Abstract
The inflow forecasting is one of the most important technologies for modern hydropower station. Under the joint influence of soil, upstream inflow, and precipitation, the inflow is often characterized by time lag, nonlinearity, and uncertainty and then results in the difficulty of accurate multistep prediction of inflow. To address the coupling relationship between inflow and the related factors, this paper proposes a long short-term memory deep learning model based on the Bagging algorithm (Bagging-LSTM) to predict the inflows of future 3 h, 12 h, and 24 h, respectively. To validate the proposed model, the inflow and related weather data come from a hydropower station in southern China. Compared with the classical time series models, the results show that the proposed model outperforms them on different accuracy metrics, especially in the scenario of multistep prediction.
Highlights
For hydropower stations, power generation is the main source of economic benefits and water is the raw material of production
We briefly review the techniques in the existing literature for inflow forecasting. e researchers have employed traditional time series analysis methods in the field, such as autoregressive (AR) [3], moving average (MA), autoregressive moving average (ARMA), and autoregressive integrated moving average (ARIMA) [4]. e application and comparison between above methods can be found in References [5,6,7]
In the past few years, many machine learning algorithms have been successfully applied to solve the reservoir inflow forecasting problems, such as support vector regression (SVR) [11, 12], deep belief network (DBN) [13, 14], as well as some hybrid models [15,16,17]; those models are frequent to inflow prediction
Summary
Power generation is the main source of economic benefits and water is the raw material of production. A three-layered artificial neural network was used to forecast inflow of reservoir for 7 days of head-time [9] It demonstrated that the ANN model has a great generalization ability over 23 dams in the U.S with varying hydrological characteristics. Reference [7] shows a comparison about prediction effect between ARMA, ARIMA, and autoregressive artificial neural network. In the past few years, many machine learning algorithms have been successfully applied to solve the reservoir inflow forecasting problems, such as support vector regression (SVR) [11, 12], deep belief network (DBN) [13, 14], as well as some hybrid models [15,16,17]; those models are frequent to inflow prediction.
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