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In order to understand the change trend of heating energy consumption in green buildings in real time and improve the prediction effect of energy consumption, a short-term prediction method of heating energy consumption in green buildings based on time series autoregression model is designed. The enhanced Dickie Fowler test is used to test the stability of the historical heating energy consumption time series of green buildings; The non-stationary historical energy consumption time series is processed by difference stationarization to obtain a stable historical energy consumption time series; Add a moving average model to the time series autoregressive model, and consider the temperature influence factors of energy consumption to establish a time series autoregressive moving average model; The order of the model is determined by Akchi information criterion, and the model parameters are determined by particle swarm optimization algorithm; After the model order and parameters are determined, stable historical energy consumption time series are input into the model, and short-term prediction value of heating energy consumption is output. The experiment shows that this method can accurately predict the short-term heating energy consumption of different types of green buildings; The short-term heating energy consumption prediction error of this method is small when the infiltration amount of green buildings is different; At different outdoor temperatures, this method has a high determinable coefficient of short-term heating energy consumption prediction, that is, the prediction accuracy is high.