Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering
Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of c...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9678362/ |
| _version_ | 1818952538043449344 |
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| author | Negin Alemazkoor Mazdak Tootkaboni Roshanak Nateghi Arghavan Louhghalam |
| author_facet | Negin Alemazkoor Mazdak Tootkaboni Roshanak Nateghi Arghavan Louhghalam |
| author_sort | Negin Alemazkoor |
| collection | DOAJ |
| description | Accurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart-meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data. |
| first_indexed | 2024-12-20T09:51:58Z |
| format | Article |
| id | doaj.art-6342af67e3274e0cb6dc51e5a5659ccc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T09:51:58Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6342af67e3274e0cb6dc51e5a5659ccc2022-12-21T19:44:34ZengIEEEIEEE Access2169-35362022-01-01108377838710.1109/ACCESS.2022.31426809678362Smart-Meter Big Data for Load Forecasting: An Alternative Approach to ClusteringNegin Alemazkoor0https://orcid.org/0000-0003-0221-3985Mazdak Tootkaboni1https://orcid.org/0000-0001-9318-1223Roshanak Nateghi2https://orcid.org/0000-0003-4569-9233Arghavan Louhghalam3https://orcid.org/0000-0002-6581-5986School of Industrial Engineering, Purdue University, West Lafayette, IN, USADepartment of Civil and Environmental Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, USASchool of Industrial Engineering, Purdue University, West Lafayette, IN, USADepartment of Civil and Environmental Engineering, University of Massachusetts Dartmouth, Dartmouth, MA, USAAccurate forecasting of electricity demand is vital to the resilient management of energy systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy have primarily centered around cluster-based approaches (CBAs), where smart-meter data are grouped into a small number of clusters and separate prediction models are developed for each cluster. The cluster-based predictions are then aggregated to compute the total demand. CBAs have provided promising results compared to conventional approaches that are generally not conducive to integrating smart-meter data. However, CBAs are computationally costly and suffer from the curse of dimensionality, especially under scenarios involving smart-meter data from millions of customers. In this work, we propose an efficient reduced model approach (RMA) that leverages a novel hierarchical dimension reduction algorithm to enable the integration of fine-resolution high-dimensional smart-meter data for millions of customers in load prediction. We demonstrate the applicability of our proposed approach by using data from a utility company, based in Illinois, United States, with more than 3.7 million customers and present model performance in-terms of forecast accuracy. The proposed hierarchical dimension reduction approach enables utilizing the high-resolution data from smart-meters in a scalable manner that is not exploitable otherwise. The results shows significant improvements in forecast accuracy compared to the available approaches that either do not harness fine-resolution data or are not scalable to large-scale smart-meter big data.https://ieeexplore.ieee.org/document/9678362/Short-term load forecastingsmart-meter databig datahierarchical dimension reduction |
| spellingShingle | Negin Alemazkoor Mazdak Tootkaboni Roshanak Nateghi Arghavan Louhghalam Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering IEEE Access Short-term load forecasting smart-meter data big data hierarchical dimension reduction |
| title | Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering |
| title_full | Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering |
| title_fullStr | Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering |
| title_full_unstemmed | Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering |
| title_short | Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering |
| title_sort | smart meter big data for load forecasting an alternative approach to clustering |
| topic | Short-term load forecasting smart-meter data big data hierarchical dimension reduction |
| url | https://ieeexplore.ieee.org/document/9678362/ |
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