no code implementations • 21 Nov 2024 • Edgar Mauricio Salazar Duque, Bart van der Holst, Pedro P. Vergara, Juan S. Giraldo, Phuong H. Nguyen, Anne van der Molen, Han, Slootweg
The lower dimensional projection of standardised load profiles unveils a latent distribution in a three-dimensional sphere.
no code implementations • 7 Mar 2024 • Edgar Mauricio Salazar Duque, Juan S. Giraldo, Pedro P. Vergara, Phuong H. Nguyen, Han, Slootweg
In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in distribution systems.
no code implementations • 2 Mar 2021 • Decebal Constantin Mocanu, Elena Mocanu, Tiago Pinto, Selima Curci, Phuong H. Nguyen, Madeleine Gibescu, Damien Ernst, Zita A. Vale
A fundamental task for artificial intelligence is learning.
no code implementations • 7 Feb 2021 • Minh-Quan Tran, Ahmed S. Zamzam, Phuong H. Nguyen
Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms.
no code implementations • 18 Jul 2017 • Elena Mocanu, Decebal Constantin Mocanu, Phuong H. Nguyen, Antonio Liotta, Michael E. Webber, Madeleine Gibescu, J. G. Slootweg
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure.
2 code implementations • 15 Jul 2017 • Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods.
no code implementations • 6 May 2016 • Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid.
no code implementations • 20 Apr 2016 • Decebal Constantin Mocanu, Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Antonio Liotta
Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.
no code implementations • 17 Jul 2015 • Subodh Paudel, Phuong H. Nguyen, Wil L. Kling, Mohamed Elmitri, Bruno Lacarrière, Olivier Le Corre
Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model.