Power big data oriented microgrid short-period load prediction method

Abstract

A power big data oriented microgrid short-period load prediction method is used for improving prediction precision for a microgrid short-period load. The power big data oriented microgrid short-period load prediction method is characterized by comprising the steps of firstly establishing a microgrid short-period load prediction model based on a kernelized extreme learning machine (KELM); then optimizing a combined parameter (C,sigma) of the kernelized extreme learning machine by means of an improved shuffled frog leaping algorithm (ISFLA), and obtaining an ISFLA-KELM prediction model; and finally predicating the microgrid short-period load by means of the ISFLA-KELM prediction model. According to the power big data oriented microgrid short-period load prediction method, the ISFLA-KELM prediction model is utilized for predicating the microgrid short-period load. An experiment represents a fact that the KELM has relatively high regression forcasting capability. Furthermroe the ISFLA algorithm has high optimization capability and the parameter of the KELM can be optimized, thereby greatly improving prediction precision for the microgrid short-period load.

Description

technical field [0001] The invention relates to a method capable of accurately predicting the short-term load of a microgrid, which belongs to the technical field of power generation. Background technique [0002] A microgrid is a small power generation and distribution system that organically integrates distributed power sources, loads, energy storage devices, converters, and monitoring and protection devices. Short-term load forecasting is an important part of microgrid economic dispatch. The size will directly affect the economy of grid operation. Compared with the large power grid environment, it is more difficult to implement short-term load forecasting for user-side microgrids. In order to improve the accuracy of load forecasting, many scholars have conducted a lot of research and proposed a series of forecasting methods, such as the load forecasting method based on sparse heteroscedastic Gaussian process; the method of using two-layer structure for microgrid load for...

AI Technical Summary

Problems solved by technology

However, these methods still have the problem of insufficient prediction accuracy, and they all run on matlab in a stand-alone environment, making it difficult to face the challenges brought by power big data

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