Multistep Prediction of Multiple Small Hydropower Stations’ Total Power in a Watershed within a Day Considering the Distributed Discharge TCN-LSTM Model

Abstract

First, this paper proposes that although the small hydropower group in the basin terrace as a whole uses the hydrological natural flow to generate electricity, the small hydropower except the first stage will also reuse a certain proportion of the small hydropower outflow and immediately upper stage, solving the problem of the relationship between the total power prediction of the small hydropower group and the hydrological natural flow prediction at all levels. Second, this paper proposes a satellite remote sensing monitoring point selection method based on topographic elevation to determine the increment of rainfall collection area above each small hydropower dam site, solving the correspondence between the source of the incoming flow of each small hydropower and the rainfall collection area above the dam site nested step by step, and the problem that each small hydropower is dividing the hydrological and natural flow of the whole basin according to the increment of rainfall collection area above the dam site. Third, this paper proposes a method that combines computational yield flow, deep learning simulation of sink flow, and fitting small hydropower generation equations, i.e., considering the distributed yield flow TCN-LSTM model (DR-TCN-LSTM), which solves the problem that the major part of the incoming flow except for the first level small hydropower is the reuse of the outgoing flow and the immediate upper-level small hydropower, which is treated by the immediate upper-level small hydropower, rather than the purely natural state product of the problem. In an example of an 8-step prediction of multiple small hydropower stations’ total power in a certain basin in Guangxi, China within a day, this paper proposes a Distributed Runoff TCN-LSTM Model (DR-BP-LSTM), and the Nash coefficient of the total power is 0.919. The Nash coefficient is increased by 0.02 due to the calculated discharge. In the selection of deep learning models, the Nash coefficient of the prediction model proposed in this paper is 0.02 more than DR-TCN-GRU and 0.011 more than DR-BP-LSTM.