Considering the difference,periodicity,fluctuation and variability of load data,which are vulnerable to the impact of cyclical load,holiday effect and meteorological factors on load,a multi index adaptive forecasting model for daily power load of low-carbon residential buildings is designed. The fuzzy C-means clustering algorithm is used to process the historical data of periodic load,holiday effect and meteorological factors;By combining short and long term memory with gated loop unit,a multi-layer bidirectional recurrent neural network model is established;The grey wolf algorithm is used to optimize the network model parameters,and a mature multi index adaptive forecasting model of daily power load is established;Input the characteristic data of the three indicators in the mature model,and output the daily power load forecast results. The experiment shows that the model can effectively cluster historical data and obtain multi index feature data;The model can accurately and adaptively predict the daily power load of low carbon residential buildings;After applying the model,the load rate and carbon emission reduction benefits can be improved.
NETL
NSTL
万方数据
