  Using artificial neural network for the prediction of heat demand has
attracted more and more attention. Weather conditions, such as ambient
temperature, wind speed and direct solar irradiance, have been identified as
key input parameters. In order to further improve the model accuracy, it is of
great importance to understand the influence of different parameters. Based on
an Elman neural network (ENN), this paper investigates the impact of direct
solar irradiance and wind speed on predicting the heat demand of a district
heating network. Results show that including wind speed can generally result in
a lower overall mean absolute percentage error (MAPE) (6.43%) than including
direct solar irradiance (6.47%); while including direct solar irradiance can
achieve a lower maximum absolute deviation (71.8%) than including wind speed
(81.53%). In addition, even though including both wind speed and direct solar
irradiance shows the best overall performance (MAPE=6.35%).
