Watch the Dallas Symposium LIVE, and fundraiser auction
Ticket proceeds support mindat.org! - click here...
Log InRegister
Quick Links : The Mindat ManualThe Rock H. Currier Digital LibraryMindat Newsletter [Free Download]
Home PageAbout MindatThe Mindat ManualHistory of MindatCopyright StatusWho We AreContact UsAdvertise on Mindat
Donate to MindatCorporate SponsorshipSponsor a PageSponsored PagesMindat AdvertisersAdvertise on Mindat
Learning CenterWhat is a mineral?The most common minerals on earthInformation for EducatorsMindat ArticlesThe ElementsThe Rock H. Currier Digital LibraryGeologic Time
Minerals by PropertiesMinerals by ChemistryAdvanced Locality SearchRandom MineralRandom LocalitySearch by minIDLocalities Near MeSearch ArticlesSearch GlossaryMore Search Options
Search For:
Mineral Name:
Locality Name:
Keyword(s):
 
The Mindat ManualAdd a New PhotoRate PhotosLocality Edit ReportCoordinate Completion ReportAdd Glossary Item
Mining CompaniesStatisticsUsersMineral MuseumsClubs & OrganizationsMineral Shows & EventsThe Mindat DirectoryDevice SettingsThe Mineral Quiz
Photo SearchPhoto GalleriesSearch by ColorNew Photos TodayNew Photos YesterdayMembers' Photo GalleriesPast Photo of the Day GalleryPhotography

Liu, , Wang, , Qin, , Liu, (2020) Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling. Applied Sciences, 10. 584pp. doi:10.3390/app10020584

Advanced
   -   Only viewable:
Reference TypeJournal (article/letter/editorial)
TitlePower Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
JournalApplied Sciences
AuthorsLiu, Author
Wang, Author
Qin, Author
Liu, Author
Year2020 (January 13)Volume10
PublisherMDPI AG
DOIdoi:10.3390/app10020584Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID10103844Long-form Identifiermindat:1:5:10103844:9
GUID0
Full ReferenceLiu, , Wang, , Qin, , Liu, (2020) Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling. Applied Sciences, 10. 584pp. doi:10.3390/app10020584
Plain TextLiu, , Wang, , Qin, , Liu, (2020) Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling. Applied Sciences, 10. 584pp. doi:10.3390/app10020584
In(n.d.) Applied Sciences Vol. 10. MDPI AG

See Also

These are possibly similar items as determined by title/reference text matching only.

Cao, Jing Jin, Qin, Li Jun (2014) The Summary of Load Forecasting Method Based on Power Demand Side Management. Applied Mechanics and Materials, 494. 1615-1618 doi:10.4028/www.scientific.net/amm.494-495.1615
Shao, Qizhuan, Liu, Shuangquan, Xie, Yigong, Zhu, Xinchun, Zhang, Yilin, Wang, Junzhou, Tang, Junjie (2023) Probabilistic Forecasting of Available Load Supply Capacity for Renewable-Energy-Based Power Systems. Applied Sciences, 13 (15) 8860 doi:10.3390/app13158860
Jiang, Ping, Nie, Ying (2020) A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism. Applied Sciences, 10. 1550pp. doi:10.3390/app10041550
Zan, Shifa, Zhang, Qiang (2023) Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model. Applied Sciences, 13 (7) 4462 doi:10.3390/app13074462
Li, Yaqiong, Wang, Tongxun, Deng, Zhanfeng (2020) An Online Reactive Power-Optimization Strategy Based on Load-Curve Prediction and Segmentation. Applied Sciences, 10. 1145pp. doi:10.3390/app10031145
Yue, Ting, Liu, Yahui (2024) Multi-Scale Price Forecasting Based on Data Augmentation. Applied Sciences, 14 (19). doi:10.3390/app14198737
Xiong, Qingliang, Liu, Mingping, Li, Yuqin, Zheng, Chaodan, Deng, Suhui (2023) Short-Term Load Forecasting Based on VMD and Deep TCN-Based Hybrid Model with Self-Attention Mechanism. Applied Sciences, 13 (22) 12479 doi:10.3390/app132212479
Cao, Jie, Zhang, Ru-Xuan, Liu, Chao-Qiang, Yang, Yuan-Bo, Chen, Chin-Ling (2023) A Group Resident Daily Load Forecasting Method Fusing Self-Attention Mechanism Based on Load Clustering. Applied Sciences, 13 (2) 1165 doi:10.3390/app13021165
Liu, Wei, Dou, Zhenhai, Wang, Weiguo, Liu, Yueyu, Zou, Hao, Zhang, Bo, Hou, Shoujun (2018) Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization. Applied Sciences, 8. 1603pp. doi:10.3390/app8091603
Hu, Linjing, Wang, Jiachen, Guo, Zhaoze, Zheng, Tengda (2023) Load Forecasting Based on LVMD-DBFCM Load Curve Clustering and the CNN-IVIA-BLSTM Model. Applied Sciences, 13 (12) 7332 doi:10.3390/app13127332
Wang, Hongjun, Zhang, Xiang, Wang, Zhengbo, Liu, Shucong (2022) Impact Load Sparse Recognition Method Based on Mc Penalty Function. Applied Sciences, 12 (16) 8147 doi:10.3390/app12168147
 
and/or  
Mindat.org is an outreach project of the Hudson Institute of Mineralogy, a 501(c)(3) not-for-profit organization.
Copyright © mindat.org and the Hudson Institute of Mineralogy 1993-2025, except where stated. Most political location boundaries are Β© OpenStreetMap contributors. Mindat.org relies on the contributions of thousands of members and supporters. Founded in 2000 by Jolyon Ralph.
To cite: Ralph, J., Von Bargen, D., Martynov, P., Zhang, J., Que, X., Prabhu, A., Morrison, S. M., Li, W., Chen, W., & Ma, X. (2025). Mindat.org: The open access mineralogy database to accelerate data-intensive geoscience research. American Mineralogist, 110(6), 833–844. doi:10.2138/am-2024-9486.
Privacy Policy - Terms & Conditions - Contact Us / DMCA issues - Report a bug/vulnerability Current server date and time: August 17, 2025 00:08:58
Go to top of page