The development of smart grid improves the em- phasis on family load forecasting. Based on the theory of state transition

a Monte Carlo Markov Chain load forecasting model of single equipment based on the selection of similar days is proposed

and the bottom - up analysis method to obtain the comprehensive load level of a single family is used. For the temperature control equipment

Pearson correlation coefficient is used to study the correlation between the ambient temperature and the operation cycle of the equipment

and the hidden Markov model is used to predict the operation state of the compressor of the temperature control equipment according to the external environment information of the day. With the operation state predicted

the average power in different time periods is calculated to reflect the user load level.The simulation results show that the predicted error of the Monte Carlo Markov chain model based on the similar day selection is about 2% ~8% for the daily load expectation of different equipment

while the predicted accuracy of the hidden Markov model for the temperature control equipment is about 70%.