Electricity consumption prediction method based on cloud computing

Abstract

The invention discloses an electricity consumption prediction method based on cloud computing, and belongs to the technical field of smart power grids, and the method comprises the following steps: S1, defining electricity meter collection nodes as a vertex set of a graph, defining the distance between the electricity meter collection nodes as an edge set of the graph, and constructing a graph convolutional neural network; S2, taking electricity meter time sequence data acquired by the electricity meter acquisition nodes as input of the graph convolutional neural network, and constructing an electricity consumption prediction model; S3, building a Hadoop platform, and carrying out parallel iterative training on the electricity consumption prediction model on the platform by using MapReduce to perfect the electricity consumption prediction model; and S4, using the trained electricity consumption prediction model to predict the electricity consumption at the next time point. The electricity consumption prediction model is established based on the graph convolutional neural network, and the model is trained, so that the electricity consumption is accurately predicted, scheduling of a power grid is facilitated, a distributed storage and calculation mode is adopted for training, the calculation efficiency is improved, and the conversion problem of mass electricity consumption data is solved.

Description

technical field [0001] The invention relates to the technical field of smart grids, in particular to a method for forecasting electricity consumption based on cloud computing. Background technique [0002] Power consumption forecasting has always been an important task in power decision-making in smart grids. Correct data forecasting can help the grid to conduct reasonable resource scheduling, reduce power loss in power transmission, and improve the operating efficiency of the power system. However, with the development of smart grid, the data of electricity consumption is increasing, the data storage capacity and data analysis ability of the traditional single-node data mining algorithm can no longer meet the prediction requirements, and new methods need to be studied to improve the prediction speed of electricity consumption prediction and levels. Contents of the invention [0003] The purpose of the present invention is to provide a cloud computing-based power consumpt...

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Problems solved by technology

[0003] The purpose of the present invention is to provide a cloud computing-based power consumption forecasting method to solve the problem that the above-mentioned single-node data mining cannot meet the forecasting requirements and has low efficiency, which has high forecasting efficiency and accuracy, and is convenient for storing and converting a large amount of data The advantages

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