| Issue |
E3S Web Conf.
Volume 194, 2020
2020 5th International Conference on Advances in Energy and Environment Research (ICAEER 2020)
|
|
|---|---|---|
| Article Number | 01029 | |
| Number of page(s) | 5 | |
| Section | Energy Engineering and Energy Development and Utilization | |
| DOI | https://doi.org/10.1051/e3sconf/202019401029 | |
| Published online | 15 October 2020 | |
Short-term Forecast of Multiple Loads in Integrated Energy System Based on IPSO-WNN
1 North China Electric Power University, 102206, Beijing China
2 State Grid Economic and Technological Research Institute Co., Ltd., 102209, Beijing China
3 State Grid Fujian Electric Power Co. Ltd., 350012, Fuzhou China
* Corresponding author: 1204301572@qq.com
Accurate short-term energy load forecasting has a considerable influence on the economic scheduling and optimal operation of integrated energy system. This study proposes an improved particle swarm optimization-wavelet neural network (IPSO-WNN) method for short-term load forecasting of integrated energy system. First, Kendall rank correlation coefficient in Copula theory is used to analyze the correlation among the influencing factors, through which the influencing factors with strong correlation are selected as input variables of the model. Secondly, chaos algorithm and adaptive weight selection strategy are introduced in the POS-WNN forecasting model to improve the prediction accuracy. Therefore, a short-term load forecasting model of integrated energy system based on IPSO-WNN is established. Finally, the analysis of examples shows that the load prediction accuracy is significantly improved based on the IPSO-WNN model compared with the traditional forecasting model.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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