SHORT-TERM WIND POWER COMBINATION FORECAST BASED ON MULTI-OBJECTIVE OPTIMIZATION AND DEEP LEARNING

Hu Jiaqiu; Zhuo Yixin; Tang Jian; Meng Wenchuan; Qi Huanxing; Liu Luning

doi:10.19912/j.0254-0096.tynxb.2023-1644

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 615-623. DOI: 10.19912/j.0254-0096.tynxb.2023-1644

SHORT-TERM WIND POWER COMBINATION FORECAST BASED ON MULTI-OBJECTIVE OPTIMIZATION AND DEEP LEARNING

Hu Jiaqiu¹, Zhuo Yixin¹, Tang Jian¹, Meng Wenchuan², Qi Huanxing³, Liu Luning⁴

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Abstract

Aiming at the characteristics of nonlinearity and volatility of wind power time series, this article proposes a wind power combination forecasting method based on multi-objective optimization and deep learning. The proposed method applies complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN） to decompose the original wind power time series, and uses extreme learning machine （ELM）, long short-term memory （LSTM）, and time convolutional network （TCN） to train forecasting models and make forecasts on subsequences of CEEMDAN. Based on this, a combination forecasting model is established, where multi-objective Harris Hawks optimization （MOHHO） and deep deterministic policy gradient （DDPG） are combined to dynamically calculate the optimal weights. Using measured wind power data from an actual wind farm in Guangxi province for model testing and comparison, results show that the developed model performs best over all four datasets, with root mean squared error reduced by 12.93%, 13.91%, 12.38% and 9.71% compared to the simple average combination method, respectively, which verifies the effectiveness of the developed method.

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wind power / forecast / neural network / combination forecasting / multi-objective optimization / deep learning

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Hu Jiaqiu, Zhuo Yixin, Tang Jian, Meng Wenchuan, Qi Huanxing, Liu Luning. SHORT-TERM WIND POWER COMBINATION FORECAST BASED ON MULTI-OBJECTIVE OPTIMIZATION AND DEEP LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 615-623 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1644

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