Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO

Accurate and effective photovoltaic output forecasting is critical for the high-proportion integration of solar power generation into the power grid. To address the issues that existing prediction methods often ignore long-term dependencies in photovoltaic power sequences and tend to produce suboptimal hyperparameters combinations, a hybrid photovoltaic power prediction model composed of GIKM, ECPO, VMD and Transformer-BiLSTM is established. Firstly, a brand-new integrated method for selecting multi-dimensional similar days based on GRA and an improved K-medoids is proposed to identify the strongly correlated historical days with meteorological conditions similar to those of the predicted day. Secondly, an innovative combination of Logistic-Tent composite chaotic mapping and Gaussian mutation is used to enhance the optimal performance of the CPO, which overcomes the issues of numerous suboptimal solutions and premature convergence to local optima that afflict traditional CPO algorithms. Thirdly, ECPO is used for the first time to determine the optimal parameter combination of VMD method (i.e. number of decomposed subsequences and penalty factor), which provides high-quality training samples. Next, a novel hybrid forecasting model combining BiLSTM and Transformer is proposed, where BiLSTM extracts temporal dynamics and Transformer captures the interdependencies among multivariate energy-related time series, thereby enhancing the generalization ability and robustness of this combined model during the power forecasting process. Accurate prediction is achieved by optimizing the epoch, number of hidden unit and learning rate of the Transformer-BiLSTM model based on ECPO. The performance of the proposed method is evaluated through four sets of comparative experiments and three evaluation metrics for two distinct PV stations located in two provinces (i.e. Yunnan and Gansu), China. The proposed ensemble model significantly outperforms other baseline models, with average MAE, RMSE, and MSE of 0.2328 MW, 0.2778 MW and 0.0809 MW2 in Yunnan, respectively, and 0.7231 MW, 1.0186 MW and 1.0583 MW2 in Gansu, respectively. Parallel cross-experiments at two photovoltaic stations with distinct meteorological conditions and installed capacities further verify the proposed model's technical superiority and robust performance, demonstrating strong potential for real-world application and promotion.