Prediction method for hydropower system generation capacity based on least squares twin extreme learning machine

Abstract

[Objective] To address the issues of low accuracy and poor stability in traditional hydropower generation capacity forecasting, [Methods] a hybrid prediction model for power generation capacity of hydropower systems, combining coupled mode decomposition, machine learning, and swarm intelligence was proposed in this paper. First, the original output sequence was decomposed and denoised using successive variational mode decomposition(SVMD) to extract multi-scale feature signals for classification and modeling. Subsequently, a least squares twin extreme learning machine(LSTELM) was employed to predict each decomposed signal, and an Improved Grey Wolf Optimization(IGWO) algorithm was used to optimize the model parameters, enhancing the prediction performance. Finally, the prediction result of each sub-sequence were integrated and aggregated to obtain the final result. [Results] The result demonstrated that the proposed method achieved accurate and reliable predictions for three hydropower stations. At the Chitan Hydropower Station, for 1-day forecast period, the proposed model improved the Nash-Sutcliffe efficiency(NSE) compared to the extreme learning machine model by 12.88% and 12.11% for direct and multi-input multi-output strategies, respectively. As the forecast period increased from 1 to 8 days, the NSE of the traditional method gradually decreased from 0.884 0 and 0.888 5 to 0.573 5 and 0.567 1, while the NSE of the two proposed strategies decreased from 0.997 9 and 0.996 1 to 0.942 3 and 0.928 6. [Conclusion] The findings indicate that the proposed model is highly stable and generalizable in predicting the generation capacity of complex hydropower systems. SVMD effectively reduces noise influence in the generation capacity sequence, and the least squares method and twin structure enhance the generalization ability of the LSTELM model. The SVMD-IGWO-LSTELM model demonstrates higher prediction accuracy for hydropower stations with stable hydrological characteristics, while prediction accuracy slightly decreases for stations with complex hydrological features, but still remains high. This model provides an effective approach for predicting hydropower system generation capacity under changing environmental conditions.

Keywords

successive variational mode decomposition method / power generation output / least squares twin extreme learning machine / Improved Grey Wolf Optimization algorithm / influencing factors