| Issue |
E3S Web Conf.
Volume 466, 2023
2023 8th International Conference on Advances in Energy and Environment Research & Clean Energy and Energy Storage Technology Forum (ICAEER & CEEST 2023)
|
|
|---|---|---|
| Article Number | 02001 | |
| Number of page(s) | 5 | |
| Section | Green Energy Technology and Low Carbon Energy Saving Strategy | |
| DOI | https://doi.org/10.1051/e3sconf/202346602001 | |
| Published online | 15 December 2023 | |
Power System Peak Regulation Demand Forecasting Based on LSTM Neural Network
1 State Grid Fujian Economic Research Institute, Fuzhou, China
2 College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, China
* aliny-02@163.com
** wuw_fjjy@163.com
*** linwei@tju.edu.cn
**** 675250747@qq.com
In the context of a high proportion of renewable energy integrated to the power grid, the net load may has significant fluctuations, and it is necessary to quantify peak regulation demand of power system. This paper stablishes a peak regulation demand prediction model based on long short-term memory (LSTM) method by training historical data. The typical data such as load and renewable energy output are selected as the input vector, and correlation coefficients are used to process and simplify the input vector. The historical prediction errors are used to set margins for peak regulation demand prediction. The case study shows that the proposed model can effectively predict the peak regulation demand of the power system.
© The Authors, published by EDP Sciences, 2023
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