Multi-weather Feature-based Short-term Load Prediction Model
Neural network prediction has gradually become the prevailing hotspot in the field of electric load prediction in recent years,but there is still a lack of mining the dependency relationship among weather features,and no scientific guid-ance for selecting predictive time scale.The present work made a preliminary attempt to establish a multi-weather feature-based short-term load prediction model in view of the characteristics of large fluctuations and strong stochasticity of elec-tric load.The model was designed to select the optimal prediction scale of 7 days and employ a modified RNN-based LSTM deep neural network for the deep mining of the relationship between load data and various weather characteristic data.In an experimental predictive verification using actual daily data of weather and load carried out on a time scale of 7 days,the proposed model achieved a prediction accuracy as high as 94.131％.
electric load forecastingweather characteristicdeep miningoptimal predictive scale
万方数据
维普
