Exportar Publicação

Labiadh, M., Obrecht, C., Ferreira da Silva, C., Ghodous, P. & Benabdeslem, K. (2023). Query-adaptive training data recommendation for cross-building predictive modeling. Knowledge and Information Systems. 65 (2), 707-732

M. Labiadh et al.,  "Query-adaptive training data recommendation for cross-building predictive modeling", in Knowledge and Information Systems, vol. 65, no. 2, pp. 707-732, 2023

@article{labiadh2023_1730765762979,
	author = "Labiadh, M. and Obrecht, C. and Ferreira da Silva, C. and Ghodous, P. and Benabdeslem, K.",
	title = "Query-adaptive training data recommendation for cross-building predictive modeling",
	journal = "Knowledge and Information Systems",
	year = "2023",
	volume = "65",
	number = "2",
	doi = "10.1007/s10115-022-01771-9",
	pages = "707-732",
	url = "https://link.springer.com/article/10.1007/s10115-022-01771-9"
}

TY  - JOUR
TI  - Query-adaptive training data recommendation for cross-building predictive modeling
T2  - Knowledge and Information Systems
VL  - 65
IS  - 2
AU  - Labiadh, M.
AU  - Obrecht, C.
AU  - Ferreira da Silva, C.
AU  - Ghodous, P.
AU  - Benabdeslem, K.
PY  - 2023
SP  - 707-732
SN  - 0219-1377
DO  - 10.1007/s10115-022-01771-9
UR  - https://link.springer.com/article/10.1007/s10115-022-01771-9
AB  - Predictive modeling in buildings is a key task for the optimal management
of building energy. Relevant building operational data are a prerequisite
for such task, notably when deep learning is used. However, building operational
data are not always available, such is the case in newly built, newly renovated,
or even not yet built buildings. To address this problem, we propose a deep similarity
learning approach to recommend relevant training data to a target building
solely by using a minimal contextual description on it. Contextual descriptions
are modeled as user queries. We further propose to ensemble most used machine
learning algorithms in the context of predictive modeling. This contributes to the
genericity of the proposed methodology. Experimental evaluations show that our
methodology offers a generic methodology for cross-building predictive modeling
and achieves good generalization performance.
ER  -