A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework

Jieyang Peng, Andreas Kimmig, Zhibin Niu, Jiahai Wang, Xiufeng Liu and Jivka Ovtcharova

Applied Energy, 2021, vol. 299, issue C, No S0306261921007315

Abstract: 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.

Keywords: Urban energy; Spatio-temporal demand forecasting; Visual analysis; Spatial mapping; Pattern recognition (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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DOI: 10.1016/j.apenergy.2021.117321

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