The load characteristics of regional power grids are easily affected by the environmental temperature

which often results in large deviations in the load identification results. The temperature-sensitive load identification method based on residual convolutional neural network is studied to effectively improve the accuracy of load identification. Firstly

the benchmark load comparison method is used to construct the benchmark daily load curve of commercial enterprises. Secondly

the Pearson correlation coefficient method is used to screen out temperature-sensitive loads with strong temperature correlation

and a polynomial regression model is used to further analyze the temperature -sensitive load and the law of real-time temperature changes quantifies the degree of influence of temperature factors. Finally

for temperature - sensitive loads

a polynomial regression model coefficient of load and temperature is used to construct a dynamic temperature-sensitive load feature library as the input of the identification model. Comparing the load identification results based on the residual convolutional neural network with the traditional convolutional neural network load identification results

the identification accuracy of the former is greatly improved.