A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm
Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This pa...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6688889 |
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| author | Dongguo Zhou Yangjie Wu Hong Zhou |
| author_facet | Dongguo Zhou Yangjie Wu Hong Zhou |
| author_sort | Dongguo Zhou |
| collection | DOAJ |
| description | Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances. |
| format | Article |
| id | doaj-art-c1d1c4fc22e74809b2a9523c18afe079 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c1d1c4fc22e74809b2a9523c18afe0792025-02-03T06:43:46ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66888896688889A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM AlgorithmDongguo Zhou0Yangjie Wu1Hong Zhou2School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaSchool of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaNonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances.http://dx.doi.org/10.1155/2021/6688889 |
| spellingShingle | Dongguo Zhou Yangjie Wu Hong Zhou A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm Complexity |
| title | A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm |
| title_full | A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm |
| title_fullStr | A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm |
| title_full_unstemmed | A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm |
| title_short | A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM Algorithm |
| title_sort | nonintrusive load monitoring method for microgrid ems using bi lstm algorithm |
| url | http://dx.doi.org/10.1155/2021/6688889 |
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