Long-term inflow forecast using meteorological data based on long short-term memory neural networks

Abstract

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Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained by inverse distance weighting (IDW). Second, the maximal information coefficient (MIC) can adequately measure the degree of correlation between meteorological data and inflow; therefore, the MIC can distinguish key attributes from massive meteorological data and further reduce the computational burden. Last, LSTM is chosen as the prediction method due to its powerful nonlinear predictive capability, which can couple historical inflow records and meteorological data to forecast inflow. The Xiaowan hydropower station is selected as the case study. To evaluate the effectiveness of the M-LSTM for runoff prediction, several methods including LSTM, meteorological data backpropagation neural network (M-BPNN), meteorological data support vector regression (M-SVR) are employed for comparison with the M-LSTM and six evaluation criteria are used to compare its performance. Results revealed that M-LSTM outperforms other test methods in developing the long-term prediction method.