Short Term Load Forecasting Based on ICEEMDAN-IWOA-BiLSTM Hybrid Algorithm Model
Power load has the characteristics of uncertainty,randomness and volatility,which makes it difficult to achieve accurate prediction.Therefore,a bi-directional long short-term memory network prediction model based on improved whale optimization complete ensemble EMD with adaptive noise algorithm and improved whale algorithm optimization was proposed.Firstly,the load data of a selected Australian power grid was preprocessed;secondly,the empirical mode decomposition method was used to decompose the load data into a series of subsequence;then,the improved whale algorithm was used to optimize the parameters of the bi-directional long short-term memory network;finally,the decomposed data of each component was input into the optimization model for prediction.The results show that the proposed algorithm achieves accurate prediction of power load,achieving better prediction performance than other single benchmark models and most combination models,and has certain applicability and application value.
electric loadload forecastingempirical modal decomposition algorithmimproved whale optimization algorithmbi-directional long short-term memory network
万方数据
维普
