电力系统保护与控制2017，Vol.45Issue(7)：35-42,8.DOI:10.7667/PSPC161188
基于并行膜计算的短期电力负荷组合预测
Short-term power load combination forecasting based on parallel membrane computing
白杨 1赵冠 1窦金延 1黄国强 1闫敏 1李逐云 2雷霞 2刘增庆2
作者信息
- 1. 国网山东省电力公司聊城供电公司,山东 聊城 252000
- 2. 流体及动力机械教育部重点实验室(西华大学),四川 成都 610039
- 折叠
摘要
Abstract
A method of parallel membrane computing (PMC) is proposed to solve the combination of short-term load forecasting. The linear regression model, trend extrapolation model, improved gray model and support vector machines with particle swarm optimization parameters are used to forecast load concurrently in different basic membranes of membrane system, and the prediction results are all output to the surface membrane. In the surface membrane, the ultimate results are got by combined optimization, which is to minimize the square value of geometric mean of above prediction values minus weighted combination result. The weighted coefficient is time-interval optimized by the improved particle swarm method. In addition, historical data has been improved through moving average processing before making the prediction, and can be selected through system clustering method. Parallel membrane computing can greatly improve composition prediction speed. The method that geometric mean of various prediction results replaces real data in objective function has more practicability. Finally, the simulation results show the rationality and effectiveness of the proposed method.关键词
组合预测/并行膜计算(PMC)/系统聚类/改进粒子群算法/几何平均数Key words
combined forecast/parallel membrane computing (PMC)/clustering system/improved particle swarm op-timization/geometric mean引用本文复制引用
白杨,赵冠,窦金延,黄国强,闫敏,李逐云,雷霞,刘增庆..基于并行膜计算的短期电力负荷组合预测[J].电力系统保护与控制,2017,45(7):35-42,8.
