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Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm
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DOI：10.1016/j.energy.2023.128171.png)
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Abstract
En 中文
Solar energy is highly economical and widespread in new energy applications, and analyzing solar radiation information is an important part of solar photovoltaic power applications. However, because of its data complexity and difficulty to measure, solar radiation data needs to be predicted. Temporal Convolutional Network (TCN) model is used to extract features and Artificial Ecosystem-based Optimization (AEO) algorithm is used to optimize the parameters of TCN. Out of consideration for the phenomenon of strong fluctuations and complex features of solar radiation data, the optimal variational mode decomposition (OVMD) method is incorporated into the model. First, the signal decomposition is performed on the original data to obtain several subsequences, and then aggregated by fuzzy entropy to reduce the number of sequences, after which the data are fed into the TCN model and the model parameters are optimized using the improved AEO algorithm, and finally the results of the model prediction are the output. Four months of solar radiation data are selected for testing, it is finally concluded that the OVMD-IAEO-TCN model can be used for solar radiation prediction with higher accuracy and reliability than the other nine comparison models.
Keywords:
Solar radiation prediction
Optimal variational mode decomposition
Artificial ecosystem-based optimization
Temporal convolutional network
Journal
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9.4
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4.3W
Citations:
20.2W
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