New energy consumption electric quantity prediction method based on long-term and short-term memory neural network

Abstract

The invention relates to the field of electric power systems, and discloses a new energy consumption electric quantity prediction method based on a long-term and short-term memory neural network, andthe method comprises the steps: A), collecting historical statistical data related to new energy consumption electric quantity in an operation electric power system of a to-be-predicted region; b) analyzing the bad data, and carrying out data preprocessing to obtain sample data; c) constructing a long-term and short-term memory neural network model; and D) predicting the new energy consumed electric quantity by using the long-term and short-term memory neural network model to obtain a predicted value of the new energy consumed electric quantity. According to the method, effective historical characteristics are mined under the condition of completely utilizing historical data, and parameter optimization is carried out on the long-short-term memory neural network model by utilizing a long-short-term memory neural network method, so that accurate prediction of the new energy consumption electric quantity in the operation electric power system of the region to be predicted is realized.

Description

technical field [0001] The invention relates to the field of electric power systems, in particular to a method for predicting electricity consumed by new energy sources based on long-short-term memory neural networks. Background technique [0002] At present, with the rapid development of technology, my country has become the country with the largest new energy industry in the world. However, due to many uncertain factors such as intermittency and volatility in new energy power generation, the consumption of new energy has been facing a severe situation. In order to reduce the occurrence of abandoned wind and light, multi-party coordination and multiple measures are required. Among them, accurate new energy consumption forecast can evaluate the future consumption level of new energy in advance, provide an important basis for power system planning, scheduling and management, and help to ensure the stable operation of the power grid, reduce power generation costs, reduce wind...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of large prediction errors of traditional prediction models and the inability to effectively use historical information in the face of various uncertain factors, and to provide a prediction method for new energy consumption electricity based on long-term and short-term memory neural networks. In the case of historical data, the present invention can mine effective historical features, use the method of long-term and short-term memory neural network, and optimize the parameters of the long-term and short-term memory neural network model, so as to realize the accuracy of medium and long-term consumption of new energy in the area to be predicted. predict

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