**Appendix A**


**Table A1.** The electricity price, subsidy for UDDSR participators and gas price.

#### **Table A2.** The equipment operating parameters.


**Table A3.** The operation and maintenance cost of equipment and subsidy parameters.


#### **References**


**Jie Sun <sup>1</sup> , Jiao Wang <sup>1</sup> , Yonghui Sun <sup>1</sup> , Mingxin Xu <sup>1</sup> , Yong Shi <sup>1</sup> , Zifa Liu <sup>2</sup> and Xingya Wen 2,\***


**Abstract:** The accuracy of the electric heating load forecast in a new load has a close relationship with the safety and stability of distribution network in normal operation. It also has enormous implications on the architecture of a distribution network. Firstly, the thermal comfort model of the human body was established to analyze the comfortable body temperature of a main crowd under different temperatures and levels of humidity. Secondly, it analyzed the influence factors of electric heating load, and from the perspective of meteorological factors, it selected the difference between human thermal comfort temperature and actual temperature and humidity by gray correlation analysis. Finally, the attention mechanism was utilized to promote the precision of combined adjunction model, and then the data results of the predicted electric heating load were obtained. In the verification, the measured data of electric heating load in a certain area of eastern Inner Mongolia were used. The results showed that after considering the input vector with most relative factors such as temperature and human thermal comfort, the LSTM network can realize the accurate prediction of the electric heating load.

**Keywords:** electric heating; load forecasting; thermal comfort; attention mechanism; LSTM neural network
