Power load forecasting method based on long-short term neural network

Abstract

The invention relates to a power load forecasting method based on a long-short term neural network, which comprises the steps of S1, performing statistical processing on historical load information, historical temperature information and holiday information of a power grid in a forecast place, dividing the historical load information in every seven days of the power grid in the forecast place intoa group, forecasting the load of the forecast day by inputting 14 pieces of neuron information altogether including the historical load information and holiday information of six days before the forecast day and temperature level information and holiday information of the forecast day so as to serve as training samples, wherein the 14 pieces or neuron information and the training samples constitute a training set; S2, substituting the training set into the long-short term neural network to sequentially perform forward calculation and back propagation training, enabling the error of the training set to be reduced below an error threshold, and obtaining a trained long-short term neural network; and S3, inputting neuron information required by the forecast day into the trained long-short term neural network so as to perform load forecasting on each time point of the forecast day.

Description

technical field [0001] The invention belongs to the technical field of electric power information, and relates to a long-short-time neural network-based power load forecasting method. Background technique [0002] Load forecasting is the basis for power system development planning, fuel planning, and power generation planning. Power system load forecasting plays a very important role in the safe, economical and reliable operation of the power system. Among them, short-term load forecasting is the basis for power system dispatching and management departments to formulate start-up and shutdown plans and online safety analysis, and is also the basis for realizing power plan management in the power market. The neural network has a strong nonlinear fitting ability, and can comprehensively consider various factors that affect the load, such as weather conditions, date types, etc., so the neural network method is widely used in power system load forecasting, but if various influen...

AI Technical Summary

Problems solved by technology

In recent years, people have used the fuzzy rough set method to obtain the input parameters of neural network load forecasting, which has improved the prediction accuracy, but the fuzzy rough set method has a large amount of calculation and takes a long time for attribute reduction.

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