Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Short-term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

Version 1 : Received: 3 May 2023 / Approved: 4 May 2023 / Online: 4 May 2023 (08:16:05 CEST)

A peer-reviewed article of this Preprint also exists.

Gao, H.; Qiu, S.; Fang, J.; Ma, N.; Wang, J.; Cheng, K.; Wang, H.; Zhu, Y.; Hu, D.; Liu, H.; Wang, J. Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM. Sustainability 2023, 15, 8266. Gao, H.; Qiu, S.; Fang, J.; Ma, N.; Wang, J.; Cheng, K.; Wang, H.; Zhu, Y.; Hu, D.; Liu, H.; Wang, J. Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM. Sustainability 2023, 15, 8266.

Abstract

Photovoltaic(PV) power generation is highly nonlinear and stochastic. Accurate prediction of PV power generation plays a crucial role in grid connection as well as the operation and scheduling of power plants. To predict the PV power combination model, this paper suggests a method based on Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Nonlinear Auto-Regressive Neural Networks with Exogenous Input (NARXNN), Long Short Term Memory (LSTM) Neural Network, and Light Gradient Boosting Machine (LightGBM) algorithms. To attempt to reduce the non-smoothness of PV power, the weather variable features with the greatest effect on PV power are first identified by correlation analysis. Following this, the PV power modal decomposition is split and reorganized into a new feature matrix. Finally, a NARX is used to obtain preliminary PV power components and residual vector features, and the PV power is predicted by combining three models of LightGBM, LSTM, and NARX and then the final prediction results are obtained by combining the PV power prediction results using error inverse method weighted optimization. The prediction results demonstrate that the model put forth in this paper outperforms those of other models and validate the model's validity by utilizing real measurement data from Andre Agassi College in the United States.

Keywords

PV Power Prediction; Mode Decomposition; NARX; LSTM; LightGBM

Subject

Engineering, Energy and Fuel Technology

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