Dynamic horizon selection methodology for model predictive control in buildings

Autor: Gerard Laguna, Gerard Mor, Florencia Lazzari, Eloi Gabaldon, Arash Erfani, Dirk Saelens, Jordi Cipriano

Publikováno v: Energy Reports, Vol 8, Iss , Pp 10193-10202 (2022)

The interest in model predictive control (MPC) for buildings has grown in recent years due to the widespread implementation of dynamic electricity tariffs, energy flexibility and distributed energy resources. The MPC applied on buildings is a computa

Externí odkaz: https://doaj.org/article/89e8a6a5d0874a1db1b7f8c2632854de

A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond

Autor: Gerard Mor, Jordi Cipriano, Giacomo Martirano, Francesco Pignatelli, Chiara Lodi, Florencia Lazzari, Benedetto Grillone, Daniel Chemisana

Publikováno v: Energy Reports, Vol 7, Iss , Pp 5667-5684 (2021)

A bottom-up electricity characterisation methodology of the building stock at the local level is presented. It is based on the statistical learning analysis of aggregated energy consumption data, weather data, cadastre, and socioeconomic information.

Externí odkaz: https://doaj.org/article/70d7ff0d26ce47269a32fe45dde904cb

Optimizing planning and operation of renewable energy communities with genetic algorithms

Autor: Florencia Lazzari, Gerard Mor, Jordi Cipriano, Francesc Solsona, Daniel Chemisana, Daniela Guericke

Publikováno v: Applied energy, 338:120906. Elsevier

Renewable Energy Communities (REC) have the potential to become a key agent for the energy transition. Since consumers have different consumption patterns depending on their habits, their grouping allows for a better use of the resource. REC provide

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e22146a179b89e90608eca7ad1465a8
https://doi.org/10.1016/j.apenergy.2023.120906

Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

Autor: Gerard Mor, Jordi Cipriano, Stoyan Danov, Florencia Lazzari, Benedetto Grillone, Andreas Sumper

Publikováno v: Repositorio Abierto de la UdL
Universitad de Lleida
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Energies, Vol 14, Iss 5556, p 5556 (2021)
Energies; Volume 14; Issue 17; Pages: 5556

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlock

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c49632656f66a926b4ceaab36c937c57
https://doi.org/10.3390/en14175556