated ga-pso-anfis approach and gaussian process regression based feature selection strategy / CSEE Journal of Power and Energy Systems 4:210~218
Zamo, M. / 2014 / A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part i:Deterministic forecast of hourly production / Solar Energy 105:792~803
Dawan, P. / 2020 / Comparison of power output forecasting on the photovoltaic system using adaptive neuro-fuzzy inference systems and particle swarm optimization-artificial neural network model / Energies 13:351
Zhang, Y. / 2020 / Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis / Renewable Energy 149:577~586
Semero, Y.K. / 2018 / Pv power forecasting using an integrated ga-pso-anfis approach and gaussian process regression based feature selection strategy / CSEE Journal of Power and Energy Systems 4:210~218
Zamo, M. / 2014 / A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part i:Deterministic forecast of hourly production / Solar Energy 105:792~803
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