ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method

Abstract

The invention discloses an ARIMA (Autoregressive integrated moving average) model load prediction based parallelization computing method. The method comprises the following steps: partitioning original electrical load data to form time series data according to the electrical load characteristic and strong relevance of data at the same moments every day; newly constructing a plurality of threads, synchronizing all threads, setting a counter shared globally, executing run () function to parallelize processed and partitioned data by use of the newly constructed thread, judging whether the counter in run () is locked, if so, queuing up; if not, locking the counter in the run (), and counting by adding 1; predicting by calling ARIMA according to data in the time series data, corresponding to the numerical value of the counter; and acquiring the predicted data, unlocking, repeating the steps until data in the time sequence data is completely processed. The method can remarkably improve the computing efficiency.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a parallel calculation method based on ARIMA model load forecasting. Background technique [0002] Electric load data presents obvious periodicity as a time series, and the ARIMA model (autoregressive integral moving average model) has a significant predictive effect on time series with obvious periodic characteristics. [0003] In the prior art, the ARIMA model is usually used to analyze and predict the power load data. However, due to the complexity of the model, a large amount of calculation and a long time-consuming, it is urgently needed to be improved in engineering applications. Contents of the invention [0004] The purpose of the present invention is to provide a parallel calculation method based on ARIMA model load forecasting, which significantly improves the calculation efficiency. [0005] The purpose of the present invention is achieved through the foll...

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Problems solved by technology

[0003] In the prior art, the ARIMA model is usually used to analyze and predict the power load data. However, due to the complexity of the model, the large amount of calculation and the long time consumption, it is urgent to improve it in engineering applications.

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