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Predictive modelling for energy management and power systems engineering /
Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
Elsevier,
[2021]
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | SCIDIR_on1198720484 | ||
| 003 | OCoLC | ||
| 005 | 20231120010512.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 201003t20212021ne a ob 001 0 eng d | ||
| 040 | |a YDX |b eng |e pn |e rda |c YDX |d OPELS |d N$T |d OCLCF |d WAU |d OCLCO |d K6U |d OCLCQ |d OCLCO | ||
| 020 | |a 9780128177730 |q ePub | ||
| 020 | |a 012817773X |q ePub | ||
| 020 | |z 9780128177723 |q paperback | ||
| 035 | |a (OCoLC)1198720484 | ||
| 046 | |k 2021 |2 edtf | ||
| 050 | 4 | |a TK1001 | |
| 082 | 0 | 4 | |a 621.310113 |2 23 |
| 245 | 0 | 0 | |a Predictive modelling for energy management and power systems engineering / |c edited by Ravinesh Deo, Pijush Samui, Sanjiban Sekhar Roy. |
| 264 | 1 | |a Amsterdam, Netherlands : |b Elsevier, |c [2021] | |
| 264 | 4 | |c �2021 | |
| 300 | |a 1 online resource (xvi, 535 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 336 | |a still image |b sti |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a Predictive Modeling for Energy Management and Power Systems Engineering introduces readers to the cutting-edge use of big data and large computational infrastructures in energy demand estimation and power management systems. The book supports engineers and scientists who seek to become familiar with advanced optimization techniques for power systems designs, optimization techniques and algorithms for consumer power management, and potential applications of machine learning and artificial intelligence in this field. The book provides modeling theory in an easy-to-read format, verified with on-site models and case studies for specific geographic regions and complex consumer markets. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Electric power systems |x Computer simulation. | |
| 650 | 6 | |a R�eseaux �electriques (�Energie) |0 (CaQQLa)201-0020539 |x Simulation par ordinateur. |0 (CaQQLa)201-0379159 | |
| 650 | 7 | |a Electric power systems |x Computer simulation |2 fast |0 (OCoLC)fst00905536 | |
| 700 | 1 | |a Deo, Ravinesh, |e editor. | |
| 700 | 1 | |a Samui, Pijush, |d 1978- |e editor. | |
| 700 | 1 | |a Roy, Sanjiban Sekhar, |e editor. | |
| 776 | 0 | 8 | |i Print version: |t Predictive modelling for energy management and power systems engineering. |d [Place of publication not identified] ELSEVIER, 2020 |z 0128177721 |w (OCoLC)1144904745 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128177723 |7 0 |z Texto completo |