Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method

Jingyue Wang, Zheng Qian, Jingyi Wang and Yan Pei
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Jingyue Wang: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Zheng Qian: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Jingyi Wang: School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Yan Pei: State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, China

Energies, 2020, vol. 13, issue 12, 1-17

Abstract: The common analog approach and ensemble methods in photovoltaic (PV) power forecasting are based on the forecasts from several numerical weather prediction (NWP) models. These may be not applicable to the very-short-term PV power forecasting, since forecasts based on NWP models are reliable in horizons longer than six hours. In this paper, a methodology for one-hour-ahead PV power forecasting is proposed. Instead of the NWP models, the persistence method is applied in the analog approach to produce meteorological forecasts. The historical data with meteorological predictions similar to the target forecast hour are identified to train the forecast model. Then, the feed forward neural networks (FNNs) act as the base predictors of the neural network ensemble method to replace the NWP-based PV power prediction methods. The forecast results produced by the FNNs are combined by the random forest (RF) algorithm. The performance of the proposed method is evaluated on a real grid-connected PV plant located in Southeast China. Results show that the proposed method outperforms six benchmark models: the persistence model, the support vector regression (SVR) model, the linear regression model, the RF model, the gradient boosting model, and XGBoost model. The improvements reach up to over 40% for the standard error metrics.

Keywords: analog approach; neural network ensemble; photovoltaic power forecasting; persistence method; feed forward neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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