Abstract: Considering that meteorological factors have a great influence on the accuracy of short-term power load forecasting,Bayesian optimization and long and short-term memory neural network（BO-LSTM）combined model is proposed. The global optimal hyperparameters is obtained by Bayesian optimization algorithm and then five kinds of meteorological factors（daily maximum temperature,daily minimum temperature,daily average temperature,daily average relative humidity,rainfall）and actual power load data are selected as input features to train optimized LSTM neural network. Through the prediction analysis of daily load data in a certain area and comparative analysis with different methods,it is proved that BO-LSTM model has high prediction accuracy and it can be used as a reliable short-term power load forecasting tool.