METHOD FOR PERFORMING PROBABILISTIC MODELING OF LARGE-SCALE RENEWABLE-ENERGY DATA

摘要

Disclosed is a method for performing rapid probabilistic modeling of large-scale renewable-energy data, containing the following processes: on the basis of Spark and a Hadoop distributed file system (HDFS), building a distributed parallel framework for rapid modeling of new energy, causing said framework to be compatible with existing renewable-energy storage systems; using the fault tolerance feature of a resilient distributed dataset (RDD) and features based on memory computation, constructing a Wakeby probability distribution model to estimate an RDD-based Wakeby probability distribution model.