Driven by the“dual carbon”goal

significant changes will occur in social and economic development

energy production

and consumption structure

leading to new characteristics in factors and trends affecting electricity de-mand forecasting. The power industry is a key area to ensure the practical achievement of the“dual carbon”goals

so research needs to be conducted on electricity demand forecasting method under the new situation. Logarithmic mean index method（LMDI）and path analysis method are used to study the influencing factors of electricity demand under the background of“dual carbon”

and extracts three carbon emission related influencing factors of electricity demand：electrification rate

clean energy generation ratio

and energy intensity. A method for predicting electricity demand based on fuzzy autoregressive distributed lag model is proposed. The regression coefficients are fuzzified on the basis of the autoregressive distributed lag model considering policy lag effects. By establishing a mini-mum fuzziness optimization model

the regression parameters with the least uncertainty are obtained

which improved the accuracy of long-term electricity demand prediction under the background of “dual carbon”. Based on the historical data of China’s socio-economy and electricity demand

and in combination with the national policy objectives

the electricity demand in China under different low-carbon paths has been predicted to verify the feasibility and effectiveness of the power demand forecasting method proposed in this article.