Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data.
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| Title: | Assessing Power System Reliability Using Anomaly Detection in Daily Nighttime Light Data. |
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| Authors: | Xu, Nuo1 (AUTHOR), Cao, Xin1,2 (AUTHOR) caoxin@bnu.edu.cn, Chen, Miaoying1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1417. 27p. |
| Subjects: | Electric power system reliability, Satellite-based remote sensing, Energy consumption, Electric power failures, Sustainable development, Outlier detection |
| Abstract: | Highlights: What are the main findings? We developed a novel, self-adaptive framework for detecting power outages from daily satellite nighttime light data, eliminating the need for prior event knowledge or fixed regional thresholds. The derived satellite-based reliability index (NTPRI) shows a significant correlation with ground-measured grid reliability, validating its use as a scalable proxy metric. What are the implications of the main findings? The framework provides a transferable, remote sensing-based tool for large-scale, long-term monitoring of electricity supply reliability, which is particularly valuable for data-scarce regions. It enables rapid impact assessment of power systems following disasters and supports the tracking of infrastructure progress toward related Sustainable Development Goals (e.g., SDG 7). Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank's System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We developed a novel, self-adaptive framework for detecting power outages from daily satellite nighttime light data, eliminating the need for prior event knowledge or fixed regional thresholds. The derived satellite-based reliability index (NTPRI) shows a significant correlation with ground-measured grid reliability, validating its use as a scalable proxy metric. What are the implications of the main findings? The framework provides a transferable, remote sensing-based tool for large-scale, long-term monitoring of electricity supply reliability, which is particularly valuable for data-scarce regions. It enables rapid impact assessment of power systems following disasters and supports the tracking of infrastructure progress toward related Sustainable Development Goals (e.g., SDG 7). Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cross-regional application. To address this, we develop an adaptive thresholding framework using daily NASA Black Marble data. Observations are grouped by view angle to mitigate radiometric instability, and a per-pixel dynamic baseline is constructed from high-radiance statistics, enabling robust anomaly detection without prior outage timing. From the detected anomalies, we formulate a population-weighted NTL power reliability index (NTPRI) to quantify regional electricity service reliability. Validation across six diverse outage events yields an F1 score of 0.807. National-scale analysis shows NTPRI correlates significantly with the World Bank's System Average Interruption Duration Index (SAIDI). The derived Light Anomaly Rate (LAR) further supports pixel-level frequency analysis. Together, this framework provides a transferable remote-sensing tool for large-scale power-reliability assessment in data-scarce regions, supporting disaster impact evaluation and energy vulnerability analysis. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091417 |
