SHORT-TERM NET LOAD PROBABILITY PREDICTION BASED ON HIGH-FREQUENCY ENHANCED DIFFUSION MODEL

Wang Chenghuang; Luo Lizi; Long Huan; Gao Runtian; Wang Xiaoming

doi:10.19912/j.0254-0096.tynxb.2024-2184

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 103-113. DOI: 10.19912/j.0254-0096.tynxb.2024-2184

SHORT-TERM NET LOAD PROBABILITY PREDICTION BASED ON HIGH-FREQUENCY ENHANCED DIFFUSION MODEL

Wang Chenghuang¹, Luo Lizi¹, Long Huan², Gao Runtian¹, Wang Xiaoming^3,4

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Abstract

In order to ensure the safe and stable operation of the power grid under the scenario of high proportion of clean energy access, a short-term net load probability prediction method based on high frequency enhanced diffusion model is proposed. Firstly, key meteorological features are extracted based on the eXtreme Gradient Boosting（XGBoost） algorithm and maximum information coefficient（MIC）to form a multivariate input condition feature set. Secondly, a conditional diffusion model architecture based on cross-attention mechanism is constructed. For the encoding-decoding link, a CNN-LSTM network incorporating wavelet attention and a multi-channel convolutional neural network are designed respectively to enhance the ability to capture high-frequency features. Then, based on the initial predicted values and kernel density estimation methods, the pre-trained model is fine-tuned using an improved loss function to improve the quality of the generated intervals further. Finally, an example analysis is carried out based on the actual load data of a power grid in East China, and the results show that the proposed method has a higher prediction accuracy when compared with various models.

Key words

distribution networks / load forecasting / feature extraction / conditional diffusion model / wavelet attention / model fine-tune

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Wang Chenghuang, Luo Lizi, Long Huan, Gao Runtian, Wang Xiaoming. SHORT-TERM NET LOAD PROBABILITY PREDICTION BASED ON HIGH-FREQUENCY ENHANCED DIFFUSION MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 103-113 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2184

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