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-In...

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Vydáno v:Energy (Oxford) Ročník 283; s. 128669
Hlavní autoři: Cao, Yisheng, Liu, Gang, Luo, Donghua, Bavirisetti, Durga Prasad, Xiao, Gang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.11.2023
Témata:
algorithms
Australia
data collection
energy
Improved Stacking ensemble algorithm
Informer
Long short-term memory
Multi-timescale photovoltaic power forecasting
neural networks
solar collectors
solar energy
time series analysis
Australia
Long short-term memory
Multi-timescale photovoltaic power forecasting
Informer
Improved Stacking ensemble algorithm
ISSN:0360-5442
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Shrnutí: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. •A novel multi-timescale photovoltaic power forecasting model is proposed.•Time-series cross validation is introduced into the Stacking algorithm.•LSTM and Informer are utilized as the base models of the Stacking algorithm.•Various methods are compared to verify the proposed model’s effectiveness.•The model’s predictive accuracy is illustrated through various visualization methods.
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ISSN:0360-5442
DOI:10.1016/j.energy.2023.128669