Photovoltaic Short-Term Output Power Forecast Model Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise–Kernel Principal Component Analysis–Long Short-Term Memory

Lan Cao (), Haoyu Yang, Chenggong Zhou, Shaochi Wang, Yingang Shen and Binxia Yuan ()
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Lan Cao: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Haoyu Yang: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Chenggong Zhou: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Shaochi Wang: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Yingang Shen: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China
Binxia Yuan: College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China

Energies, 2024, vol. 17, issue 24, 1-14

Abstract: To solve the problem of photovoltaic power prediction in areas with large climate changes, this article proposes a hybrid Long Short-Term Memory method to improve the prediction accuracy and noise resistance. It combines the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and kernel principal component analysis (KPCA) algorithm. The ICEEMDAN algorithm reduces the instability of the environmental factor sequence. The KPCA algorithm reduces the input dimensions of the model. LSTM performs dynamic time modeling of the multivariate feature sequences to predict the output PV power. The adaptability of the ICEEMDAN-KPCA-LSTM model is assessed with datasets from a PV plant in west China and evaluated by root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared metrics. Using 70% of the datasets for output PV power estimation, the results show a good performance, with an RMSE of 4.3715, MAPE of 8.9264%, and R-squared value of 89.973%. By comparing with other prediction models, the ICEEMDAN-KPCA-LSTM photovoltaic output power model outperforms other models.

Keywords: photovoltaic output power prediction; short-term forecasting; ICEEMDAN; KPCA; LSTM (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: 2024
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