Cascade hydropower station generating capacity prediction method based on long-short-term memory network

Abstract

The invention discloses a cascade hydropower station generating capacity prediction method based on a long-term and short-term memory network. The method comprises the steps of performing stability test on a hydropower station generating capacity time sequence; performing correlation test on the generating capacity time sequence; converting the generating capacity time sequence data into supervised learning data; establishing a generating capacity prediction model based on the long-term and short-term memory network; performing integrated empirical mode decomposition on the generating capacitydata to obtain a training set and a test set; training the generating capacity prediction model by using the training set, and performing model hyper-parameter optimization by using an improved discrete differential evolution algorithm to obtain optimal model parameters; and predicting the generating capacity of the cascade hydropower station by adopting the generating capacity prediction model.The method is suitable for predicting the generating capacity of large and medium-sized cascade power stations; the power generation quantity prediction model based on the LSTM neural network has moreadvantages for predicting the power generation quantity of the power station adjusted for many years, and the fitting precision of the model is improved through hyper-parameter optimization of the model.

Description

technical field [0001] The invention belongs to the field of hydropower energy optimization, and in particular relates to a method for predicting power generation of cascade hydropower stations based on long-short-term memory networks. Background technique [0002] With the rapid development of my country's power grid construction and the continuous expansion of the grid scale, the demand for safe and stable economic operation of the power grid is getting higher and higher, and the contradiction between the growing demand for electricity and peak-shaving capabilities has brought great challenges to hydropower operations. . However, as hydropower is the main peak-shaving power source in my country's power grid, the output process needs to fully consider the peak-shaving requirements of each power grid, which puts forward higher requirements for mining hydropower generation forecasting based on multiple constraints. The power generation forecast of the hydropower station is ba...

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Problems solved by technology

Existing research methods rarely mention the prediction of power generation of large and medium-sized cascade power stations, which brings difficulties to the preparation of dispatching plans, and the current research has not considered the long-term dependence of power generation time series

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