Artificial Intelligence for Smart and Sustainable Energy Systems and Applications
Material type:
ArticleLanguage: English Publication details: MDPI - Multidisciplinary Digital Publishing Institute 2020Description: 1 electronic resource (258 p.)Content type: - text
- computer
- online resource
- 9783039288892
- 9783039288908
- Искусственный интеллект
- artificial neural network
- home energy management systems
- conditional random fields
- LR
- ELR
- energy disaggregation
- artificial intelligence
- genetic algorithm
- decision tree
- static young’s modulus
- price
- scheduling
- self-adaptive differential evolution algorithm
- Marsh funnel
- energy
- yield point
- non-intrusive load monitoring
- mud rheology
- distributed genetic algorithm
- MCP39F511
- Jetson TX2
- sustainable development
- artificial neural networks
- transient signature
- load disaggregation
- smart villages
- ambient assisted living
- smart cities
- demand side management
- smart city
- CNN
- wireless sensor networks
- object detection
- drill-in fluid
- ERELM
- sandstone reservoirs
- RPN
- deep learning
- RELM
- smart grids
- multiple kernel learning
- load
- feature extraction
- NILM
- energy management
- energy efficient coverage
- insulator
- Faster R-CNN
- home energy management
- smart grid
- LSTM
- smart metering
- optimization algorithms
- forecasting
- plastic viscosity
- machine learning
- computational intelligence
- policy making
- support vector machine
- internet of things
- sensor network
- nonintrusive load monitoring
- demand response
| Item type | Current library | Collection | Shelving location | Call number | Status | Notes | Date due | Barcode |
|---|---|---|---|---|---|---|---|---|
| Electronic edition | Bucheon University Library | Computers | MDPI books | 004.8 A81 | Not for loan | View (pdf) | 1010633 |
Open Access star Unrestricted online access
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.
Creative Commons https://creativecommons.org/licenses/by-nc-nd/4.0/ cc
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