A Distributed Load Forecasting Algorithm Based on Cloud Computing and Extreme Learning Machine

To improve the accuracy of load forecasting and cope with the challenge of single computer's insufficient computing resource under massive and high-dimension data due to power grid intellectualization,a short-term distributed load forecasting model based on cloud computing and extreme learning machine is proposed.According to the features of load data the online sequential optimization of load forecasting algorithm based on extreme learning machine is performed;leading in the distributed and multi-agent thinking the accuracy of load forecasting algorithm is improved;adopting the MapReduce programming framework of cloud computing the parallelization of the model of the proposed algorithm is carried out to enhance its ability of dealing with massive and high-dimension data.The analysis of example based on real load data provided by EUNITE is conducted and corresponding experiments are done by 32-node cloud computing cluster,and experimental results show that the load forecasting accuracy by the proposed model is higher than the accuracy by traditional vector regression forecasting algorithm and the accuracy by generalized neural network based load forecasting algorithm,besides,the proposed forecasting algorithm possesses excellent parallel performance.