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%).

翻译：利用人工神经网络预测热需求吸引了越来越多的注意力。天气条件,如环境温度、风速和直接太阳辐照等,已被确定为关键输入参数。为了进一步提高模型的准确性,了解不同参数的影响非常重要。根据Elman神经网络(ENN),本文件调查直接太阳辐照和风速对预测地区供暖网络热需求的影响。结果显示,包括风速在内的风速通常会导致总体平均绝对误差(MAPE)(6.43%)低于直接太阳辐照(6.47%);包括直接太阳辐照(71.8%),但直接太阳辐照(71.8%)可以达到比包括风速(81.53%)更低的最大绝对偏差(71.8%)。此外,即使包括风速和直接太阳辐照(MAPE=6.35%)都显示最佳的总体性(MAPE=6.35%)。