Short term power load forecasting can evaluate the overall power load changes in a certain region,which is crucial for the safe and stable operation of the power system,and meteorological factors also have a profound impact.This article proposes a model that combines Empirical Mode Decomposition(EMD)and Bidirectional Long Short Term Memo-ry Network(BiLSTM).Firstly,the correlation between meteorological factors and power system consumption is ana-lyzed,and relevant meteorological factors and historical load data are selected as input feature sets.The EMD algorithm is used to decompose the historical power load data with strong randomness into a finite number of intrinsic mode function components and trend components with distinct features.Then,along with meteorological factors,it is input into BiLSTM to deeply mine historical data and train the model.Finally,predict and overlay the predicted values for each component da-ta separately.Taking the power load data of a certain area as an actual calculation example,the experimental results show that the fitting degree of the prediction model using this method can reach 97％,and it has a good prediction effect.Com-pared to the prediction results of LSTM network and BiLSTM network,its prediction curve is closer to historical load da-ta,especially for sudden changes in power load trends,which greatly improves its prediction accuracy.
short term power load forecastingempirical mode decompositionbidirectional long-term and short-term memory networktime series
NETL
NSTL
万方数据
