MIKEL
LUMBRERAS MUGAGUREN
INVESTIGADOR/A PREDOCTORAL
Basque Research and Technology Alliance
Mendaro, EspañaPublicaciones en colaboración con investigadoras/es de Basque Research and Technology Alliance (12)
2023
-
Surface heat transfer coefficients in building envelopes: Uncertainty levels in experimental methods
Journal of Building Physics, Vol. 47, Núm. 1, pp. 62-91
-
Unsupervised recognition and prediction of daily patterns in heating loads in buildings
Journal of Building Engineering, Vol. 65
2022
-
Corrigendum to “Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters” [Energy 239, Part D, 2022, 122318] (Energy (2022) 239(PD), (S0360544221025664), (10.1016/j.energy.2021.122318))
Energy
-
Data Driven Supervision of District Heating Systems
Green Energy and Technology (Springer Science and Business Media Deutschland GmbH), pp. 165-178
-
Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters
Energy, Vol. 239
-
System Level Planning of Heat Production Technologies
Green Energy and Technology (Springer Science and Business Media Deutschland GmbH), pp. 61-97
2021
-
Corrigendum to “Energy & economic assessment of façade-integrated solar thermal systems combined with ultra-low temperature district-heating” [Renew. Energy 159 (2020) 1000–1014], ISSN 0960–1481, https://doi.org/10.1016/j.renene.2020.06.019 (Renewable Energy (2020) 159 (1000–1014), (S0960148120309137), (10.1016/j.renene.2020.06.019))
Renewable Energy
-
Unsupervised clustering for pattern recognition of heating energy demand in buildings connected to district-heating network
2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021
2020
-
Data driven process for the energy assessment of building envelope retrofits
E3S Web of Conferences
-
District Heating De-Carbonisation in Belgrade. Multi-Year transition plan
IOP Conference Series: Earth and Environmental Science
-
Energy & economic assessment of façade-integrated solar thermal systems combined with ultra-low temperature district-heating
Renewable Energy, Vol. 159, pp. 1000-1014
-
Energy meters in district-heating substations for heat consumption characterization and prediction using machine-learning techniques
IOP Conference Series: Earth and Environmental Science