print

Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities

author
Machlev, Ram
Heistrene, L.
Perl, M.
Levy, K. Y.
Belikov, Juri
Mannor, S.
Levron, Yoash
statement of authorship
R. Machlev, L. Heistrene, M. Perl, K. Y. Levy, J. Belikov, S. Mannor, Y. Levron
source
Energy and AI
publisher
Elsevier
journal volume number month
vol. 9
year of publication
2022
pages
art. 100169, 13 p. : ill
url
https://doi.org/10.1016/j.egyai.2022.100169
subject term
elektrienergia
süvaõpe
tehisintellekt
tehisnärvivõrgud
keyword
power
energy
neural network
deep-learning (DL)
Explainable Artificial Intelligence (XAI)
ISSN
2666-5468
notes
Bibliogr.: 71 ref
Open Access
Open Access
scientific publication
teaduspublikatsioon
classifier
1.1
Scopus
Journal metrics at Scopus
Article at Scopus
WOS
Article at WOS
category (general)
Engineering
Computer science
Energy
category (sub)
Engineering. Engineering (miscellaneous)
Computer science. Artificial intelligence
Energy. General energy
kvartiil
Q1
TTÜ department
tarkvarateaduse instituut
language
inglise
Uurimisrühm
Centre for intelligent systems
Nonlinear control systems group
Machlev, R., Heistrene, L., Perl, M., Levy, K. Y., Belikov, J., Mannor, S., Levron, Y. Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities // Energy and AI (2022) vol. 9, art. 100169, 13 p. : ill. https://doi.org/10.1016/j.egyai.2022.100169