Predicting day-ahead photovoltaic power using CNN-BiLSTM and attention mechanism

Jiyao Shi; Kui Wang; Chengfei Wu

doi:10.1117/12.2660002

3 February 2023 Predicting day-ahead photovoltaic power using CNN-BiLSTM and attention mechanism

Jiyao Shi, Kui Wang, Chengfei Wu

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125110W (2023) https://doi.org/10.1117/12.2660002
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Highly accurate forecasts of day-ahead PV output are an effective way of dealing with the uncertainty of a high proportion of renewable energy generation on a growing power system. At the same time, PV output is highly intermittent and stochastic, making accurate forecasting difficult to achieve. In this paper, a hybrid neural network model consisting of a convolutional neural network with an attention mechanism and a Bi-directional long and short-term memory network is used to forecast the PV system data of the Yulara town for the first hour of the day. The correlation between the historical data and the data to be predicted was also compared using the MIC metric, and it was found that the correlation between the historical data and the data to be predicted at the same time from one week ago to one quarter ago was very high, thus adding it to the dataset to improve the prediction accuracy. Root mean square error and mean absolute error were used as evaluation metrics for the forecasting models. Compared with multivariate LSTM, support vector regression and decision tree regression, the evaluation metrics of the proposed methods in this paper are all better.

Citation Download Citation

Jiyao Shi, Kui Wang, and Chengfei Wu "Predicting day-ahead photovoltaic power using CNN-BiLSTM and attention mechanism", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125110W (3 February 2023); https://doi.org/10.1117/12.2660002

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