In order to reduce the impact of weather factors on short-term power load forecasting and improve the forecasting accuracy of the model

a short-term load forecasting model based on weather classification and convolutional neural networks is proposed. Firstly

through the preliminary classification of weather types

the model divides the original data sample set into four types such as sunny

cloudy

cloudy and rainy days. Secondly

in order to identify similar meteorological conditions

the correlation coefficient and k- means clustering method are used to find the meteorological factors that have the greatest impact on the new load output

cluster them

and select high similarity data samples. Then

according to the result of feature selection

the neural network input data set is constructed. Finally

the data set is input to the convolutional neural network for training and prediction. The proposed model has higher prediction accuracy through verification and analysis of numerical examples.