With the gradual advancement of electricity market reform

demand response will play an increasingly important role in future new power systems. Considering the problems of cumbersome process of DR potential calculation and lack of detailed data on user electricity consumption process

a group users DR potential prediction and classification method based on historical load

temperature

and electricity price data is proposed. First

data processing and information extraction are carried out on the user’s daily electricity load curves.Three characteristics

monthly load regularity

daily load fluctuation and peak-valley consistency

are calculated

which form an index system of physical regulative potential evaluation. Further

nonlinear au-to-regressive model with exogenous inputs neural network is applied to the prediction of group users’daily load and DR potential. Finally

taking industrial users as examples

Meanshift algorithm is used to partition user clusters

and DR regulation power of general component manufacturing industry is predicted. By comparing and analyzing with actual data

the effectiveness of the proposed method in this paper is verified.