Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy researc...
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| Format: | Book Chapter |
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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| Online Access: | Get Fullteks DOAB: description of the publication |
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| LEADER | 04076naaaa2201009uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_41352 | ||
| 005 | 20210211 | ||
| 020 | |a books978-3-03928-890-8 | ||
| 020 | |a 9783039288892 | ||
| 020 | |a 9783039288908 | ||
| 024 | 7 | |a 10.3390/books978-3-03928-890-8 |c doi | |
| 041 | 0 | |a English | |
| 042 | |a dc | ||
| 100 | 1 | |a Lytras, Miltiadis |4 auth | |
| 700 | 1 | |a Chui, Kwok Tai |4 auth | |
| 245 | 1 | 0 | |a Artificial Intelligence for Smart and Sustainable Energy Systems and Applications |
| 260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
| 300 | |a 1 electronic resource (258 p.) | ||
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists-namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities. | ||
| 540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
| 546 | |a English | ||
| 653 | |a artificial neural network | ||
| 653 | |a home energy management systems | ||
| 653 | |a conditional random fields | ||
| 653 | |a LR | ||
| 653 | |a ELR | ||
| 653 | |a energy disaggregation | ||
| 653 | |a artificial intelligence | ||
| 653 | |a genetic algorithm | ||
| 653 | |a decision tree | ||
| 653 | |a static young's modulus | ||
| 653 | |a price | ||
| 653 | |a scheduling | ||
| 653 | |a self-adaptive differential evolution algorithm | ||
| 653 | |a Marsh funnel | ||
| 653 | |a energy | ||
| 653 | |a yield point | ||
| 653 | |a non-intrusive load monitoring | ||
| 653 | |a mud rheology | ||
| 653 | |a distributed genetic algorithm | ||
| 653 | |a MCP39F511 | ||
| 653 | |a Jetson TX2 | ||
| 653 | |a sustainable development | ||
| 653 | |a artificial neural networks | ||
| 653 | |a transient signature | ||
| 653 | |a load disaggregation | ||
| 653 | |a smart villages | ||
| 653 | |a ambient assisted living | ||
| 653 | |a smart cities | ||
| 653 | |a demand side management | ||
| 653 | |a smart city | ||
| 653 | |a CNN | ||
| 653 | |a wireless sensor networks | ||
| 653 | |a object detection | ||
| 653 | |a drill-in fluid | ||
| 653 | |a ERELM | ||
| 653 | |a sandstone reservoirs | ||
| 653 | |a RPN | ||
| 653 | |a deep learning | ||
| 653 | |a RELM | ||
| 653 | |a smart grids | ||
| 653 | |a multiple kernel learning | ||
| 653 | |a load | ||
| 653 | |a feature extraction | ||
| 653 | |a NILM | ||
| 653 | |a energy management | ||
| 653 | |a energy efficient coverage | ||
| 653 | |a insulator | ||
| 653 | |a Faster R-CNN | ||
| 653 | |a home energy management | ||
| 653 | |a smart grid | ||
| 653 | |a LSTM | ||
| 653 | |a smart metering | ||
| 653 | |a optimization algorithms | ||
| 653 | |a forecasting | ||
| 653 | |a plastic viscosity | ||
| 653 | |a machine learning | ||
| 653 | |a computational intelligence | ||
| 653 | |a policy making | ||
| 653 | |a support vector machine | ||
| 653 | |a internet of things | ||
| 653 | |a sensor network | ||
| 653 | |a nonintrusive load monitoring | ||
| 653 | |a demand response | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2319 |7 0 |z Get Fullteks |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/41352 |7 0 |z DOAB: description of the publication |