Impacts of Weather Conditions on District Heat System
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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 direct solar irradiance (6.47 achieve a lower maximum absolute deviation (71.8 (81.53 irradiance shows the best overall performance (MAPE=6.35
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