Production capacity prediction of hydropower industries for energy optimization: Evidence based on novel extreme learning machine integrating Monte Carlo

Abstract

As the basic industry of national economic development, the electric power industry is closely associated with the overall economic development and social progress. Hydropower, as a power generation form, has fewer data samples than thermal power generation, which leads to a difficult problem to establish an accurate production and energy prediction model. Therefore, a novel production capacity prediction model using extreme learning machine based on Monte Carlo algorithm is presented for energy optimization and saving. Through using the Monte Carlo algorithm, the small sample data can be expanded. Then, the expansion of the small sample data is utilized as the training set and the testing set for the extreme learning machine to predict the production capacity and optimize the energy configuration. Finally, the proposed method is used to predict the production of a hydropower plant for improving the energy efficiency. Compared with the traditional extreme learning machine, the correctness and the applicability of the proposed method are proved. Moreover, the energy optimal configuration of the hydropower industry production can improve the energy efficiency and save the energy of hydropower industrial processes.