Impacts of Weather Conditions on District Heat System
This work addresses improving prediction accuracy for district heat systems, which is an incremental contribution to energy management.
The paper investigated the impact of wind speed and direct solar irradiance on predicting heat demand in a district heating system using an Elman neural network, finding that including both parameters yields the best performance with a mean absolute percentage error of 6.35%.
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%).