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Long-term forecasting of hourly electricity load: Identification of consumption profiles and segmentation of customers

机译:每小时电力负荷的长期预测:识别消耗曲线并细分客户

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Data for aggregated hourly electricity demand shows systematic variations over the day, week, and seasons, and forecasting of aggregated hourly electricity load has been the subject of many studies. With hourly metering of individual customers, data for individual consumption profiles is available. Using this data and analysing the case of Denmark, we show that consumption profiles for categories of customers are equally systematic but very different for distinct categories, that is, distinct categories of customers contribute differently to the aggregated electricity load profile. Therefore, to model and forecast long-term changes in the aggregated electricity load profile, we identify profiles for different categories of customers and link these to projections of the aggregated annual consumption by categories of customers. Long-term projection of the aggregated load is important for future energy system planning, and the hourly load profile is an important input to energy system models that serves this purpose. In particular, these models often assume an unchanged hourly load profile (although the level may change). In contrast, our model suggests that the hourly load profile also changes as the shares of consumption by categories of customers change and new consumption technologies such as electrical vehicles and (for Denmark in particular) individual heat pumps are introduced.
机译:每小时总用电量的数据显示出白天,每周和每个季节的系统变化,并且每小时总用电量的预测已成为许多研究的主题。通过每小时对单个客户进行计量,可以获取单个消费配置文件的数据。使用此数据并分析丹麦的案例,我们显示,不同类别的客户的用电情况是同样系统的,但不同类别的情况却大不相同,也就是说,不同类别的客户对总电力负荷情况的贡献不同。因此,为了对总的电力负荷状况进行长期建模和预测,我们确定了不同类别客户的状况,并将其与按客户类别划分的年度总消费量预测联系在一起。总负荷的长期预测对于未来的能源系统规划很重要,每小时负荷曲线是为此目的而设计的能源系统模型的重要输入。特别是,这些模型通常会假设每小时的负载曲线保持不变(尽管水平可能会发生变化)。相比之下,我们的模型表明,小时负荷曲线也会随着按客户类别划分的消费份额的变化而变化,并且会引入新的消费技术,例如电动汽车和(特别是对于丹麦)单独的热泵。

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