Short-term load forecasting method based on distributed bp neural network based on hadoop architecture

Abstract

The invention discloses a Hadoop framework-based short-term load prediction method for a distributed BP (Back Propagation) neural network. The method specifically comprises the steps of obtaining an initial load data set; dividing the load data set into small data sets and storing the small data sets in data nodes of a distributed file system; initializing BP neural network parameters and uploading a parameter set into the distributed file system; training the BP neural network according to a current load sample, and obtaining correction values of a weight and a threshold of the BP neural network in the current data set; performing statistics on sum of weight and threshold parameters of all layers and between the layers of the network according to a key value of a key value pair; judging whether the convergence precision or the maximum iterative frequency is reached or not in a current iterative task, and if yes, establishing a distributed BP neural network model, or otherwise, performing correction of the weight and threshold parameters of the network; and inputting prediction day data and obtaining load power data of a prediction day. According to the method, the load prediction speed is increased and the requirements of load prediction precision are met.

Description

technical field [0001] The invention relates to the technical field of short-term load forecasting application in the scene of combining power system and big data, and in particular to a short-term load forecasting method based on a distributed BP neural network of Hadoop architecture. Background technique [0002] Power load forecasting is of great significance in ensuring power system planning, reliable and economical operation. With the continuous progress of modern technology and the deepening of smart grid, the theory and technology of load forecasting have been greatly developed. Over the years, power load forecasting methods and theories have emerged continuously. Technologies such as time series method, fuzzy theory, regression analysis method, regression support vector machine, Bayesian and neural network have provided good technical support for power load forecasting. [0003] Existing algorithms still have certain limitations. Time series method: It has high acc...

AI Technical Summary

Problems solved by technology

However, since the algorithm will conduct a round of training for each load input and output sequence to calculate the weights and threshold corrections of each layer of the network, when the amount of data is very large, the amount of calculation will become very large, and the single-machine serial training time will likely reach several hours, or even larger

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