Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm

Keyong Hu (), Chunyuan Lang, Zheyi Fu, Yang Feng, Shuifa Sun and Ben Wang ()
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Keyong Hu: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Chunyuan Lang: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Zheyi Fu: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Yang Feng: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Shuifa Sun: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Ben Wang: School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

Mathematics, 2024, vol. 12, issue 16, 1-25

Abstract: With the continuous increase in the proportion of distributed photovoltaic power stations, the demand for photovoltaic power grid connection is becoming more and more urgent, and the requirements for the accuracy of regional distributed photovoltaic power forecasting are also increasing. A distributed regional photovoltaic power prediction model based on a stacked ensemble algorithm is proposed here. This model first uses a graph attention network (GAT) to learn the structural features and relationships between sub-area photovoltaic power stations, dynamically calculating the attention weights of the photovoltaic power stations to capture the global relationships and importance between stations, and selects representative stations for each sub-area. Subsequently, the CNN-LSTM-multi-head attention parallel multi-channel (CNN-LSTM-MHA (PC)) model is used as the basic model to predict representative stations for sub-areas by integrating the advantages of both the CNN and LSTM models. The predicted results are then used as new features for the input data of the meta-model, which finally predicts the photovoltaic power of the large area. Through comparative experiments at different seasons and time scales, this distributed regional approach reduced the MAE metric by a total of 22.85 kW in spring, 17 kW in summer, 30.26 kW in autumn, and 50.62 kW in winter compared with other models.

Keywords: GAT; CNN-LSTM-MHA (PC); power; basic model; meta-model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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