Integrated model of spatial and global load forecast for power distribution systems

Brunoro, Claudio Marcelo; Hage, Fabio S. El; Oliveira, Carlos César Barioni de

doi:10.1049/cp.2009.0845

Cited by 2 publications

(1 citation statement)

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“…Spatial feature information has a significant influence on the accuracy of SLF, and using a geographic information system (GIS) to describe and distinguish different SLD patterns is an effective means of obtaining spatial-temporal forecasting models (Monteiro et al, 2005;Brunoro et al, 2009;Shin et al, 2011). Based on the introduction of geospatial features, the SLF accuracies achieved with fuzzy clustering, spatial correlation, cellular automaton, and multiagent methods can all be effectively improved (Ying and Pan, 2008;Melo et al, 2010;Melo et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Spatial Electric Load Forecasting Method Based on High-Level Encoding of High-Resolution Remote Sensing Images

Wang

Sun

2022

Front. Energy Res.

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Spatial load forecasting (SLF) is important for regional power infrastructure construction planning and power grid management. However, for rapidly developing urban regions, SLF is generally inaccurate due to insufficient historical data. Hence, it is important to introduce the spatial load density (SLD) from similar regions to improve the accuracy of SLF. To select similar regions appropriately and acquire SLDs with limited available auxiliary data, this study proposes a spatial electric load forecasting method based on the high-level encoding of high-resolution remote sensing images called SELF-HE. In particular, SELF-HE introduces high-level ground object features as a key index to describe the characteristics of electric loads in a region and can establish connections between the remote sensing image features and SLD similarity. Based on this functionality, SELF-HE achieves more accurate SLF in regions with insufficient historical data. In the experiments, SELF-HE was compared with four traditional methods, and the results revealed that SELF-HE achieved improved SLF accuracy. Given that the high-resolution remote sensing images fully covered urban areas and were readily obtained, the proposed method can improve the accuracy of SLF with extremely low data collection costs and is applicable to rapidly developing urban regions.

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