Improved Multiple Scale Nested Long Short-term Memory Neural Network Load Forecasting Method for Virtual Power Plants
The virtual power plant load is affected by a variety of factors and exhibits highly nonlinear and dynamic characteris-tics,which make it difficult to forecast.Existing forecasting methods have many limitations,such as insufficient extraction of load time series features and incomplete consideration of load influencing factors,which make it difficult to further improve the forecasting accuracy.To this end,this paper proposes an improved virtual power plant load forecasting method.This method is based on the multiple scale nested long short-term memory(MSNLSTM)neural network and constructs a multi-level long short-term memory network to extract the patterns of load sequences at different time scales,so as to deeply learn the inherent periodicity and correlation characteristics of the load.At the same time,external factor datas are introduced as the input of the network to enhance the modelling ability of load influencing factors.Experiments results show that compared with a single long short-term memory network and traditional forecasting methods,the improved model can improve the accuracy of day-ahead and week-ahead load forecasting.
万方数据
维普
