Static to dynamic data mining to improve future predictions of anthropogenic heat flux

目前对人为热通量的模拟是基于代表过去的数据集，而不是城市能源系统的当前或未来状态。然而，这些数据集中的许多都会定期更新，从而有可能更新能源系统的模型表示，以更好地捕捉这些系统对热通量的影响。例如，卫星数据可用来突出可能与特定时间和地点的人为活动有关的能源“热点”。将这样的数据集纳入建模过程中，可以在将能源系统行为与人为热流联系起来方面提供更高的准确性。通过将动态数据挖掘纳入建模过程，可以检测和调查行为变化和系统变化的早期趋势，以便更好地预测当前和未来的能源使用情况。

Current modelling of anthropogenic heat fluxes are based on data sets that are representative of the past, rather than the current or future state of city energy systems. However, many of these data sets are regularly updated making it possible to update model representations of energy systems to better capture the influence of those systems on heat fluxes. As an example, satellite data can be used to highlight energy `hotspots' that can be related to anthropogenic activity at a given time and location. Incorporating data sets such as these into the modelling process can give greater accuracy in relating energy system behaviour to anthropogenic heat flux. Through incorporating dynamic data mining into the modelling process, early trends in behaviour change and system changes can be detected and investigated for better projection of current and future energy use.