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A distributed computing framework for wind speed big data forecasting on Apache Spark
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DOI：10.1016/j.seta.2019.100582.png)
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Abstract
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The randomness of the wind speed leads to the intermittency of wind power, which is a challenge to realize wind power energy as reliable and renewable power. The prediction of wind speed time series can promote the use of wind energy. However, the traditional stand-alone methods are unable to meet the requirements of wind speed big data environments. In the study, a hybrid distributed computing framework on Apache Spark is applied for wind speed big data forecasting. Using the distributed computing strategy, the framework can divide the wind speed big data into RDD groups and operate them in parallel. In the framework, a modified wind speed extreme learning machine predictor is built using the Distributed Computing Process strategy, enhanced by the data decomposition and result reconstruction components on Apache Spark. The experimental results indicate that the proposed distributed computing framework on Spark can forecast wind speed big data in multi-step accurately. Besides, the effectiveness of different components in the framework is verified. It is also proved that the proposed distributed computing framework has a faster computation speed when processing big data, compared to the stand-alone method.
Keywords:
Wind speed big data forecasting
Distributed computing
Apache Spark
Multi-step forecasting
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