Short-term Load Probabilistic Forecasting Based on Conditional Enhanced Diffusion Model

被引：0
|
作者：
Liu, Jinxiang [1 ]
Zhang, Jiangfeng [2 ]
Dong, Shanling [1 ]
Liu, Meiqin [1 ,3 ]
Zhang, Senlin [1 ,4 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou
[2] Electric Power Research Institute, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou
[3] Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an
[4] Jinhua Institute of Zhejiang University, Jinhua
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 23期
关键词：
attention mechanism; diffusion model; load forecasting; probabilistic forecasting;
D O I：
10.7500/AEPS20240109003
中图分类号：
学科分类号：
摘要：
Load probabilistic forecasting can provide guidance for power grid planning, and the conditional generation model can effectively improve the forecasting performance by mining historical similar-day information. However, previous studies did not pay attention to the curve shape information and the noise analysis function of unconditional models, which increased the uncertainty of the generation curve. Therefore, a short-term load probabilistic forecasting method based on conditional enhanced diffusion model is proposed. Firstly, an improved iTransformer daily load forecasting model is constructed to forecast the adjacent daily load data. Secondly, a diffusion model combining multi-head self-attention mechanism and U-net is constructed using a loss function that combines unconditional noise estimation and conditional noise estimation. Then, the daily load forecasting results and characteristics such as temperature are used as conditional inputs. Through the reverse diffusion process of conditional enhanced guidance, multiple sets of random noise are denoised to generate multiple load curves for probability density analysis. Finally, based on a publicly available dataset from a region in China and comparative tests with various models, the case study analysis demonstrates that the proposed method has higher forecasting accuracy. © 2024 Automation of Electric Power Systems Press. All rights reserved.
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页码：197 / 207
页数：10
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