Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis
Understanding regional and seasonal patterns in electricity consumption is essential for ensuring grid reliability, resiliency, and sustainable infrastructure management. With the rapid growth in electricity demand data and the integration of distributed energy resources, traditional methods struggl...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10839749/ |
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| author | Utkarsh Misra Gyana Ranjan Sahoo Shikhar Prakash Madhur Srivastava |
| author_facet | Utkarsh Misra Gyana Ranjan Sahoo Shikhar Prakash Madhur Srivastava |
| author_sort | Utkarsh Misra |
| collection | DOAJ |
| description | Understanding regional and seasonal patterns in electricity consumption is essential for ensuring grid reliability, resiliency, and sustainable infrastructure management. With the rapid growth in electricity demand data and the integration of distributed energy resources, traditional methods struggle to capture zonal load patterns effectively. This study presents a dimensionality reduction approach combined with clustering to analyze seasonal energy load dynamics across zones managed by the New York Independent System Operator (NYISO). By applying Multidimensional Scaling (MDS), we condense complex high-dimensional data into a form that reveals nuanced relationships among zones. This transformation allows for effective K-means clustering of load profiles, uncovering four distinct seasonal clusters representing shared load characteristics among NYISO zones. Our analysis, covering eleven zones across four seasons, provides visually compelling two- and three-dimensional scatter plots that highlight both converging and diverging demand patterns. This approach unveils similarities in electricity usage and significant seasonal variations, offering deeper insights into the regional and temporal complexity of electricity demand across NYISO’s operational landscape. The <monospace>Python</monospace> script developed for this work is available on GitHub and the signalsciencelab.com website. |
| format | Article |
| id | doaj-art-f646e95da3464c6f81b093bab5dd9fa9 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f646e95da3464c6f81b093bab5dd9fa92025-01-25T00:00:30ZengIEEEIEEE Access2169-35362025-01-0113135731358310.1109/ACCESS.2025.352947010839749Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering AnalysisUtkarsh Misra0https://orcid.org/0009-0002-2896-0890Gyana Ranjan Sahoo1https://orcid.org/0000-0002-9857-4314Shikhar Prakash2https://orcid.org/0000-0002-7118-3479Madhur Srivastava3https://orcid.org/0000-0002-2095-0412Systems Engineering, Cornell University, Ithaca, NY, USADepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USASystems Engineering, Cornell University, Ithaca, NY, USADepartment of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USAUnderstanding regional and seasonal patterns in electricity consumption is essential for ensuring grid reliability, resiliency, and sustainable infrastructure management. With the rapid growth in electricity demand data and the integration of distributed energy resources, traditional methods struggle to capture zonal load patterns effectively. This study presents a dimensionality reduction approach combined with clustering to analyze seasonal energy load dynamics across zones managed by the New York Independent System Operator (NYISO). By applying Multidimensional Scaling (MDS), we condense complex high-dimensional data into a form that reveals nuanced relationships among zones. This transformation allows for effective K-means clustering of load profiles, uncovering four distinct seasonal clusters representing shared load characteristics among NYISO zones. Our analysis, covering eleven zones across four seasons, provides visually compelling two- and three-dimensional scatter plots that highlight both converging and diverging demand patterns. This approach unveils similarities in electricity usage and significant seasonal variations, offering deeper insights into the regional and temporal complexity of electricity demand across NYISO’s operational landscape. The <monospace>Python</monospace> script developed for this work is available on GitHub and the signalsciencelab.com website.https://ieeexplore.ieee.org/document/10839749/EnergyNYISOelectricity consumption patternload characteristicsmultidimensional scalingclustering |
| spellingShingle | Utkarsh Misra Gyana Ranjan Sahoo Shikhar Prakash Madhur Srivastava Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis IEEE Access Energy NYISO electricity consumption pattern load characteristics multidimensional scaling clustering |
| title | Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis |
| title_full | Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis |
| title_fullStr | Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis |
| title_full_unstemmed | Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis |
| title_short | Unraveling Power Consumption Pattern Using Multidimensional Scaling and Clustering Analysis |
| title_sort | unraveling power consumption pattern using multidimensional scaling and clustering analysis |
| topic | Energy NYISO electricity consumption pattern load characteristics multidimensional scaling clustering |
| url | https://ieeexplore.ieee.org/document/10839749/ |
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