Abstract
In order to better mine and analyze effective information from massive electric power big data and improve the short-term power prediction accuracy of photovoltaic power generation data, this paper proposes a short-term power prediction method based on improved kernel principal component analysis and back propagation neural network optimized by particle swarm optimization (IKPCA-PSO-BPNN). The IKPCA method is used to reduce the feature dimension, and the processed data is trained and predicted in the PSO-BPNN model, which improves the model performance and prediction accuracy.
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Acknowledgements
This work was supported by the “Thirteenth Five-Year Plan” for scientific and technological research and planning of the Education Department of Jilin Province (JJKH20200121KJ).
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Wu, Y., Shi, Y. (2021). Short-Term Power Prediction for Photovoltaic Power Generation Based on IKPCA and PSO-BPNN. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_10
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DOI: https://doi.org/10.1007/978-981-33-6757-9_10
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