Industrial load accounts for a large proportion of social electricity consumption

and the adjustable load resources are abundant

so it is imperative to analyze its adjustable potential. Due to the large change rate of industrial load and many load tips

it is difficult to predict the adjustable potential in real time. Therefore

the silicon carbide industry of a typical industrial enterprise is selected to establish an adjustable potential deduction model. First

the impact of weather characteristics and electricity price factors on the enterprise load is considered through the Person correlation analysis method. At the same time

the Bi-directional long short-term memory（BiLSTM）prediction model processed by convolutional neural network（CNN）and Attention mechanism is established. The adjustable potential of silicon carbide enterprises is explored by using the model prediction results. In order to verify the effectiveness of this method

this algorithm is significantly superior to other comparison algorithms by establishing different algorithms for comparison and the tunable potential results under different strategies. Meanwhile

the tunable potential results of the three strategies can deepen the power grid’s understanding of the load characteristics of such enterprises.