A short-term power prediction of photovoltaic based on SSA-DBN

Zhonghua Liang; XiaoHong Zhang

doi:10.1117/12.3024474

13 May 2024 A short-term power prediction of photovoltaic based on SSA-DBN

Zhonghua Liang, XiaoHong Zhang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131591N (2024) https://doi.org/10.1117/12.3024474
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

A single prediction model is not a sufficient solution to the problems arising from the stochasticity and high volatility of PV power output. To improve prediction accuracy and mitigate the negative impact on the grid, this study proposes a short-term PV power prediction technique based on SSA-DBN. Firstly, we performed Pearson's method correlation analysis and feature screening for each meteorological feature quantity, then we used SSA to optimise the parameter settings of the DBN, and selected the optimal hyperparameters to give full play to the model performance, so as to overcome the limitations of the traditional DBN model, and ultimately compared it with a single Long-Short-Term Memory (LSTM) and Deep Belief Network (DBN) model. The experimental findings demonstrate that the methodology proposed in this paper exhibits superior predictive precision.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhonghua Liang and XiaoHong Zhang "A short-term power prediction of photovoltaic based on SSA-DBN", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131591N (13 May 2024); https://doi.org/10.1117/12.3024474

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