Distributed short-term load forecasting algorithm based on Spark and IPPSO_LSSVM
Wang Baoy · Dianli zidonghua shebei · 2016
Aiming at the insufficient resource of single computer,a short-term load forecasting model based on LSSVM(Least Squares Support Vector Machine) optimized by IPPSO(Improved Parallel Particle Swarm Optimization) algorithm is proposed to improve the accuracy of load forecasting. A Spark-on-YARN memory computing platform is introduced and the IPPSO is operated there to optimize the uncertain parameters of LSSVM,which are then applied in the load forecasting. The parallel and distributed computation is adopted to improve the accuracy of forecasting algorithm and the capability of massive high-dimensional data processing. Experiment and analysis are carried out with the actual load data provided by EUNITE on an 8-bus cloud computing platform and results show that,the proposed algorithm has better accuracy than the generalized traditional neural network algorithm and better efficiency than the MR-OSELM-WA(Map Reduce-Online Sequential Extreme Learning Machine-Weighted Averaged) algorithm.