Two-level deep learning ensemble model for forecasting hydroelectricity production

Abstract

Due to its cheapest cost of production, flexibility, and very low indirect emissions, hydroelectricity is of utmost importance to the economy. Its production is challenging due to the stochastic nature of water and other environmental factors. Hence, forecasting its production is very important to the power planner. In this work, a novel two-level deep learning ensemble based on hyperparameter variations, random initialization, long short-term memory (LSTM), Convolutional one-dimensional neural network (Conv1D), and the dense neural network is proposed for short-term hydropower forecasting. In level, I, ensembles of LSTM, Conv1D, and dense network are used to forecast the components decomposed by Seasonal Trend Decomposition using Loess. These ensembles are built using multiple loss functions and making use of random initialization of the deep learning models. Integrating the median of these ensembles to obtain the final forecast constitutes level II. The model was validated with 61368 hourly univariate data sets from the largest hydropower plant in Cameroon, the Songloulou hydropower plant. The result reveals that the proposed model consistently outperforms all the other deep learning models base on evaluation criteria. The LSTM model was the best model among the individual deep models with a Mean square error (MSE), Mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) of 533.557 (MWh)2, 15.690 MWh, and 0.646 respectively while the proposed two-level ensemble model had an MSE of 448.711 (MWh)2, MAE of 14.938 MWh, and NSE of 0.702 respectively.

Keywords

• Hydropower generation forecasting
• Univariate time series
• Two-level ensemble model
• Deep learning model
• Time series decomposition