Abstract
| Language | English |
|---|---|
| Title of host publication | IEEE PES Innovative Smart Grid Technologies Europe |
| Publisher | IEEE Press |
| Number of pages | 5 |
| State | Accepted/In press - 2019 |
| Event | 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe - University POLITEHNICA, Bucharest, Romania, Bucharest, Romania Duration: 29 Sep 2019 → 2 Oct 2019 Conference number: 19 http://sites.ieee.org/isgt-europe-2019/ |
Conference
| Conference | 2019 IEEE PES Innovative Smart Grid Technologies Europe, ISGT Europe |
|---|---|
| Country | Romania |
| City | Bucharest |
| Period | 29/09/19 → 2/10/19 |
| Internet address |
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Collaborative learning for classification and prediction of building energy flexibility. / Kumar, Anil; Mocanu, Elena; Babar, Muhammad; Nguyen, Phuong.
IEEE PES Innovative Smart Grid Technologies Europe. IEEE Press, 2019.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Collaborative learning for classification and prediction of building energy flexibility
AU - Kumar,Anil
AU - Mocanu,Elena
AU - Babar,Muhammad
AU - Nguyen,Phuong
PY - 2019
Y1 - 2019
N2 - In this paper we propose an simple digital learning platform for flexible energy detection using data with fine granularity. The platform is empowered with artificially intelligent methods aiming to quantify the uncertainty of building energy consumption at building level, as well as at the aggregated level. Two major learning tasks are perform in this context: prediction and classification. Firstly, the building energy prediction with various time steps resolution are perform using methods such as Fully Connected Neural Networks (FCNN), Long short-term memory (LSTM), and Decision Trees (DT). Secondly, a Support Vector Machine (SVM) method is used to unlock the building energy flexibility by performing classification assuming three different levels of flexibility. Further on, a collaborative task is integrate within the platform to improve the multi-class classification accuracy. Through the end, we argue that this approach can be considered a solid integrated and automated basic block able to incorporate future AI models in (near) real-time to explore the benefits at the synergy between built environment and emerging smart grid technologies and applications.
AB - In this paper we propose an simple digital learning platform for flexible energy detection using data with fine granularity. The platform is empowered with artificially intelligent methods aiming to quantify the uncertainty of building energy consumption at building level, as well as at the aggregated level. Two major learning tasks are perform in this context: prediction and classification. Firstly, the building energy prediction with various time steps resolution are perform using methods such as Fully Connected Neural Networks (FCNN), Long short-term memory (LSTM), and Decision Trees (DT). Secondly, a Support Vector Machine (SVM) method is used to unlock the building energy flexibility by performing classification assuming three different levels of flexibility. Further on, a collaborative task is integrate within the platform to improve the multi-class classification accuracy. Through the end, we argue that this approach can be considered a solid integrated and automated basic block able to incorporate future AI models in (near) real-time to explore the benefits at the synergy between built environment and emerging smart grid technologies and applications.
M3 - Conference contribution
BT - IEEE PES Innovative Smart Grid Technologies Europe
PB - IEEE Press
ER -