An Integrated Transfer Learning Method for Power Generation Prediction of Run-Off Small Hydropower in Data-Scarce Areas

Abstract

historical data scarce and varying patterns of new built run-off small hydropower (RSHP) limits precise power generation prediction. Unforeseen hydropower can induce uneconomic power grid operations. To address this issue, a novel transfer learning method enabling integration of public RSHP knowledge is proposed. First, a RSHP data matching algorithm is proposed to pre-filter similar source domain data and produce a RSHP database matching patterns of target RSHP. This algorithm allows us to improve performance of transfer learning model. Next, public prediction knowledge implicated in the RSHP database is learned towards a CNN-BiLSTM hybrid pre-trained network. Then, the pre-trained network is transferred to the target RSHP prediction models by hyper-parameter fine-tuning algorithm, which reduces divergence between the pre-trained network outputs and the target domain data. As a result, accurate new RSHP prediction models can be generated under the challenge of data lack. At the last, the RSHP prediction models are fed back to the fine-tuning algorithm such that generalizability of the models enables life-long self-renewal. The real-world case demonstrates the superiority of the proposed method in terms of accuracy and data utilization. The average prediction error of the proposed method is 16.27% lower than the best traditional alternative.