A Short-Term Photovoltaic Power Forecasting Method Based on Multiscale Decomposition and Adaptive Optimization

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Existing studies still have limitations in characterizing the complex temporal patterns of photovoltaic time series. In particular, current forecasting models often struggle to capture local abrupt fluctuations and nonlinear relationships among variables, while hyperparameter optimization remains challenging. To overcome these limitations, this paper proposes a CNN-iTransformer short-term photovoltaic power forecasting method based on multiscale decomposition and adaptive optimization. First, the original photovoltaic power time series is decomposed into trend, seasonal, and residual components using seasonal-trend decomposition based on Loess (STL). The residual component is then further decomposed through variational mode decomposition (VMD) to fully extract the latent multiscale temporal information embedded in the sequence. Second, a convolutional neural network (CNN) is employed to extract local fluctuation features, while the iTransformer is utilized to model the nonlinear relationships and long-term temporal dependencies between multiple meteorological variables and photovoltaic power. Finally, the Phototropic Growth Algorithm (PGA) is introduced to optimize key hyperparameters of the forecasting model, thereby improving its generalization capability and forecasting accuracy under multi-seasonal scenarios. Simulation experiments are conducted using real-world data from a photovoltaic power station in Alice Springs, Australia. The experimental results show that, under the same PGA optimization conditions, the proposed model reduces the root mean square error (RMSE) and mean absolute error (MAE) by approximately 19.12% and 17.43%, respectively, compared with the baseline iTransformer. Meanwhile, its performance is better than that of several mainstream deep learning models, which verifies the generalization ability and forecasting accuracy of the proposed method under complex seasonal variations.

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