Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Yannig Goude"'
Publikováno v:
Revstat Statistical Journal, Vol 21, Iss 2 (2023)
Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to repl
Externí odkaz:
https://doaj.org/article/c73786d8529149a588fa523971d568ac
Autor:
Joseph De Vilmarest, Yannig Goude
Publikováno v:
IEEE Open Access Journal of Power and Energy, Vol 9, Pp 192-201 (2022)
We present the winning strategy for the IEEE DataPort Competition on Day-Ahead Electricity Load Forecasting: Post-Covid Paradigm. This competition was organized to design new forecasting methods for unstable periods such as the one starting in Spring
Externí odkaz:
https://doaj.org/article/21e56a66e2f74213a2384dc7783a39c5
Publikováno v:
Journal of Statistical Software, Vol 100, Pp 1-31 (2021)
Generalized additive models (GAMs) are flexible non-linear regression models, which can be fitted efficiently using the approximate Bayesian methods provided by the mgcv R package. While the GAM methods provided by mgcv are based on the assumption th
Externí odkaz:
https://doaj.org/article/b2f599d50a774eab989c491c66b5af79
Publikováno v:
Energies, Vol 14, Iss 8, p 2233 (2021)
The field of electric vehicle charging load modelling has been growing rapidly in the last decade. In light of the Paris Agreement, it is crucial to keep encouraging better modelling techniques for successful electric vehicle adoption. Additionally,
Externí odkaz:
https://doaj.org/article/71641f84040d48b99ff7d347c14e2a5e
Publikováno v:
Energies, Vol 11, Iss 7, p 1893 (2018)
Smart grids require flexible data driven forecasting methods. We propose clustering tools for bottom-up short-term load forecasting. We focus on individual consumption data analysis which plays a major role for energy management and electricity load
Externí odkaz:
https://doaj.org/article/6738856350184406b54a549ce418a318
Publikováno v:
IEEE Transactions on Power Systems. 36:4754-4763
The coronavirus disease 2019 (COVID-19) pandemic has urged many governments in the world to enforce a strict lockdown where all nonessential businesses are closed and citizens are ordered to stay at home. One of the consequences of this policy is a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::47a2cadd2f1c1e2b21ba6bf91962ce6e
https://doi.org/10.1109/tpwrs.2021.3067551
https://doi.org/10.1109/tpwrs.2021.3067551
Publikováno v:
IEEE Transactions on Smart Grid. 11:1895-1904
The development of smart grid and new advanced metering infrastructures induces new opportunities and challenges for utilities. Exploiting smart meters information for forecasting stands as a key point for energy providers who have to deal with time
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c66222a449da4d37b94fa935c456c224
https://doi.org/10.1109/tsg.2019.2945088
https://doi.org/10.1109/tsg.2019.2945088
Publikováno v:
Journal of the American Statistical Association. 116:1402-1412
We propose a novel framework for fitting additive quantile regression models, which provides well calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9823c4c75083cfeadcfd417c893f7915
https://doi.org/10.1080/01621459.2020.1725521
https://doi.org/10.1080/01621459.2020.1725521
Publikováno v:
International Journal of Forecasting
International Journal of Forecasting, 2022
International Journal of Forecasting, 2022
International audience; In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implement
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a846578eec9ca1e6d64aa0fe40069da3
https://hal.inria.fr/hal-03469721/document
https://hal.inria.fr/hal-03469721/document
