Residential load prediction method of elman-based neural network

Abstract

The invention discloses a residential load prediction method of an elman-based neural network. The method comprises the following steps: acquiring residential load historical data of last year and corresponding historical weather parameter data; calculating a seasonal index of the residential load of each month; correcting the residential load historical data by using the seasonal index; determining input and output data of a neural network and determining an optimal hidden layer neuron number so as to establish the elman-based neural network; normalizing the corrected residential load historical data and the corresponding historical weather parameter data to further train the established neural network and control the prediction error in a preset range; predicting the residential load by using the trained neural network. The method has the capacity of adapting to time-variant characteristic and seasonal fluctuation of the residential load, the dynamic characteristic of the residential load can be directly predicted and reflected, the prediction accuracy is high, and the method can be widely applied to the charge prediction field of a power system.

Description

technical field [0001] The invention relates to the technical field of power system load forecasting, in particular to a residential load forecasting method based on an elman neural network. Background technique [0002] As an important energy source, electricity plays a pivotal role in daily life and work. With the rapid development of the national economy, the electricity consumption of the whole society and various industries has also increased steadily. Therefore, the trend of electricity consumption It not only affects the production and operation decision-making and economic benefits of power grid operating enterprises, but also affects the trend analysis of social economy. Reasonable power load forecasting is the precondition for power system to dispatch and plan power resources. [0003] Power loads can generally be divided into industrial loads, commercial loads, residential loads, etc. Among them, industrial loads and commercial loads account for a relatively high...

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Problems solved by technology

[0003] Power loads can generally be divided into industrial loads, commercial loads, residential loads, etc. Among them, industrial loads and commercial loads account for a relatively high proportion of power loads. Power grid companies have always paid more attention to this load forecasting, and have successively built load control systems. and electricity consumption information collection system to complete the data collection and load forecasting of industrial and commercial loads; due to the characteristics of scattered distribution and small scale of residential user loads, the method of centralized forecasting has been adopted all the time, that is, the unit of station area or feeder load Forecasting, the disadvantage of this forecasting method is that the accuracy is not high, especially with the increase of household appliances year by year, the popularity of electric bicycles and the gradual promotion of electric vehicles, the electricity load of residential users shows a steady growth trend and obvious seasonality Fluctuations, the disadvantages of residential user load forecasting through centralized forecasting methods are becoming more and more apparent

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