001463073 000__ 04085cam\a22006257i\4500
001463073 001__ 1463073
001463073 003__ OCoLC
001463073 005__ 20230601003304.0
001463073 006__ m\\\\\o\\d\\\\\\\\
001463073 007__ cr\cn\nnnunnun
001463073 008__ 230503s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001463073 019__ $$a1378293346
001463073 020__ $$a9783031278525$$qelectronic book
001463073 020__ $$a3031278526$$qelectronic book
001463073 020__ $$z9783031278518
001463073 020__ $$z3031278518
001463073 0247_ $$a10.1007/978-3-031-27852-5$$2doi
001463073 035__ $$aSP(OCoLC)1378166136
001463073 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX
001463073 049__ $$aISEA
001463073 050_4 $$aTK1005$$b.H33 2023
001463073 08204 $$a621.31/21$$223/eng/20230503
001463073 1001_ $$aHaben, Stephen,$$eauthor.
001463073 24510 $$aCore concepts and methods in load forecasting :$$bwith applications in distribution networks /$$cStephen Haben, Marcus Voss, William Holderbaum.
001463073 264_1 $$aCham :$$bSpringer,$$c2023.
001463073 300__ $$a1 online resource (xv, 331 pages) :$$billustrations (some color)
001463073 336__ $$atext$$btxt$$2rdacontent
001463073 337__ $$acomputer$$bc$$2rdamedia
001463073 338__ $$aonline resource$$bcr$$2rdacarrier
001463073 504__ $$aIncludes bibliographical references and index.
001463073 5050_ $$aChapter 1. Introduction -- Chapter 2. Primer on Distribution Electricity Networks -- Chapter 3. Primer on Statistics and Probability -- Chapter 4. Primer on Machine Learning -- Chapter 5. Time Series Forecasting: Core Concepts and Definitions -- Chapter 6. Load Data: Preparation, Analysis and Feature Generation -- Chapter 7. Verification and Evaluation of Load Forecast Models -- Chapter 8. Load Forecasting Model Training and Selection -- Chapter 9. Benchmark and Statistical Point Forecast Methods -- Chapter 10. Machine Learning Point Forecasts Methods -- Chapter 11. Probabilistic Forecast Methods -- Chapter 12. Load Forecast Process -- Chapter 13. Advanced and Additional Topics -- Chapter 14. Case Study: Low Voltage Demand Forecasts -- Chapter 15. Selected Applications and Examples -- Appendix.
001463073 5060_ $$aOpen access.$$5GW5XE
001463073 520__ $$aThis comprehensive open access book enables readers to discover the essential techniques for load forecasting in electricity networks, particularly for active distribution networks. From statistical methods to deep learning and probabilistic approaches, the book covers a wide range of techniques and includes real-world applications and a worked examples using actual electricity data (including an example implemented through shared code). Advanced topics for further research are also included, as well as a detailed appendix on where to find data and additional reading. As the smart grid and low carbon economy continue to evolve, the proper development of forecasting methods is vital. This book is a must-read for students, industry professionals, and anyone interested in forecasting for smart control applications, demand-side response, energy markets, and renewable utilization.
001463073 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 3, 2023).
001463073 650_0 $$aElectric power-plants$$xLoad$$xForecasting.
001463073 650_0 $$aElectric power distribution$$xForecasting.
001463073 650_0 $$aElectric power transmission$$xForecasting.
001463073 655_0 $$aElectronic books.
001463073 7001_ $$aVoss, Marcus,$$eauthor.
001463073 7001_ $$aHolderbaum, William,$$eauthor.
001463073 77608 $$iPrint version: $$z3031278518$$z9783031278518$$w(OCoLC)1369513342
001463073 852__ $$bebk
001463073 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-031-27852-5$$zOnline Access$$91397441.2
001463073 909CO $$ooai:library.usi.edu:1463073$$pGLOBAL_SET
001463073 980__ $$aBIB
001463073 980__ $$aEBOOK
001463073 982__ $$aEbook
001463073 983__ $$aOnline
001463073 994__ $$a92$$bISE