Ultra-Short-Term Load Forecasting Method for Aggregated Users Considering the Impact of Temperature-Controlled Load Characteristics

doi:10.11930/j.issn.1004-9649.202502075

Abstract

Abstract:

The load of aggregated users with a high proportion of temperature-controlled loads is prone to abrupt in characteristics due to factors such as temperature variations, leading to temporal distribution shifts in load characteristics across different historical periods. This results in poor performance of existing load forecasting modeling methods for aggregated users due to insufficient generalization capability. Drawing on the concept of transfer learning for extracting domain-invariant features in the spatial dimension, an ultra-short-term load forecasting method for aggregated users based on time-domain invariant feature modeling is proposed. Since the cycles of temporal distribution shifts in load data and the boundaries of these cycles are typically unknown, firstly, the temporal distribution shift is quantified, and the load is segmented into sequences with significant distribution differences to support the subsequent extraction of time-domain common features among samples. Then, a Transformer-based time-domain invariant feature extraction algorithm is proposed, which minimizes the temporal distribution differences among data samples with varying distributions to extract time-domain invariant features, thereby optimizing load forecasting modeling and improving prediction accuracy under scenarios of abrupt load characteristic changes. Finally, the superiority of the proposed method is validated using real residential load data.

Key words: temperature-controlled load, data temporal distribution shift, transformer model, ultra-short-term load forecasting, deep learning

MENG Hao, XU Fei, FU Shuai, SUN Peng, HAO Ling, LIU Boyu, LIU Zhiwei. Ultra-Short-Term Load Forecasting Method for Aggregated Users Considering the Impact of Temperature-Controlled Load Characteristics[J]. Electric Power, 2025, 58(12): 63-72, 85.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202502075

https://www.electricpower.com.cn/EN/Y2025/V58/I12/63

Figures/Tables 10

Fig.1 Data shift degree indicator

Fig.2 Flow chart of research methods

Fig.3 Structure of load forecasting model

Table 1 Accuracy comparison of different numbers of domains

域数量	调和精度/%	RMSE精度/%	MAE精度/%
2	95.72	94.91	96.43
3	95.82	95.25	96.91
4	96.08	94.89	96.44
5	96.17	95.47	97.11
6	96.07	95.33	96.90
7	96.10	94.93	96.52
8	95.75	94.32	95.81

Fig.4 Comparison of forecasting accuracy of different sequence partitioning methods

Table 2 Test set accuracy

方法	调和精度/%	NRMSE精度/%	NMAE精度/%
1	94.65	94.37	95.61
2	96.02	95.13	96.91
3	95.89	95.27	96.93
4	91.93	90.68	92.50
5	96.17	95.47	97.11

Fig.5 RMSE accuracy for different forecasting steps

Fig.6 Comparison of forecasting results of different methods under different time scales

Fig.7 Accuracy comparison under different temporal distribution shift degrees

Fig.8 Improvement degree under different temporal distribution shift degrees

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