Data-Driven Methods of Machine Learning in modeling the Smart Grids

Abstract

Electricity demand is rising in lockstep with globalpopulation growth. The present power system, which is almosta century old, faces numerous issues in maintaining a steadysupply of electricity from huge power plants to customers. Tomeet these issues, the electricity industry has enthusiasticallyembraced the new smart grid concept proposed by engineers. Ifwe can provide a secure smart grid, this movement will be moreuseful and sustainable. Machine learning, which is a relativelyrecent era of information technology, has the potential to makesmart grids extremely safe. This paper is a literature survey ofthe application of machine learning in different areas of smartgrids. This paper concludes by mentioning the best machinelearning algorithms that can be used in different aspects of thesmart grid

Editorial: Advanced data-driven methods and applications for smart grid

“…The explosive growth of data has brought challenges to power monitoring and data transmission to a certain extent, and also restricted the development of smart grid to a certain extent. The development of smart grid requires scientific methods to optimize the configuration of data, deeply analyze the massive data stored in it, and use big data technology to effectively mine, analyze, transform and store unstructured data, so as to make the smart grid develop more directionally and achieve the goal of scientific and sustainable development (Srikantha and Kundur, 2019;Rituraj et al, 2022).…”