电力系统自动化2017，Vol.41Issue(21)：25-32,155,9.DOI:10.7500/AEPS20170119007
基于混沌粒子群—高斯过程回归的饱和负荷概率预测模型
Forecasting Model of Saturated Load Based on Chaotic Particle Swarm and Optimization-Gaussian Process Regression
摘要
Abstract
Saturated load forecasting could effectively estimate future direction and final scale of the regional power grid, providing guidance for planning and mid/long-term transactions of the power market.Firstly,a probabilistic forecasting model based on Gaussian process regression (GPR) is adopted for saturated load forecasting,aiming at its characteristic of strong uncertainty and large time span.Secondly,the optimal solution of model hyper-parameters with the objective of minimizing the sum of squares due to errors (SSE) is realized by a modified chaotic particle swarm optimization (MCPSO) presented.In consideration of the randomness of the factors influencing the saturated load,a saturated load forecasting model based on modified chaotic particle swarm optimization-Gaussian process regression is proposed.Thirdly,in multi-scenarios using the above model while taking saturation criterion into account could forecast the saturated load and obtain multi-scenario scale and time-point.Finally,case studies show that this model not only has high precision,but also enhances the elasticity of forecasting results.关键词
饱和负荷/负荷预测/高斯过程回归/混沌粒子群优化/概率预测Key words
saturated load/load forecasting/Gaussian process regression (GPR)/chaotic particle swarm optimization/probabilistic forecasting引用本文复制引用
彭虹桥,顾洁,胡玉,宋柄兵..基于混沌粒子群—高斯过程回归的饱和负荷概率预测模型[J].电力系统自动化,2017,41(21):25-32,155,9.基金项目
国家重点研发计划资助项目(2016YFB0900101).This work is supported by National Key Research and Development Program of China(No.2016YFB0900101). （2016YFB0900101）
