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- 000 02190nam a2200349 i 4500
- 008 241126s2025 ne a 001 0 eng d
- 035 __ |a (OCoLC)1473751406
- 040 __ |a OPELS |b eng |e rda |c OPELS |d OCLCO |d StGlU
- 082 04 |a 621.31028563/1 |2 23/eng/20241126
- 245 00 |a Green machine learning and big data for smart grids : : |b practices and applications / |c edited by V. Indragandhi, R. Elakkiya and V. Subramaniyaswamy.
- 260 __ |a Amsterdam, Netherlands : |b Elsevier, |c [2025]
- 300 __ |a xvii, 298 pages : |b illustrations (some colour) ; |c 23 cm.
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 490 0_ |a Advances in Intelligent Energy Systems
- 500 __ |a Includes index.
- 520 __ |a Green Machine Learning and Big Data for Smart Grids: Practices and Applications is a guidebook to the best practices and potential for green data analytics when generating innovative solutions to renewable energy integration in the power grid. This book begins with a solid foundation in the concept of "green" machine learning and the essential technologies for utilizing data analytics in smart grids. A variety of scenarios are examined closely, demonstrating the opportunities for supporting renewable energy integration using machine learning, from forecasting and stability prediction to smart metering and disturbance tests. Uses for control of physical components including inverters and converters are examined, along with policy implications. Importantly, real-world case studies and chapter objectives are combined to signpost essential information, and to support understanding and implementation.
- 650 _0 |a Smart power grids |x Data processing.
- 650 _0 |a Machine learning.
- 700 1_ |a Indragandhi, V., |e editor.
- 700 1_ |a Elakkiya, R., |e editor.
- 700 1_ |a Subramaniyaswamy, V., |e editor.
- 856 40 |u https://ezproxy.lib.gla.ac.uk/login?url=https://www.sciencedirect.com/science/book/9780443289514 |z Connect to resource