001440569 000__ 05026cam\a2200601\i\4500
001440569 001__ 1440569
001440569 003__ OCoLC
001440569 005__ 20230309004612.0
001440569 006__ m\\\\\o\\d\\\\\\\\
001440569 007__ cr\cn\nnnunnun
001440569 008__ 211027s2022\\\\sz\a\\\\o\\\\\001\0\eng\d
001440569 019__ $$a1280197956$$a1280274708
001440569 020__ $$a9783030776961$$q(electronic bk.)
001440569 020__ $$a3030776964$$q(electronic bk.)
001440569 020__ $$z9783030776954$$q(print)
001440569 020__ $$z3030776956
001440569 0247_ $$a10.1007/978-3-030-77696-1$$2doi
001440569 035__ $$aSP(OCoLC)1280484539
001440569 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dN$T$$dOCLCF$$dOCLCO$$dOCLCQ$$dMUU
001440569 049__ $$aISEA
001440569 050_4 $$aTK1005
001440569 08204 $$a621.310285/63$$223
001440569 24500 $$aApplication of machine learning and deep learning methods to power system problems /$$cMorteza Nazari-Heris, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Moloud Abdar, Houtan Jebelli, Milad Sadat-Mohammadi, editors.
001440569 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2022]
001440569 300__ $$a1 online resource (ix, 391 pages) :$$billustrations
001440569 336__ $$atext$$btxt$$2rdacontent
001440569 337__ $$acomputer$$bc$$2rdamedia
001440569 338__ $$aonline resource$$bcr$$2rdacarrier
001440569 4901_ $$aPower systems,$$x1860-4676
001440569 500__ $$aIncludes index.
001440569 5050_ $$aChapter 1. Power System Challenges and Issues -- Chapter 2. Introduction and literature review of power system challenges and issues -- Chapter 3. Machine learning and power system planning: opportunities, and challenges -- Chapter 4. Introduction to Machine Learning Methods in Energy Engineering -- Chapter 5. Introduction and Literature Review of the Application of Machine Learning/Deep Learning to Control Problems of Power Systems -- Chapter 6. Introduction and literature review of the application of machine learning/deep learning to load forecasting in power system -- Chapter 7. A Survey of Recent particle swarm optimization (PSO)-Based Clustering Approaches to Energy Efficiency in Wireless Sensor Networks -- Chapter 8. Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods -- Chapter 9. Voltage stability assessment in power grids using novel machine learning-based methods -- Chapter 10. Evaluation and Classification of cascading failure occurrence potential due to line outage -- Chapter 11. LSTM-Assisted Heating Energy Demand Management in Residential Buildings -- Chapter 12. Wind Speed Forecasting Using Innovative Regression Applications of Machine Learning Techniques -- Chapter 13. Effective Load Pattern Classification by Processing the Smart Meter Data Based on Event-Driven Processing and Machine Learning -- Chapter 14. Prediction of Out-of-step Condition for Synchronous Generators Using Decision Tree Based on the Dynamic data by WAMS/PMU -- Chapter 15. The adaptive neuro-fuzzy inference system model for short-term load, price and topology forecasting of distribution system -- Chapter 16. Application of Machine Learning for Predicting User Preferences in Optimal Scheduling of Smart Appliances -- Chapter 17. Machine Learning Approaches in a Real Power System and Power Markets.
001440569 506__ $$aAccess limited to authorized users.
001440569 520__ $$aThis book evaluates the role of innovative machine learning and deep learning methods in dealing with power system issues, concentrating on recent developments and advances that improve planning, operation, and control of power systems. Cutting-edge case studies from around the world consider prediction, classification, clustering, and fault/event detection in power systems, providing effective and promising solutions for many novel challenges faced by power system operators. Written by leading experts, the book will be an ideal resource for researchers and engineers working in the electrical power engineering and power system planning communities, as well as students in advanced graduate-level courses. Offers innovative machine learning and deep learning methods for dealing with power system issues; Provides promising solution methodologies; Covers theoretical background and experimental analysis.
001440569 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 27, 2021).
001440569 650_0 $$aElectric power systems$$xData processing.
001440569 650_0 $$aMachine learning.
001440569 650_6 $$aRéseaux électriques (Énergie)$$xInformatique.
001440569 650_6 $$aApprentissage automatique.
001440569 655_0 $$aElectronic books.
001440569 7001_ $$aNazari-Heris, Morteza,$$eeditor.
001440569 7001_ $$aAsadi, Somayeh,$$d1981-$$eeditor.
001440569 7001_ $$aMohammadi-Ivatloo, Behnam,$$eeditor.
001440569 7001_ $$aAbdar, Moloud,$$eeditor.
001440569 7001_ $$aJebelli, Houtan,$$eeditor.
001440569 7001_ $$aSadat-Mohammadi, Milad,$$eeditor.
001440569 77608 $$iPrint version:$$z3030776956$$z9783030776954$$w(OCoLC)1249093050
001440569 830_0 $$aPower systems,$$x1860-4676
001440569 852__ $$bebk
001440569 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-77696-1$$zOnline Access$$91397441.1
001440569 909CO $$ooai:library.usi.edu:1440569$$pGLOBAL_SET
001440569 980__ $$aBIB
001440569 980__ $$aEBOOK
001440569 982__ $$aEbook
001440569 983__ $$aOnline
001440569 994__ $$a92$$bISE