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Short-term load prediction method under big data environment

A short-term load forecasting, big data technology, applied in forecasting, data processing applications, neural learning methods, etc., can solve the problems of large computational load, slow convergence speed, long single-computer operation time, etc., to improve the accuracy of load forecasting, increase load The effect of predicting speed

Inactive Publication Date: 2018-06-19
NANJING INST OF TECH
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AI Technical Summary

Problems solved by technology

The traditional BP neural network learning process has a slow convergence speed, is easy to fall into local minimum points, and has poor robustness.
At the same time, with the vigorous development of smart grids, massive amounts of data emerge in links such as power generation, transmission, and scheduling. In this case, due to the need to find neighbors for each test point, the amount of calculation is very large, and the time for single-machine calculation is very long

Method used

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  • Short-term load prediction method under big data environment
  • Short-term load prediction method under big data environment
  • Short-term load prediction method under big data environment

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Embodiment Construction

[0044] The technical solution of the present invention will be further introduced below in conjunction with specific implementation methods and accompanying drawings.

[0045] This specific embodiment discloses a short-term load forecasting method in a big data environment, including the following steps:

[0046] S1: Get the historical load data set.

[0047] S2: Use the Hadoop-based MapReduce data processing system to split the load data set into small data sets and store them in each data node of the distributed file system.

[0048] S3: build as figure 1 As shown in the BP neural network, initialize the parameters of the BP neural network.

[0049] S4: Use the particle swarm optimization algorithm to optimize the initial parameters of the BP neural network to obtain weights and thresholds.

[0050] S5: The workflow of Hadoop is as follows figure 2 As shown, the weight and threshold in S4 are first stored in the distributed file system, and the data is split according t...

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Abstract

The present invention discloses a short-term load prediction method under a big data environment. The short-term load prediction method adopts a Hadoop architecture to carry out the distributed storage and processing on the mass data, thereby improving the load prediction speed. The short-term load prediction method of the present invention uses an improved particle swarm optimization algorithm tooptimize a conventional BP neural network, thereby improving the load prediction precision.

Description

technical field [0001] The invention relates to short-term load forecasting technology, in particular to a short-term load forecasting method in a big data environment. Background technique [0002] The level of power system load forecasting has become one of the symbols to measure the modernization of power system management. Short-term load forecasting is an important part of electric load forecasting. With the continuous reform of the electricity market, the accuracy of short-term load forecasting of the power system directly affects the economic benefits of the power grid and power plants. [0003] Existing forecasting methods still have certain limitations. The traditional BP neural network learning process has a slow convergence speed, is easy to fall into local minimum points, and has poor robustness. At the same time, with the vigorous development of smart grids, massive amounts of data are emerging in links such as power generation, transmission, and scheduling. ...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08G06N3/00
CPCG06N3/006G06N3/084G06Q10/04G06Q50/06
Inventor 李先允朱一骄王书征唐昕杰王建宇
Owner NANJING INST OF TECH
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