| Issue |
E3S Web Conf.
Volume 453, 2023
International Conference on Sustainable Development Goals (ICSDG 2023)
|
|
|---|---|---|
| Article Number | 01008 | |
| Number of page(s) | 6 | |
| DOI | https://doi.org/10.1051/e3sconf/202345301008 | |
| Published online | 30 November 2023 | |
Machine Learning for Load Forecasting in Power Systems
Departement of Electrical and Electronics Engineering,Hyderabad Institute of Technology and Management
* Corresponding author: pshve2011@gmail.com
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.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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