Browsing NTNU Open by Author "Georges, Laurent Francis Ghislain"
Now showing items 1-7 of 7
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Demand response with active phase change material based thermal energy storage in buildings
Chandra, Aneesh; Zong, Yi; Georges, Laurent Francis Ghislain; You, Shi (Journal article; Peer reviewed, 2023) -
Demonstrating the load-shifting potential of a schedule-based control in a real-life educational building
Clauß, John; Brozovsky, Johannes Georg; Georges, Laurent Francis Ghislain (Journal article; Peer reviewed, 2024)This work investigates the potential of simplified control approaches to deploy the building energy flexibility (BEF), here for the shifting of the space-heating load in a real-life educational building. The educational ... -
Low-parameter linear model to activate the flexibility of the building thermal mass in energy system optimization
Askeland, Magnus; Georges, Laurent Francis Ghislain; Korpås, Magnus (Peer reviewed; Journal article, 2023)As buildings are becoming an integrated part of the energy system, the potential activation of the thermal mass as a source of flexibility needs to be considered in energy system modeling. Since energy system models represent ... -
Predicting the performance of hybrid ventilation in buildings using a multivariate attention-based biLSTM Encoder – Decoder
Chaudhary, Gaurav; Johra, Hicham; Georges, Laurent Francis Ghislain; Austbø, Bjørn (Journal article; Peer reviewed, 2023)Hybrid ventilation is an energy-efficient solution to provide fresh air for most climates, given that it has a reliable control system. To operate such systems optimally, a high-fidelity control-oriented modesl is required. ... -
pymodconn: A python package for developing modular sequence-to-sequence control-oriented deep neural networks
Chaudhary, Gaurav; Johra, Hicham; Georges, Laurent Francis Ghislain; Austbø, Bjørn (Peer reviewed; Journal article, 2023)This paper introduces "pymodconn", a comprehensive python package developed for constructing modular sequence-to-sequence control-oriented deep neural networks. These deep neural networks (DNNs) are designed to predict the ... -
Sub-hourly measurement datasets from 6 real buildings: Energy use and indoor climate
Sartori, Igor; Walnum, Harald Taxt; Skeie, Kristian; Georges, Laurent Francis Ghislain; Knudsen, Michael D.; Bacher, Peder; Candanedo, José; Sigounis, Anna-Maria; Prakash, Anand Krishnan; Pritoni, Marco; Granderson, Jessica; Yang, Shiyu; Wan, Man Pun (Journal article; Peer reviewed, 2023) -
Synconn_build: A python based synthetic dataset generator for testing and validating control-oriented neural networks for building dynamics prediction
Chaudhary, Gaurav; Johra, Hicham; Georges, Laurent Francis Ghislain; Austbø, Bjørn (Peer reviewed; Journal article, 2023)Applying model-based predictive control in buildings requires a control-oriented model capable of learning how various control actions influence building dynamics, such as indoor air temperature and energy use. However, ...