Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting

Andi A. H. Lateko, Hong-Tzer Yang, Chao-Ming Huang, Happy Aprillia, Che-Yuan Hsu, Jie-Lun Zhong and Nguyễn H. Phương
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Andi A. H. Lateko: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Hong-Tzer Yang: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Chao-Ming Huang: Department of Electrical Engineering, Kun Shan University, Tainan 710, Taiwan
Happy Aprillia: Department of Industrial Engineering and Process, Kalimantan Institute of Technology, Balikpapan 76127, Indonesia
Che-Yuan Hsu: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Jie-Lun Zhong: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan
Nguyễn H. Phương: Department of Electrical Engineering, National Cheng Kung University, Tainan 701, Taiwan

Energies, 2021, vol. 14, issue 16, 1-23

Abstract: Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.

Keywords: ensemble forecasting; recurrent neural network; PV power forecasting; clustering method (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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