In District Heating (DH) network, accurate heat demand prediction has been considered as an important part for efficiency improvement and cost saving. In order to improve the prediction accuracy, it is extremely important to study the influence of different factors on heat load forecasting. In this paper, the Nonlinear AutoRegressive with eXogenous input (NARX) neural network models were trained using the datasets with different key factors and used to compare their prediction performance in order to investigate the impact of direct solar radiance and wind speed on heat demand prediction. The experimental results show that direct solar radiance and wind speed are key factors of heat demand prediction. Including wind speed only, the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) of the proposed prediction model are lower than those of direct solar radiation only. Including both wind speed and direct solar radiance shows the best model performance, but it cannot result in a large decrease of MAPE and RMSE.