Zobrazeno 1 - 10
of 54
pro vyhledávání: '"Qingsong Wen"'
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 3, Pp 426-441 (2024)
Abstract Long‐term storage will play a crucial role in future local multi‐energy systems (MES) with high penetration renewable energy integration for demand balancing. Local MES planning with long‐term energy storage is essentially a very large
Externí odkaz:
https://doaj.org/article/8ea4f2a3d4564365a0981986e87a08b5
Publikováno v:
iEnergy, Vol 1, Iss 3, Pp 341-350 (2022)
Building-level load forecasting has become essential with the support of fine-grained data collected by widely deployed smart meters. It acts as a basis for arranging distributed energy resources, implementing demand response, etc. Compared to aggreg
Externí odkaz:
https://doaj.org/article/ec30d416e1ca421a98a97688b8211788
Publikováno v:
IEEE Transactions on Power Systems. 38:1445-1459
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::482947cb941f98b313fa8169cb2047e4
https://doi.org/10.1109/tpwrs.2022.3175252
https://doi.org/10.1109/tpwrs.2022.3175252
Accurate prediction of electric load is crucial in power grid planning and management. In this paper, we solve the electric load forecasting problem under extreme events such as scorching heats. One challenge for accurate forecasting is the lack of t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::881d7a68907f50e16df0fd00ecc568ea
http://arxiv.org/abs/2306.08299
http://arxiv.org/abs/2306.08299
Publikováno v:
IET Renewable Power Generation.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::84c521d0d51e9f6141a659dd51e7c276
https://doi.org/10.1049/rpg2.12726
https://doi.org/10.1049/rpg2.12726
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-doma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dab3c7861ab8eaaca1256acf20e6c880
http://arxiv.org/abs/2210.09693
http://arxiv.org/abs/2210.09693
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b68225f974a24a5e11dd846fc34db30
https://doi.org/10.1145/3534678.3542612
https://doi.org/10.1145/3534678.3542612
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9d4ef695fb7e0f5ac26f2a615e540cf
https://doi.org/10.1145/3534678.3539234
https://doi.org/10.1145/3534678.3539234
Publikováno v:
Applied Energy. 331:120396
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b395f2e15958c20d72c0fb326e3174ce
https://doi.org/10.1016/j.apenergy.2022.120396
https://doi.org/10.1016/j.apenergy.2022.120396
Publikováno v:
IEEE Transactions on Smart Grid. :1-1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d55f2355eb93a065f27a67132229cad8
https://doi.org/10.1109/tsg.2023.3253855
https://doi.org/10.1109/tsg.2023.3253855