Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

Yiling Fan (), Zhuang Ma, Wanwei Tang, Jing Liang and Pengfei Xu
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Yiling Fan: Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
Zhuang Ma: Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
Wanwei Tang: Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
Jing Liang: Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China
Pengfei Xu: Hebei Key Laboratory of Intelligent Data Information Processing and Control, Tangshan University, Tangshan 063000, China

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

Abstract: Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient energy management systems and prediction technologies. Through optimizing scheduling and integration in PV power generation, the stability and reliability of the power grid can be further improved. In this study, a new prediction model is introduced that combines the strengths of convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms, so we call this algorithm CNN-LSTM-Attention (CLA). In addition, the Crested Porcupine Optimizer (CPO) algorithm is utilized to solve the short-term prediction problem in photovoltaic power generation. This model is abbreviated as CPO-CLA. This is the first time that the CPO algorithm has been introduced into the LSTM algorithm for parameter optimization. To effectively capture univariate and multivariate time series patterns, multiple relevant and target variables prediction patterns (MRTPPs) are employed in the CPO-CLA model. The results show that the CPO-CLA model is superior to traditional methods and recent popular models in terms of prediction accuracy and stability, especially in the 13 h timestep. The integration of attention mechanisms enables the model to adaptively focus on the most relevant historical data for future power prediction. The CPO algorithm further optimizes the LSTM network parameters, which ensures the robust generalization ability of the model. The research results are of great significance for energy generation scheduling and establishing trust in the energy market. Ultimately, it will help integrate renewable energy into the grid more reliably and efficiently.

Keywords: photovoltaic; time series; MRTPP; CPO; attention mechanism (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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