Hourly energy consumption profiles are of primary interest for measures to apply to the dynamics of the energy system. Indeed, during the planning phase, the required data availability and their quality is essential for a successful scenarios’ projection. As a matter of fact, the resolution of available data is not the requested one, especially in the field of their hourly distribution when the objective function is the production-demand matching for effective renewables integration. To fill this gap, there are several data analysis techniques but most of them require strong statistical skills and proper size of the original database. Referring to the built environment data, the monthly energy bills are the most common and easy to find source of data. This is why the authors in this paper propose, test and validate an expeditious mathematical method to extract the building energy demand on an hourly basis. A benchmark hourly profile is considered for a specific type of building, in this case an office one. The benchmark profile is used to normalize the consumption extracted from the 3 tariffs the bill is divided into, accounting for weekdays, Saturdays and Sundays. The calibration is carried out together with a sensitivity analysis of on-site solar electricity production. The method gives a predicted result with an average 25% MAPE and a 32% cvRMSE during one year of hourly profile reconstruction when compared with the measured data given by the Distributor System Operator (DSO).

每小时能耗概况是适用于能源系统动力学的措施的主要关注点。确实，在计划阶段，所需的数据可用性及其质量对于成功预测方案至关重要。事实上，解决可用数据并不是要求的，尤其是在每小时分配领域中，当目标函数是有效进行可再生能源整合的生产需求匹配时。为了填补这一空白，有几种数据分析技术，但是大多数技术需要强大的统计能力和适当大小的原始数据库。参考建筑环境数据，每月电费单是最常见且最容易找到的数据源。这就是为什么本文的作者提出，测试并验证一种快速的数学方法，以每小时提取一次建筑物的能源需求。对于特定类型的建筑物（在本例中为办公室），考虑基准小时配置文件。基准配置文件用于规范化从账单中所划分的3种关税中提取的消费量，计入工作日，周六和周日。校准与现场太阳能发电的灵敏度分析一起进行。与分配系统操作员（DSO）给出的测量数据相比，该方法在一年的每小时轮廓重建过程中提供了平均25％MAPE和32％cvRMSE的预测结果。基准配置文件用于规范化从账单中所划分的3种关税中提取的消费量，计入工作日，周六和周日。校准与现场太阳能发电的灵敏度分析一起进行。与分配系统操作员（DSO）给出的测量数据相比，该方法在一年的每小时轮廓重建期间给出了平均25％MAPE和32％cvRMSE的预测结果。基准配置文件用于规范化从账单中所划分的3种关税中提取的消费量，计入工作日，周六和周日。校准与现场太阳能发电的灵敏度分析一起进行。与分配系统操作员（DSO）给出的测量数据相比，该方法在一年的每小时轮廓重建过程中提供了平均25％MAPE和32％cvRMSE的预测结果。