Data-Intensive Computing in Smart Microgrids
Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid adva...
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| 格式: | Online |
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| 語言: | 英语 |
| 出版: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| 主題: | |
| 在線閱讀: | ONIX_20220111_9783036516271_513 |
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| _version_ | 1869529804479397888 |
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| collection | Directory of Open Access Books |
| description | Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area. |
| format | Online |
| id | doab-20.500.12854ir-76778 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-767782024-04-09T23:15:47Z Data-Intensive Computing in Smart Microgrids Herodotou, Herodotos electricity load forecasting smart grid feature selection Extreme Learning Machine Genetic Algorithm Support Vector Machine Grid Search AMI TL SG NB-PLC fog computing green community resource allocation processing time response time green data center microgrid renewable energy energy trade contract real time power management load forecasting optimization techniques deep learning big data analytics electricity theft detection smart grids electricity consumption electricity thefts smart meter imbalanced data data-intensive smart application cloud computing real-time systems multi-objective energy optimization renewable energy sources wind photovoltaic demand response programs energy management battery energy storage systems demand response scheduling automatic generation control single/multi-area power system intelligent control methods virtual inertial control soft computing control methods n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area. 2022-01-11T13:41:59Z 2022-01-11T13:41:59Z 2021 book ONIX_20220111_9783036516271_513 9783036516271 9783036516288 https://directory.doabooks.org/handle/20.500.12854/76778 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4227 https://mdpi.com/books/pdfview/book/4227 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-1628-8 10.3390/books978-3-0365-1628-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036516271 9783036516288 238 Basel, Switzerland open access |
| spellingShingle | electricity load forecasting smart grid feature selection Extreme Learning Machine Genetic Algorithm Support Vector Machine Grid Search AMI TL SG NB-PLC fog computing green community resource allocation processing time response time green data center microgrid renewable energy energy trade contract real time power management load forecasting optimization techniques deep learning big data analytics electricity theft detection smart grids electricity consumption electricity thefts smart meter imbalanced data data-intensive smart application cloud computing real-time systems multi-objective energy optimization renewable energy sources wind photovoltaic demand response programs energy management battery energy storage systems demand response scheduling automatic generation control single/multi-area power system intelligent control methods virtual inertial control soft computing control methods n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Data-Intensive Computing in Smart Microgrids |
| title | Data-Intensive Computing in Smart Microgrids |
| title_full | Data-Intensive Computing in Smart Microgrids |
| title_fullStr | Data-Intensive Computing in Smart Microgrids |
| title_full_unstemmed | Data-Intensive Computing in Smart Microgrids |
| title_short | Data-Intensive Computing in Smart Microgrids |
| title_sort | data intensive computing in smart microgrids |
| topic | electricity load forecasting smart grid feature selection Extreme Learning Machine Genetic Algorithm Support Vector Machine Grid Search AMI TL SG NB-PLC fog computing green community resource allocation processing time response time green data center microgrid renewable energy energy trade contract real time power management load forecasting optimization techniques deep learning big data analytics electricity theft detection smart grids electricity consumption electricity thefts smart meter imbalanced data data-intensive smart application cloud computing real-time systems multi-objective energy optimization renewable energy sources wind photovoltaic demand response programs energy management battery energy storage systems demand response scheduling automatic generation control single/multi-area power system intelligent control methods virtual inertial control soft computing control methods n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | electricity load forecasting smart grid feature selection Extreme Learning Machine Genetic Algorithm Support Vector Machine Grid Search AMI TL SG NB-PLC fog computing green community resource allocation processing time response time green data center microgrid renewable energy energy trade contract real time power management load forecasting optimization techniques deep learning big data analytics electricity theft detection smart grids electricity consumption electricity thefts smart meter imbalanced data data-intensive smart application cloud computing real-time systems multi-objective energy optimization renewable energy sources wind photovoltaic demand response programs energy management battery energy storage systems demand response scheduling automatic generation control single/multi-area power system intelligent control methods virtual inertial control soft computing control methods n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036516271_513 |