Study on power prediction of runoff hydropower station based on learning model

Abstract

[Objective] Accurate power prediction of a runoff hydropower station is crucial for formulating generation scheduling plans and ensuring a reliable power supply strategy. Considering the strong randomness in the generation output of runoff hydropower stations and the low accuracy of direct prediction, a combined prediction model based on Adaptive Variational Mode Decomposition(VMD) and Temporal Convolutional Network(TCN) was proposed.[Methods] Initially, the Whale Optimization Algorithm(WOA) is employed to optimize the parameters of Variational Mode Decomposition(VMD), achieving optimal adaptive decomposition of the original output sequence. Subsequently, TCN model is individually established for trend prediction of each decomposed component. Finally, the obtained result are reconstructed to obtain the final prediction.[Results] The result shows that, compares to other models, the model established has varying degrees of improvement in prediction performance under the same conditions.[Conclusion] The result indicates:(1) The WOA-VMD method can effectively extract the characteristics of the output sequence of a runoff-type hydropower station and reduce the influence of the instability of its own data on the prediction result.(2) Compared to the five models of VMD-TCN, TCN, LSTM, RNN and BP, the proposed WOA-VMD-TCN prediction model can effectively improve the prediction accuracy of hydropower station power, providing a new and effective modeling approach for power prediction of runoff hydropower stations.

Keywords

runoff hydropower station / power prediction / whale swarm algorithm / Variational Mode Decomposition(VMD) / Temporal Convolutional Network(TCN) / influencing factors