Data-driven demand-side energy management approaches based on the smart energy network
The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach empl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2019-11-01
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| Series: | Journal of Algorithms & Computational Technology |
| Online Access: | https://doi.org/10.1177/1748302619891611 |
| _version_ | 1818302706495782912 |
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| author | Feng Pan Guoying Lin Yuyao Yang Sijian Zhang Jucheng Xiao Shuai Fan |
| author_facet | Feng Pan Guoying Lin Yuyao Yang Sijian Zhang Jucheng Xiao Shuai Fan |
| author_sort | Feng Pan |
| collection | DOAJ |
| description | The energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management. |
| first_indexed | 2024-12-13T05:43:10Z |
| format | Article |
| id | doaj.art-6c9d5db3892941aea3295f13dc37eb2c |
| institution | Directory Open Access Journal |
| issn | 1748-3026 |
| language | English |
| last_indexed | 2024-12-13T05:43:10Z |
| publishDate | 2019-11-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Journal of Algorithms & Computational Technology |
| spelling | doaj.art-6c9d5db3892941aea3295f13dc37eb2c2022-12-21T23:57:44ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262019-11-011310.1177/1748302619891611Data-driven demand-side energy management approaches based on the smart energy networkFeng PanGuoying LinYuyao YangSijian ZhangJucheng XiaoShuai FanThe energy shortage problem cannot be ignored in the development of economics. A demand-side smart energy network is introduced in this paper, which integrates renewable energy resources, energy storage devices, and various types of load into an autonomous distributed architecture. Our approach employs the internet, Internet of Things (IoT), data mining, and other advanced technologies. The network aims to share energy information to realize distributed smart energy management, which can, for example, save energy while ensuring the reliability and quality of electricity for customers. Based on the network, a series of smart energy management theories are proposed to support the smart energy network. The core idea of these theories is to allow the network to teach itself in order to learn from the massive amounts of collected energy data, by using machine learning algorithms. The concept of power utility is proposed to quantitatively assess load energy efficiency. Then, a data-driven consumer energy activity recognition method is proposed based on a hidden Markov model (HMM). A test system is generated using field data from a pilot project in Guangdong Province, China. The energy saving rate in our test is 37.9%, which means that the smart energy network and the proposed algorithms perform well for automatic and intelligent energy efficiency management.https://doi.org/10.1177/1748302619891611 |
| spellingShingle | Feng Pan Guoying Lin Yuyao Yang Sijian Zhang Jucheng Xiao Shuai Fan Data-driven demand-side energy management approaches based on the smart energy network Journal of Algorithms & Computational Technology |
| title | Data-driven demand-side energy management approaches based on the smart energy network |
| title_full | Data-driven demand-side energy management approaches based on the smart energy network |
| title_fullStr | Data-driven demand-side energy management approaches based on the smart energy network |
| title_full_unstemmed | Data-driven demand-side energy management approaches based on the smart energy network |
| title_short | Data-driven demand-side energy management approaches based on the smart energy network |
| title_sort | data driven demand side energy management approaches based on the smart energy network |
| url | https://doi.org/10.1177/1748302619891611 |
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