Temperature prediction of battery energy storage plant based on EGA-BiLSTM
Battery energy storage plants (BESPs) are more and more important in the future power systems. The industry desires a credible temperature prediction method to deliver a safe temperature range of the BESPs. This paper develops a bespoke methodology that combines the elitist preservation genetic algo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-08-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722004425 |
| _version_ | 1798032969694183424 |
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| author | Ling Jiang Chunkai Yan Xinsong Zhang Bojun Zhou Tianyu Cheng Jiahao Zhao Juping Gu |
| author_facet | Ling Jiang Chunkai Yan Xinsong Zhang Bojun Zhou Tianyu Cheng Jiahao Zhao Juping Gu |
| author_sort | Ling Jiang |
| collection | DOAJ |
| description | Battery energy storage plants (BESPs) are more and more important in the future power systems. The industry desires a credible temperature prediction method to deliver a safe temperature range of the BESPs. This paper develops a bespoke methodology that combines the elitist preservation genetic algorithm (EGA) and bidirectional long-short term memory network (BiLSTM) to deliver accurate battery temperature predictions. First, this paper applies the EGA to obtain the optimal segmentation strategy of time-series data. Second, the BiLSTM is used to predict both the highest and the lowest temperature of the battery pack within the energy storage power plant. In this step, an improved loss function is proposed to improve the prediction accuracy of the BiLSTM. This paper applies the real operation data, from January to February 2020, of one certain BESP to justify the developed an EGA-BiLSTM method. Case studies reveal that compared to the LSTM and the LightGBM, the developed method significantly reduces the prediction error by 12% and 26%, respectively. Lastly, we also conduct ablation experiments to prove that the EGA-BiLSTM method also accommodates short-term scenarios, where the R2_score of short-term temperature prediction for BESP could achieve up to 0.86. All these can provide an effective method of temperature prediction, ensuring safe operation of the BESPs. |
| first_indexed | 2024-04-11T20:22:52Z |
| format | Article |
| id | doaj.art-844d8e5e60974075bc348ab0109662dc |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-11T20:22:52Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-844d8e5e60974075bc348ab0109662dc2022-12-22T04:04:46ZengElsevierEnergy Reports2352-48472022-08-01810091018Temperature prediction of battery energy storage plant based on EGA-BiLSTMLing Jiang0Chunkai Yan1Xinsong Zhang2Bojun Zhou3Tianyu Cheng4Jiahao Zhao5Juping Gu6School of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, China; School of Automation, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, ChinaSchool of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, China; School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China; Corresponding author at: School of Information Science and Technology, Nantong University, No. 9 Seyuan Road, Nantong, 226019, China.Battery energy storage plants (BESPs) are more and more important in the future power systems. The industry desires a credible temperature prediction method to deliver a safe temperature range of the BESPs. This paper develops a bespoke methodology that combines the elitist preservation genetic algorithm (EGA) and bidirectional long-short term memory network (BiLSTM) to deliver accurate battery temperature predictions. First, this paper applies the EGA to obtain the optimal segmentation strategy of time-series data. Second, the BiLSTM is used to predict both the highest and the lowest temperature of the battery pack within the energy storage power plant. In this step, an improved loss function is proposed to improve the prediction accuracy of the BiLSTM. This paper applies the real operation data, from January to February 2020, of one certain BESP to justify the developed an EGA-BiLSTM method. Case studies reveal that compared to the LSTM and the LightGBM, the developed method significantly reduces the prediction error by 12% and 26%, respectively. Lastly, we also conduct ablation experiments to prove that the EGA-BiLSTM method also accommodates short-term scenarios, where the R2_score of short-term temperature prediction for BESP could achieve up to 0.86. All these can provide an effective method of temperature prediction, ensuring safe operation of the BESPs.http://www.sciencedirect.com/science/article/pii/S2352484722004425Battery energy storage plantsTemperature predictionElitist preservation genetic algorithmBidirectional long-short term memory network |
| spellingShingle | Ling Jiang Chunkai Yan Xinsong Zhang Bojun Zhou Tianyu Cheng Jiahao Zhao Juping Gu Temperature prediction of battery energy storage plant based on EGA-BiLSTM Energy Reports Battery energy storage plants Temperature prediction Elitist preservation genetic algorithm Bidirectional long-short term memory network |
| title | Temperature prediction of battery energy storage plant based on EGA-BiLSTM |
| title_full | Temperature prediction of battery energy storage plant based on EGA-BiLSTM |
| title_fullStr | Temperature prediction of battery energy storage plant based on EGA-BiLSTM |
| title_full_unstemmed | Temperature prediction of battery energy storage plant based on EGA-BiLSTM |
| title_short | Temperature prediction of battery energy storage plant based on EGA-BiLSTM |
| title_sort | temperature prediction of battery energy storage plant based on ega bilstm |
| topic | Battery energy storage plants Temperature prediction Elitist preservation genetic algorithm Bidirectional long-short term memory network |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722004425 |
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