A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework
A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework
复制标题
基于灵活势流模型的高分辨率时空能源需求预测框架
DOI：
10.1016/j.apenergy.2021.117321
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发表时间：
2021-10
期刊：
影响因子：
11.2
通讯作者：
Ovtcharova Jivka
中科院分区：
文献类型：
--
作者：
Peng Jieyang;Kimmig Andreas;Niu Zhibin;Wang Jiahai;Liu Xiufeng;Ovtcharova Jivka
了解城市需求概况是能源调度和优化电力供应的重要决定因素。对于能源供应系统的设计，一个重要的考虑是，表达城市家庭能源需求作为空间和时间函数的特征。然而，大多数研究活动的重点只是时间序列数据的建模。目前文献中很少报道高分辨率的能量时空分布预测模型。本文提出了一种基于势流的城市能源需求时空预测模型。与以往的研究相比，势流能够以较高的分辨率描述空间中的能量迁移。基于向量的取向，该模型可以预测能源需求空间迁移的方向和强度，识别能源转移事件。在实际数据集上的大量实验证明，与传统方法相比，我们的方法可以达到更好的预测精度。在进一步的实证研究中，我们发现时间电力需求流在城市不同区域表现出局部集中的行为。除了季节、峰谷等时间因素外，这种聚集行为还取决于当地人口和主要产业（金融、商业、住宅等）。最后，我们利用熵来定量描述这种聚类现象的强度，并探讨其与气象因子的关系。我们的研究展示了一种统一的可视化预测方法来支持探索性需求分析。我们预计，这一进程将在未来扩大，以支持更多形式的能源。
Understanding urban demand profiles is an important determinant for energy dispatch and the optimization of the electric energy supply. For the design of the energy supply system, an important consideration is, to express the characteristics of urban household energy demand as a function of space and time. However, the focus of most research activities is only on the modeling of time series data. High-resolution forecasting models for the spatial–temporal distribution of energy were rarely reported in current literature. In this paper, we propose a spatio-temporal forecasting model based on potential-flow for urban energy demand forecasting. Compared with previous studies, potential-flow can describe energy migration in space with a high resolution. Based on the orientation of vectors, our model can predict the direction and intensity of spatial migrations in energy demand and identify energy transfer events. Extensive experiments on real-world data sets verify that our approach can achieve a better prediction accuracy compared with traditional methods. In further empirical studies, we find that the temporal electricity demand flow shows locally concentrating behavior for different regions of the city. In addition to temporal factors such as seasons, peaks and valleys, such clustering behavior also depend on local populations and major industries (financial, commercial, residential, etc.). Finally, we use entropy to quantitatively describe the intensity of this clustering phenomenon and explore its relationship with meteorological factors. Our research demonstrates a unified visual prediction approach to support exploratory demand analysis. We anticipate that the process will be expanded to support more forms of energy in the future.
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