Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model

As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent operation of the grid system is facing significant challenges. Therefore, accurately forecasting PV power output at various time scales is particularly urgent. To meet this demand, this paper proposes an LSTM-Informer model based on an improved Stacking ensemble algorithm (ISt-LSTM-Informer). The proposed model improves the k-fold cross validation in the traditional Stacking algorithm to a time-series cross validation for integrating time-series forecasting models. Simultaneously, it utilizes long short-term memory (LSTM) and Informer as the base models. By integrating the advantages of the two base models, the ISt-LSTM-Informer achieves accurate short and medium-term PV power forecasting. To validate the effectiveness of the model, a historical dataset from a PV system located in Uluru, Australia, is used for various types of experiments. Among them, comparative experiments validate the superiority of the model. Compared with five other methods, the ISt-LSTM-Informer obtains 21 optimal results for the four evaluation metrics of RMSE, MAE, MAPE, and R2 across eight forecasting time scales. In addition, different combinations of base models are conducted to verify the advantages of the Stacking ensemble algorithm and the two base models, respectively.

Automated summary

With the integration of more photovoltaic (PV) systems into the grid, accurately forecasting PV power output at various time scales becomes urgent. This paper proposes an improved Stacking ensemble algorithm combined with LSTM and Informer models (ISt-LSTM-Informer) to achieve accurate short and medium-term PV power forecasting. Experimental results using a historical dataset from a PV system in Uluru, Australia, validate the superiority and effectiveness of the proposed model.