期刊
IEEE ACCESS
卷 9, 期 -, 页码 36571-36588出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3062776
关键词
Forecasting; Predictive models; Meteorology; Training; Data models; Computational modeling; Uncertainty; Long short-term memory (LSTM); photovoltaic power forecasting; nonlinear auto-regressive neural networks with exogenous input (NARXNN); Tabu Search Algorithm (TSA)
资金
- National Priorities Research Program (NPRP) under Qatar National Research Fund (a member of Qatar Foundation) [NPRP10-0101-170082]
- IBERDROLA QSTP LLC
- Qatar National Library
The proposed hierarchical learning technique combining NARXNN and LSTM models for PV power forecasting shows superior accuracy compared to existing benchmarks, as demonstrated through real datasets.
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with exogenous input (NARXNN) with Long Short-Term Memory (LSTM) model. First, the NARXNN model acquires the data to generate a residual error vector. Then, the stacked LSTM model, optimized by Tabu search algorithm, uses the residual error correction associated with the original data to produce a point and interval PVPF. The performance of the proposed PVPF technique was investigated using two real datasets with different scales and locations. The comparative analysis of the NARX-LSTM with twelve existing benchmarks confirms its superiority in terms of accuracy measures. In summary, the proposed NARX-LSTM technique has the following major achievements: 1) Improves the prediction performance of the original LSTM and NARXNN models; 2) Evaluates the uncertainties associated with point forecasts with high accuracy; 3) Provides a high generalization capability for PV systems with different scales. Numerical results of the comparison of the proposed NARX-LSTM method with two real-world PV systems in Australia and USA demonstrate its improved prediction accuracy, outperforming the benchmark approaches with an overall normalized Rooted Mean Squared Error (nRMSE) of 1.98% and 1.33% respectively.
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