Power Load Forecasting Based on Improved Gray Wolf Optimizer Algorithm and Extreme Learning Machine
The problem of short-term power grid load forecasting is studied,a power grid load forecasting method based on im-proved grey wolf optimizer(IGWO)algorithm and extreme learning machine is proposed.The IGWO is proposed,the self var-ying weight coefficient changes and elite disturbance update strategies are introduced to improve the global optimization ability of IGWO.The density based spatial clustering of application with noise(DBSCAN)algorithm is used for clustering analysis of power load data to minimize the impact of data differences on prediction accuracy.The IGWO optimized ELM model(IGWO-ELM)is used to predict the multi cluster power load data-set.The simulation results show that compared with other prediction methods,the proposed classification IGWO-ELM has higher prediction accuracy.
power load forecastinggray wolf optimizerDBSCANextreme learning machineprediction accuracy
万方数据
维普
