Attention-Based Encoder-Decoder Model for Photovoltaic Power Generation Prediction

The weather factors that affect the output of photovoltaic power generation systems have great volatility and discontinuities. Thus how to accurately predict the output of photovoltaic power generation has become a crucial issue. In this paper, we propose an attention-based Encoder-Decoder model for photovoltaic power generation. Filtered data based on maximum information coefficient is used as one of the features to reconstruct the experiment data. Then the attention mechanism is introduced to the Encoder-Decoder model, which constructed by Long Short-Term Memory (LSTM) neurons. We implement this experiment based on actual photovoltaic power plant examples and experimental results confirm the accuracy and applicability of the proposed model in predicting photovoltaic power generation