Wind farm power forecasting by physical data fusion

doi:10.11930/j.issn.1004-9649.202510007

Abstract

Abstract:

Power forecasting is a fundamental research topic in the wind power industry. Existing wind power forecasting methods predominantly rely on either data-driven or physics-driven approaches, with few studies combining physical models and data-driven techniques despite their significant complementary potential. A data-driven model is established using K-means clustering, empirical mode decomposition, and parallel weighted long short-term memory networks. A novel integrated approach combining physics-driven and data-driven methods was developed for wind farm forecasting. Validation using real-world data from a Chinese wind farm demonstrated that the proposed integrated method achieved 21.67% higher prediction accuracy than data-driven methods and 35.17% higher accuracy than physics-driven methods. These results confirm the superiority and reliability of physics-data fusion methods in wind farm ultra-short power forecasting.

Key words: wind power prediction, data-driven, physical-data fusion

ZHAO Jun, ZHANG Shifeng, SONG Jinge. Wind farm power forecasting by physical data fusion[J]. Electric Power, 2026, 59(5): 176-182.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202510007

https://www.electricpower.com.cn/EN/Y2026/V59/I5/176

Figures/Tables 8

Fig.1 K-EMD-LSTM power forecasting method

Fig.2 Power prediction via physical data fusion

Fig.3 Wind farm turbine layout

Fig.4 Wind speed, wind direction, and temperature data

Fig.5 Bar chart of power forecasting errors for different data-driven methods

Fig.6 Comparison of forecasting errors in physics-driven, data-driven, and fusion models for power generation

Fig.7 Bar chart of prediction error metrics for different driving models

Table 1 Comparison of errors among different methods on the training set 单位：MW

模型	E_RMS	E_MA
物理驱动	18.024 8	12.648 9
数据驱动	24.197 6	14.302 5
融合模型	12.899 0	8.988 2

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