Machine Learning for Load Forecasting in Power Systems
For the electrical sector, the analysis of massive volumes of data acquired from different electrical systems like Generation, Transmission, and Distribution plays a vital role. Without human interaction, control systems like SCADA and HMI are used to evaluate the data, which is retrieved from vario...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/90/e3sconf_icsdg2023_01008.pdf |
| _version_ | 1797345337463013376 |
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| author | Satyanarayana Salava V. Madhavi Pillalamarri |
| author_facet | Satyanarayana Salava V. Madhavi Pillalamarri |
| author_sort | Satyanarayana Salava V. |
| collection | DOAJ |
| description | For the electrical sector, the analysis of massive volumes of data acquired from different electrical systems like Generation, Transmission, and Distribution plays a vital role. Without human interaction, control systems like SCADA and HMI are used to evaluate the data, which is retrieved from various electrical systems such as Generation, Transmission, and Distribution. Automation of every system is necessary to fulfil industry 4.0 criteria. The Internet of Things (IoT) can be used to do this by incorporating the data while implementing proper cybersecurity safeguards. To improve the operational maintenance of electrical systems in the future, this research makes the suggestion that intelligent predictive data analysis be used. Several energy sources and total capacity data files are used in the analysis of both contemporary and historical data in the study. supervised machine learning algorithms are used to analyze the data that is accessible, and each algorithm’s precisionis evaluated by the examination of anticipated data. |
| first_indexed | 2024-03-08T11:16:15Z |
| format | Article |
| id | doaj.art-3672b682e5d642b38a1409bdfa776a2c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T11:16:15Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-3672b682e5d642b38a1409bdfa776a2c2024-01-26T10:32:22ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014530100810.1051/e3sconf/202345301008e3sconf_icsdg2023_01008Machine Learning for Load Forecasting in Power SystemsSatyanarayana Salava V.0Madhavi Pillalamarri1Departement of Electrical and Electronics Engineering,Hyderabad Institute of Technology and ManagementDepartement of Electrical and Electronics Engineering,Hyderabad Institute of Technology and ManagementFor the electrical sector, the analysis of massive volumes of data acquired from different electrical systems like Generation, Transmission, and Distribution plays a vital role. Without human interaction, control systems like SCADA and HMI are used to evaluate the data, which is retrieved from various electrical systems such as Generation, Transmission, and Distribution. Automation of every system is necessary to fulfil industry 4.0 criteria. The Internet of Things (IoT) can be used to do this by incorporating the data while implementing proper cybersecurity safeguards. To improve the operational maintenance of electrical systems in the future, this research makes the suggestion that intelligent predictive data analysis be used. Several energy sources and total capacity data files are used in the analysis of both contemporary and historical data in the study. supervised machine learning algorithms are used to analyze the data that is accessible, and each algorithm’s precisionis evaluated by the examination of anticipated data.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/90/e3sconf_icsdg2023_01008.pdf |
| spellingShingle | Satyanarayana Salava V. Madhavi Pillalamarri Machine Learning for Load Forecasting in Power Systems E3S Web of Conferences |
| title | Machine Learning for Load Forecasting in Power Systems |
| title_full | Machine Learning for Load Forecasting in Power Systems |
| title_fullStr | Machine Learning for Load Forecasting in Power Systems |
| title_full_unstemmed | Machine Learning for Load Forecasting in Power Systems |
| title_short | Machine Learning for Load Forecasting in Power Systems |
| title_sort | machine learning for load forecasting in power systems |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/90/e3sconf_icsdg2023_01008.pdf |
| work_keys_str_mv | AT satyanarayanasalavav machinelearningforloadforecastinginpowersystems AT madhavipillalamarri machinelearningforloadforecastinginpowersystems |