Transfer learning in demand response: A review of algorithms for data-efficient modelling and control
A number of decarbonization scenarios for the energy sector are built on simultaneous electrification of energy demand, and decarbonization of electricity generation through renewable energy sources. However, increased electricity demand due to heat and transport electrification and the variability...
Enregistré dans:
Auteurs principaux: | Thijs Peirelinck, Hussain Kazmi, Brida V. Mbuwir, Chris Hermans, Fred Spiessens, Johan Suykens, Geert Deconinck |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Elsevier
2022
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/d313e56a574c4f5a9f42632ea63e8ffa |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
pycity_scheduling—A Python framework for the development and assessment of optimisation-based power scheduling algorithms for multi-energy systems in city districts
par: Sebastian Schwarz, et autres
Publié: (2021) -
Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor
par: Jian Sun, et autres
Publié: (2021) -
The Model of Makerspace Development Element and Performance Analysis Based on NVivo Classification
par: Yingyan Wang, et autres
Publié: (2021) -
CT Imaging in the Diagnosis of Lung Injury of Organophosphorus Poisoning and Analysis of Its Correlation with Procalcitonin and C-Reactive Protein Levels
par: Wenwen Sun, et autres
Publié: (2021) -
Simulation Analysis of the Evolution of Sustainable Operation of Transport Infrastructure Projects under Government Regulation Based on Prospect Theory and BP Neural Network
par: Chongsen Ma, et autres
Publié: (2021)